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Matboli M, Diab GI, Saad M, Khaled A, Roushdy M, Ali M, ELsawi HA, Aboughaleb IH. Machine-Learning-Based Identification of Key Feature RNA-Signature Linked to Diagnosis of Hepatocellular Carcinoma. J Clin Exp Hepatol 2024; 14:101456. [PMID: 39055616 PMCID: PMC11268357 DOI: 10.1016/j.jceh.2024.101456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/09/2024] [Indexed: 07/27/2024] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC. Methods We have used five classifiers (KNN, RF, SVM, LGBM, and DNNs) to predict the liver disease (HCC). The classifiers were trained on 187 samples and then tested on 80 samples. The model included 22 features (age, sex, smoking, cirrhosis, non-cirrhosis, albumin, ALT, AST bilirubin (total and direct), INR, AFP, HBV Ag, HCV Abs, RQmiR-1298, RQmiR-1262, RQmiR-106b-3p, RQmRNARAB11A, and RQSTAT1, RQmRNAATG12, RQLnc-WRAP53, RQLncRNA- RP11-513I15.6). Results LGBM achieved the highest accuracy of 98.75% in predicting HCC among all models surpassing Random Forest (96.25%), DNN (91.25%), SVC (88.75%), and KNN (87.50%). Conclusion Our machine-learning model incorporating the expression data of RAB11A/STAT1/ATG12/miR-1262/miR-1298/miR-106b-3p/lncRNA-RP11-513I15.6/lncRNA-WRAP53 signature and clinical data represents a potential novel diagnostic model for HCC.
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Affiliation(s)
- Marwa Matboli
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Ain Shams University, Cairo 11566, Egypt
| | - Gouda I. Diab
- Biomedical Engineering Department, Egyptian Armed Forces, Cairo, Egypt
| | - Maha Saad
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Modern University for Technology and Information, Cairo, Egypt
| | - Abdelrahman Khaled
- Bioinformatics Group, Center of Informatics Sciences (CIS), School of Information Technology and Computer Sciences, Nile University, Giza, Egypt
| | - Marian Roushdy
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Ain Shams University, Cairo 11566, Egypt
| | - Marwa Ali
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Ain Shams University, Cairo 11566, Egypt
| | - Hind A. ELsawi
- Department of Internal Medicine, Badr University in Cairo, Badr City, Egypt
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Chan YT, Zhang C, Wu J, Lu P, Xu L, Yuan H, Feng Y, Chen ZS, Wang N. Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma. Mol Cancer 2024; 23:189. [PMID: 39242496 PMCID: PMC11378508 DOI: 10.1186/s12943-024-02101-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 08/23/2024] [Indexed: 09/09/2024] Open
Abstract
Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.
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Affiliation(s)
- Yau-Tuen Chan
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Cheng Zhang
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Junyu Wu
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Pengde Lu
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Lin Xu
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Hongchao Yuan
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Yibin Feng
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Zhe-Sheng Chen
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong.
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, 8000 Utopia Parkway, Queens, NY, 11439, USA.
| | - Ning Wang
- School of Chinese Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong.
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Feng W, Liang J, Xu B, Huang L, Xu Q, Chen D, Lai J, Chen J. Fatty acid metabolism affects hepatocellular carcinoma progression via the PPAR-γ signaling pathway and fatty acid β-oxidation. Int Immunopharmacol 2024; 141:112917. [PMID: 39137630 DOI: 10.1016/j.intimp.2024.112917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 07/07/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024]
Abstract
PURPOSE This study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism. METHODS RNA-seq and clinical data of HCC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatic analyses were employed to identify differentially expressed genes (DEGs) related to prognosis. A signature was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to classify HCC patients from the TCGA database into low-risk and high-risk groups. The predictive performance of the signature was evaluated through principal components analysis (PCA), Kaplan Meier (KM) survival analysis, receiver operating characteristics (ROC) curves, nomogram, genetic mutations, drug sensitivity analysis, immunological correlation analysis, and enrichment analysis. Single-cell maps were constructed to illustrate the distribution of core genes. Immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blot were employed to verify the expression of core genes. The function of one core gene was validated through a series of in vitro assays, including cell viability, colony formation, wound healing, trans-well migration, and invasion assays. The results were analyzed in the context of relevant signaling pathways. RESULTS Bioinformatic analyses identified 15 FAMGs that were related to prognosis. A 4-gene signature was constructed, and patients were divided into high- and low-risk groups according to the signature. The high-risk group exhibited a poorer prognosis compared to the low-risk group in both the training (P < 0.001) and validation (P = 0.020) sets. Furthermore, the risk score was identified as an independent predictor of OS (P < 0.001, HR = 8.005). The incorporation of the risk score and clinicopathologic features into a nomogram enabled the effective prediction of patient prognosis. The model was able to effectively predict the immune microenvironment, drug sensitivity to chemotherapy, and gene mutation for each group. Single-cell maps demonstrated that FAMGs in the model were distributed in tumor cells. Enrichment analyses revealed that the cell cycle, fatty acid β oxidation and PPAR signaling pathways were the most significant pathways. Among the four key prognostically related FAMGs, Spermine Synthase (SMS) was selected and validated as a potential oncogene affecting cell cycle, PPAR-γ signaling pathway and fatty acid β oxidation in HCC. CONCLUSIONS The risk characteristics based on FAMGs could serve as independent prognostic indicators for predicting HCC prognosis and could also serve as evaluation criteria for gene mutations, immunity, and chemotherapy drug therapy in HCC patients. Meanwhile, targeted fatty acid metabolism could be used to treat HCC through related signaling pathways.
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Affiliation(s)
- Wei Feng
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Jiahua Liang
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Borui Xu
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Li Huang
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Qiongcong Xu
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Dong Chen
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Jiaming Lai
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
| | - Jiancong Chen
- Department of Pancreato-Biliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
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Xin H, Li Y, Wang Q, Liu R, Zhang C, Zhang H, Su X, Bai B, Li N, Zhang M. A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method. Comput Biol Med 2024; 178:108663. [PMID: 38905890 DOI: 10.1016/j.compbiomed.2024.108663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/28/2024] [Accepted: 05/26/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine. MATERIAL AND METHODS We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH). RESULTS We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012). CONCLUSION The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
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Affiliation(s)
- Haibei Xin
- Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Yuanfeng Li
- Beijing Institute of Radiation Medicine, Beijing, PR China.
| | - Quanlei Wang
- Dongguan Institute of Gallbladder Disease Research, Dongguan Nancheng Hospital, Dongguan, PR China
| | - Ren Liu
- The 902nd Hospital of the PLA, Bengbu, PR China
| | - Cunzhen Zhang
- Department of Hepatic Surgery I (Ward I), The Third Affiliated Hospital of Naval Military Medical University, Shanghai, PR China
| | - Haidong Zhang
- Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Xian Su
- Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Bin Bai
- Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Nan Li
- Department of Hepatic Surgery I (Ward I), The Third Affiliated Hospital of Naval Military Medical University, Shanghai, PR China; The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China.
| | - Minfeng Zhang
- Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
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Li Y, Fan N, He X, Zhu J, Zhang J, Lu L. Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence. J Hepatocell Carcinoma 2024; 11:1429-1438. [PMID: 39050809 PMCID: PMC11268770 DOI: 10.2147/jhc.s474922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Hepatocellular Carcinoma (HCC) is a condition associated with significant morbidity and mortality. The presence of Portal Vein Tumour Thrombus (PVTT) typically signifies advanced disease stages and poor prognosis. Artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a promising tool for extracting quantitative data from medical images. AI is increasingly integrated into the imaging omics workflow and has become integral to various medical disciplines. This paper provides a comprehensive review of the mechanisms underlying the formation and progression of PVTT, as well as its impact on clinical management and prognosis. Additionally, it outlines the advancements in AI for predicting the diagnosis of HCC and the development of PVTT. The limitations of existing studies are critically evaluated, and potential future research directions in the realm of imaging for the diagnostic prediction of HCC and PVTT are discussed, with the ultimate goal of enhancing survival outcomes for PVTT patients.
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Affiliation(s)
- Yaduo Li
- Medical Imaging Department, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital), Zhuhai, People’s Republic of China
| | - Ningning Fan
- Medical Imaging Department, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital), Zhuhai, People’s Republic of China
| | - Xu He
- Department of Interventional Medicine, Guangzhou First People’s Hospital, Guangzhou, People’s Republic of China
| | - Jianjun Zhu
- R&D Department, Hanglok-Tech Co., Ltd., Hengqin, People’s Republic of China; Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People’s Republic of China
| | - Jie Zhang
- Medical Imaging Department, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital), Zhuhai, People’s Republic of China
| | - Ligong Lu
- Medical Imaging Department, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital), Zhuhai, People’s Republic of China
- Department of Interventional Medicine, Guangzhou First People’s Hospital, Guangzhou, People’s Republic of China
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Ding GY, Tan WM, Lin YP, Ling Y, Huang W, Zhang S, Shi JY, Luo RK, Ji Y, Wang XY, Zhou J, Fan J, Cai MY, Yan B, Gao Q. Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning. BMC Med 2024; 22:282. [PMID: 38972973 PMCID: PMC11229270 DOI: 10.1186/s12916-024-03482-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 06/13/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. METHODS We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. RESULTS The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. CONCLUSIONS We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.
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Affiliation(s)
- Guang-Yu Ding
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
| | - Wei-Min Tan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, No.2005, Song Hu Road, Shanghai, 200433, China
| | - You-Pei Lin
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
| | - Yu Ling
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, No.2005, Song Hu Road, Shanghai, 200433, China
| | - Wen Huang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Shu Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
| | - Jie-Yi Shi
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
| | - Rong-Kui Luo
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiao-Ying Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Mu-Yan Cai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No.651 Dongfeng Road East, Guangzhou, 510060, China.
| | - Bo Yan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, No.2005, Song Hu Road, Shanghai, 200433, China.
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, No.180, Feng Lin Road, Shanghai, 200032, China.
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200433, China.
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Yang C, Yang HC, Luo YG, Li FT, Cong TH, Li YJ, Ye F, Li X. Predicting Survival Using Whole-Liver MRI Radiomics in Patients with Hepatocellular Carcinoma After TACE Refractoriness. Cardiovasc Intervent Radiol 2024; 47:964-977. [PMID: 38750156 DOI: 10.1007/s00270-024-03730-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/07/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To develop a model based on whole-liver radiomics features of pre-treatment enhanced MRI for predicting the prognosis of hepatocellular carcinoma (HCC) patients undergoing continued transarterial chemoembolization (TACE) after TACE-resistance. MATERIALS AND METHODS Data from 111 TACE-resistant HCC patients between January 2014 and March 2018 were retrospectively collected. At a ratio of 7:3, patients were randomly assigned to developing and validation cohorts. The whole-liver were manually segmented, and the radiomics signature was extracted. The tumor and liver radiomics score (TLrad-score) was calculated. Models were trained by machine learning algorithms and their predictive efficacies were compared. RESULTS Tumor stage, tumor burden, body mass index, alpha-fetoprotein, and vascular invasion were revealed as independent risk factors for survival. The model trained by Random Forest algorithms based on tumor burden, whole-liver radiomics signature, and clinical features had the highest predictive efficacy, with c-index values of 0.85 and 0.80 and areas under the ROC curve of 0.96 and 0.83 in the developing cohort and validation cohort, respectively. In the high-rad-score group (TLrad-score > - 0.34), the median overall survival (mOS) was significantly shorter than in the low-rad-score group (17 m vs. 37 m, p < 0.001). A shorter mOS was observed in patients with high tumor burden compared to those with low tumor burden (14 m vs. 29 m, p = 0.007). CONCLUSION The combined radiomics model from whole-liver signatures may effectively predict survival for HCC patients continuing TACE after TACE refractoriness. The TLrad-score and tumor burden are potential prognostic markers for TACE therapy following TACE-resistance.
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Affiliation(s)
- Chao Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Hong-Cai Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yin-Gen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fu-Tian Li
- Huiying Medical Technology (Beijing) Co., Ltd, Beijing, 100192, China
| | - Tian-Hao Cong
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yu-Jie Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Feng Ye
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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8
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Han JW, Lee SK, Kwon JH, Nam SW, Yang H, Bae SH, Kim JH, Nam H, Kim CW, Lee HL, Kim HY, Lee SW, Lee A, Chang UI, Song DS, Kim SH, Song MJ, Sung PS, Choi JY, Yoon SK, Jang JW. A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma. Clin Cancer Res 2024; 30:2812-2821. [PMID: 38639918 DOI: 10.1158/1078-0432.ccr-23-3978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/29/2024] [Accepted: 04/16/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment. EXPERIMENTAL DESIGN We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C). RESULTS The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup. CONCLUSIONS ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.
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Affiliation(s)
- Ji W Han
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Soon K Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Jung H Kwon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Soon W Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Hyun Yang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Si H Bae
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ji H Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Heechul Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Chang W Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hae L Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Hee Y Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Sung W Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Ahlim Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - U I Chang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Do S Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Seok-Hwan Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Myeong J Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Pil S Sung
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jong Y Choi
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung K Yoon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jeong W Jang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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9
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Zhu Y, Lu F. Astragaloside IV inhibits cell viability and glycolysis of hepatocellular carcinoma by regulating KAT2A-mediated succinylation of PGAM1. BMC Cancer 2024; 24:682. [PMID: 38835015 DOI: 10.1186/s12885-024-12438-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Astragaloside IV (AS-IV) is one of the basic components of Astragali radix, that has been shown to have preventive effects against various diseases, including cancers. This study aimed to explore the role of AS-IV in hepatocellular carcinoma (HCC) and its underlying mechanism. METHODS The cell viability, glucose consumption, lactate production, and extracellular acidification rate (ECAR) in SNU-182 and Huh7 cell lines were detected by specific commercial kits. Western blot was performed to analyze the succinylation level in SNU-182 and Huh7 cell lines. The interaction between lysine acetyltransferase (KAT) 2 A and phosphoglycerate mutase 1 (PGAM1) was evaluated by co-immunoprecipitation and immunofluorescence assays. The role of KAT2A in vivo was explored using a xenografted tumor model. RESULTS The results indicated that AS-IV treatment downregulated the protein levels of succinylation and KAT2A in SNU-182 and Huh7 cell lines. The cell viability, glucose consumption, lactate production, ECAR, and succinylation levels were decreased in AS-IV-treated SNU-182 and Huh7 cell lines, and the results were reversed after KAT2A overexpression. KAT2A interacted with PGAM1 to promote the succinylation of PGAM1 at K161 site. KAT2A overexpression promoted the viability and glycolysis of SNU-182 and Huh7 cell lines, which were partly blocked following PGAM1 inhibition. In tumor-bearing mice, AS-IV suppressed tumor growth though inhibiting KAT2A-mediated succinylation of PGAM1. CONCLUSION AS-IV inhibited cell viability and glycolysis in HCC by regulating KAT2A-mediated succinylation of PGAM1, suggesting that AS-IV might be a potential and suitable therapeutic agent for treating HCC.
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Affiliation(s)
- Yuanzhang Zhu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Huangpu District, Shanghai, 200020, China
| | - Fei Lu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Huangpu District, Shanghai, 200020, China.
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10
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Guo Z, Zhao M, Liu Z, Zheng J, Gong Y, Huang L, Xue J, Zhou X, Li S. Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. PLoS Negl Trop Dis 2024; 18:e0012235. [PMID: 38870200 PMCID: PMC11207143 DOI: 10.1371/journal.pntd.0012235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 06/26/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.
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Affiliation(s)
- Zhaoyu Guo
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Miaomiao Zhao
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Zhenhua Liu
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Jinxin Zheng
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfeng Gong
- School of Public Health, Fudan University, Shanghai, China
| | - Lulu Huang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Jingbo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaonong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shizhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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11
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Liu HF, Wang M, Lu YJ, Wang Q, Lu Y, Xing F, Xing W. CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning. Acad Radiol 2024; 31:2346-2355. [PMID: 38057182 DOI: 10.1016/j.acra.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023]
Abstract
RATIONALE AND OBJECTIVES To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC). METHODS CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively. CONCLUSION A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.
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Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Min Wang
- Department of Anesthesiology, The Second People's Hospital of Changzhou, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China (M.W.)
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Fei Xing
- Department of Radiology, Nantong Third People's Hospital, Nantong, Jiangsu, China (F.X.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.).
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12
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Pugliese N, Polverini D, Lombardi R, Pennisi G, Ravaioli F, Armandi A, Buzzetti E, Dalbeni A, Liguori A, Mantovani A, Villani R, Gardini I, Hassan C, Valenti L, Miele L, Petta S, Sebastiani G, Aghemo A. Evaluation of ChatGPT as a Counselling Tool for Italian-Speaking MASLD Patients: Assessment of Accuracy, Completeness and Comprehensibility. J Pers Med 2024; 14:568. [PMID: 38929789 PMCID: PMC11204905 DOI: 10.3390/jpm14060568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based chatbots have shown promise in providing counseling to patients with metabolic dysfunction-associated steatotic liver disease (MASLD). While ChatGPT3.5 has demonstrated the ability to comprehensively answer MASLD-related questions in English, its accuracy remains suboptimal. Whether language influences these results is unclear. This study aims to assess ChatGPT's performance as a counseling tool for Italian MASLD patients. METHODS Thirteen Italian experts rated the accuracy, completeness and comprehensibility of ChatGPT3.5 in answering 15 MASLD-related questions in Italian using a six-point accuracy, three-point completeness and three-point comprehensibility Likert's scale. RESULTS Mean scores for accuracy, completeness and comprehensibility were 4.57 ± 0.42, 2.14 ± 0.31 and 2.91 ± 0.07, respectively. The physical activity domain achieved the highest mean scores for accuracy and completeness, whereas the specialist referral domain achieved the lowest. Overall, Fleiss's coefficient of concordance for accuracy, completeness and comprehensibility across all 15 questions was 0.016, 0.075 and -0.010, respectively. Age and academic role of the evaluators did not influence the scores. The results were not significantly different from our previous study focusing on English. CONCLUSION Language does not appear to affect ChatGPT's ability to provide comprehensible and complete counseling to MASLD patients, but accuracy remains suboptimal in certain domains.
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Affiliation(s)
- Nicola Pugliese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (N.P.); (D.P.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Davide Polverini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (N.P.); (D.P.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Rosa Lombardi
- Unit of Internal Medicine and Metabolic Disease, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, 20122 Milan, Italy;
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy;
| | - Grazia Pennisi
- Section of Gastroenterology and Hepatology, PROMISE, University of Palermo, 90127 Palermo, Italy; (G.P.); (S.P.)
| | - Federico Ravaioli
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy;
- Division of Internal Medicine, Hepatobiliary and Immunoallergic Diseases, IRCCS Azienda Ospedaliero Universitaria di Bologna, 40138 Bologna, Italy
| | - Angelo Armandi
- Division of Gastroenterology and Hepatology, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126 Turin, Italy;
- Metabolic Liver Disease Research Program, I. Department of Internal Medicine, University Medical Center of Mainz, 55131 Mainz, Germany
| | - Elena Buzzetti
- Internal Medicine and Centre for Hemochromatosis and Hereditary Liver Diseases, ERN-EuroBloodNet Center for Iron Disorders, Azienda Ospedaliero-Universitaria di Modena-Policlinico, 41125 Modena, Italy;
- Department of Medical and Surgical Sciences, Università degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy
| | - Andrea Dalbeni
- Division of General Medicine C, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37134 Verona, Italy;
- Liver Unit, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37134 Verona, Italy
| | - Antonio Liguori
- DiSMeC—Department of Scienze Mediche e Chirurgiche, Fondazione Policlinico Gemelli IRCCS, 00168 Rome, Italy; (A.L.); (L.M.)
| | - Alessandro Mantovani
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Piazzale Stefani, 37126 Verona, Italy;
| | - Rosanna Villani
- C.U.R.E. (University Center for Liver Disease Research and Treatment), Liver Unit, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Ivan Gardini
- EpaC Onlus, Italian Liver Patient Association, 10141 Turin, Italy;
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (N.P.); (D.P.); (C.H.)
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano, 20089 Milan, Italy
| | - Luca Valenti
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy;
- Precision Medicine Lab, Biological Resource Center, Department of Transfusion Medicine, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Luca Miele
- DiSMeC—Department of Scienze Mediche e Chirurgiche, Fondazione Policlinico Gemelli IRCCS, 00168 Rome, Italy; (A.L.); (L.M.)
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, 00168 Rome, Italy
| | - Salvatore Petta
- Section of Gastroenterology and Hepatology, PROMISE, University of Palermo, 90127 Palermo, Italy; (G.P.); (S.P.)
| | - Giada Sebastiani
- Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, QC H4A 3J1, Canada;
| | - Alessio Aghemo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (N.P.); (D.P.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
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13
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Jiang X, Zhou R, Jiang F, Yan Y, Zhang Z, Wang J. Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning. Front Oncol 2024; 14:1401496. [PMID: 38812780 PMCID: PMC11133637 DOI: 10.3389/fonc.2024.1401496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.
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Affiliation(s)
- Xin Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Ruilong Zhou
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengle Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Yanan Yan
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Zheting Zhang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Jianmin Wang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
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14
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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15
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Sridhar GR, Siva Prasad AV, Lakshmi G. Scope and caveats: Artificial intelligence in gastroenterology. Artif Intell Gastroenterol 2024; 5:91607. [DOI: 10.35712/aig.v5.i1.91607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/18/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.
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Affiliation(s)
| | - Atmakuri V Siva Prasad
- Department of Gastroenterology, Institute of Gastroenterology, Visakhapatnam 530003, India
| | - Gumpeny Lakshmi
- Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare & Medical Technology, Visakhapatnam 530048, India
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16
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Mai Y, Liao C, Wang S, Zhou X, Meng L, Chen C, Qin Y, Deng G. High glucose-induced NCAPD2 upregulation promotes malignant phenotypes and regulates EMT via the Wnt/β-catenin signaling pathway in HCC. Am J Cancer Res 2024; 14:1685-1711. [PMID: 38726276 PMCID: PMC11076239 DOI: 10.62347/hynz9211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Diabetes mellitus (DM) is recognized as a risk factor for hepatocellular carcinoma (HCC). High glucose levels have been implicated in inducing epithelial-mesenchymal transition (EMT), contributing to the progression of various cancers. However, the molecular crosstalk remains unclear. This study aimed to elucidate the molecular mechanisms linking DM to HCC. Initially, the expression of NCAPD2 in HCC cells and patients was measured. A series of functional in vitro assays to examine the effects of NCAPD2 on the malignant behaviors and EMT of HCC under high glucose conditions were then conducted. Furthermore, the impacts of NCAPD2 knockdown on HCC proliferation and the β-catenin pathway were investigated in vivo. In addition, bioinformatics methods were performed to analyze the mechanisms and pathways involving NCAPD2, as well as its association with immune infiltration and drug sensitivity. The findings indicated that NCAPD2 was overexpressed in HCC, particularly in patients with DM, and its aberrant upregulation was linked to poor prognosis. In vitro experiments demonstrated that high glucose upregulated NCAPD2 expression, enhancing proliferation, invasion, and EMT, while knockdown of NCAPD2 reversed these effects. In vivo studies suggested that NCAPD2 knockdown might suppress HCC growth via the β-catenin pathway. Functional enrichment analysis revealed that NCAPD2 was involved in cell cycle regulation and primarily interacted with NCAPG, SMC4, and NCAPH. Additionally, NCAPD2 was positively correlated with EMT and the Wnt/β-catenin pathway, whereas knockdown of NCAPD2 inhibited the Wnt/β-catenin pathway. Moreover, NCAPD2 expression was significantly associated with immune cell infiltration, immune checkpoints, and drugs sensitivity. In conclusion, our study identified NCAPD2 as a novel oncogene in HCC and as a potential therapeutic target for HCC patients with DM.
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Affiliation(s)
- Yuhua Mai
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Chuanjie Liao
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of EducationNanning 530021, Guangxi, China
| | - Shengyu Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Xin Zhou
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Liheng Meng
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Cuihong Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Yingfen Qin
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Ganlu Deng
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of EducationNanning 530021, Guangxi, China
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17
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Gonvers S, Martins-Filho SN, Hirayama A, Calderaro J, Phillips R, Uldry E, Demartines N, Melloul E, Park YN, Paradis V, Thung SN, Alves V, Sempoux C, Labgaa I. Macroscopic Characterization of Hepatocellular Carcinoma: An Underexploited Source of Prognostic Factors. J Hepatocell Carcinoma 2024; 11:707-719. [PMID: 38605975 PMCID: PMC11007400 DOI: 10.2147/jhc.s447848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/29/2024] [Indexed: 04/13/2024] Open
Abstract
The macroscopic appearance of a tumor such as hepatocellular carcinoma (HCC) may be defined as its phenotype which is de facto dictated by its genotype. Therefore, macroscopic characteristics of HCC are unlikely random but rather reflect genomic traits of cancer, presumably acting as a valuable source of information that can be retrieved and exploited to infer prognosis. This review aims to provide a comprehensive overview of the available data on the prognostic value of macroscopic characterization in HCC. A total of 57 studies meeting eligible criteria were identified, including patients undergoing liver resection (LR; 47 studies, 83%) or liver transplant (LT; 9 studies, 16%). The following macroscopic variables were investigated: tumor size (n = 42 studies), number of nodules (n = 28), vascular invasion (n = 24), bile duct invasion (n = 6), growth pattern (n = 15), resection margin (n = 11), tumor location (n = 6), capsule (n = 2) and satellite (n = 1). Although the selected studies provided insightful data with notable prognostic performances, a lack of standardization and substantial gaps were noted in the report and the analysis of gross findings. This topic remains incompletely covered. While the available studies underscored the value of macroscopic variables in HCC prognostication, important lacks were also observed. Macroscopic characterization of HCC is likely an underexploited source of prognostic factors that must be actively explored by future multidisciplinary research.
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Affiliation(s)
- Stéphanie Gonvers
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | | | - André Hirayama
- Department of Pathology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Julien Calderaro
- Department of Pathology, APHP, Henri Mondor University Hospital, Creteil, Val-de-Marne, France
| | - Rebecca Phillips
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Emilie Uldry
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Nicolas Demartines
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Emmanuel Melloul
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Young Nyun Park
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Valérie Paradis
- Department of Pathology, APHP, Beaujon University Hospital, Clichy, France
| | - Swan N Thung
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Venancio Alves
- Department of Pathology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Christine Sempoux
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ismail Labgaa
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
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18
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Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
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19
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Shen Y, Huang J, Jia L, Zhang C, Xu J. Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma. Biochem Biophys Rep 2024; 37:101587. [PMID: 38107663 PMCID: PMC10724547 DOI: 10.1016/j.bbrep.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
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Affiliation(s)
- Ye Shen
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
| | - Juanjie Huang
- Department of General Surgery, Dongguan Qingxi Hospital, Dongguan, 523660, China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, ShenZhen, 518060, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China
| | - Jianxing Xu
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
- Department of Radiology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, 213002, China
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20
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Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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21
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Oh JH, Sinn DH. Multidisciplinary approach for hepatocellular carcinoma patients: current evidence and future perspectives. JOURNAL OF LIVER CANCER 2024; 24:47-56. [PMID: 38527905 PMCID: PMC10990674 DOI: 10.17998/jlc.2024.02.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/27/2024]
Abstract
Management of hepatocellular carcinoma (HCC) is challenging due to the complex relationship between underlying liver disease, tumor burden, and liver function. HCC is also notorious for its high recurrence rate even after curative treatment for early-stage tumor. Liver transplantation can substantially alter patient prognosis, but donor availability varies by each patient which further complicates treatment decision. Recent advancements in HCC treatments have introduced numerous potentially efficacious treatment modalities. However, high level evidence comparing the risks and benefits of these options is limited. In this complex situation, multidisciplinary approach or multidisciplinary team care has been suggested as a valuable strategy to help cope with escalating complexity in HCC management. Multidisciplinary approach involves collaboration among medical and health care professionals from various academic disciplines to provide comprehensive care. Although evidence suggests that multidisciplinary care can enhance outcomes of HCC patients, robust data from randomized controlled trials are currently lacking. Moreover, the implementation of a multidisciplinary approach necessitates increased medical resources compared to conventional cancer care. This review summarizes the current evidence on the role of multidisciplinary approach in HCC management and explores potential future directions.
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Affiliation(s)
- Joo Hyun Oh
- Department of Medicine, Nowon Eulji Medical Center, Eulji University, Eulji University School of Medicine, Seoul, Korea
| | - Dong Hyun Sinn
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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22
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Wang F, Yan CY, Qin Y, Wang ZM, Liu D, He Y, Yang M, Wen L, Zhang D. Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study. J Hepatocell Carcinoma 2024; 11:427-442. [PMID: 38440051 PMCID: PMC10911084 DOI: 10.2147/jhc.s449737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
Background Currently, it is still confused whether preoperative aminotransferase-to-platelet ratio (APRI) and gamma-glutamyl transferase-to-platelet ratio (GPR) can predict microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC). We aimed to develop and validate a machine-learning integration model for predicting MVI using APRI, GPR and gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI. Methods A total of 314 patients from XinQiao Hospital of Army Medical University were divided chronologically into training set (n = 220) and internal validation set (n = 94), and recurrence-free survival was determined to follow up after surgery. Seventy-three patients from Chongqing University Three Gorges Hospital and Luzhou People's Hospital served as external validation set. Overall, 387 patients with solitary HCC were analyzed as whole dataset set. Least absolute shrinkage and selection operator, tenfold cross-validation and multivariate logistic regression were used to gradually filter features. Six machine-learning models and an ensemble of the all models (ENS) were built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance. Results APRI, GPR, HBPratio3 ([liver SI‒tumor SI]/liver SI), PLT, peritumoral enhancement, non-smooth margin and peritumoral hypointensity were independent risk factors for MVI. Six machine-learning models showed good performance for predicting MVI in training set (AUCs range, 0.793-0.875), internal validation set (0.715-0.832), external validation set (0.636-0.746) and whole dataset set (0.756-0.850). The ENS achieved the highest AUCs (0.879 vs 0.858 vs 0.839 vs 0.851) in four cohorts with excellent calibration and more net benefit. Subgroup analysis indicated that ENS obtained excellent AUCs (0.900 vs 0.809 vs 0.865 vs 0.908) in HCC >5cm, ≤5cm, ≤3cm and ≤2cm cohorts. Kaplan‒Meier survival curves indicated that ENS achieved excellent stratification for MVI status. Conclusion The APRI and GPR may be new potential biomarkers for predicting MVI of HCC. The ENS achieved optimal performance for predicting MVI in different sizes HCC and may aid in the individualized selection of surgical procedures.
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Affiliation(s)
- Fei Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
- Department of Medical Imaging, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Chun Yue Yan
- Department of Emergency Medicine, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Yuan Qin
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404031, People’s Republic of China
| | - Zheng Ming Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Dan Liu
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Ying He
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Ming Yang
- Department of Medical Imaging, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Li Wen
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Dong Zhang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
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Ramírez-Mejía MM, Méndez-Sánchez N. From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring. World J Gastroenterol 2024; 30:631-635. [PMID: 38515945 PMCID: PMC10950631 DOI: 10.3748/wjg.v30.i7.631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.
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Affiliation(s)
- Mariana Michelle Ramírez-Mejía
- Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
| | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
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24
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Floreani A, Gabbia D, De Martin S. Current Perspectives on the Molecular and Clinical Relationships between Primary Biliary Cholangitis and Hepatocellular Carcinoma. Int J Mol Sci 2024; 25:2194. [PMID: 38396870 PMCID: PMC10888596 DOI: 10.3390/ijms25042194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Primary biliary cholangitis (PBC) is an autoimmune liver disease characterised by the immune-mediated destruction of small and medium intrahepatic bile ducts, with variable outcomes and progression. This review summarises the state of the art regarding the risk of neoplastic progression in PBC patients, with a particular focus on the molecular alterations present in PBC and in hepatocellular carcinoma (HCC), which is the most frequent liver cancer in these patients. Major risk factors are male gender, viral infections, e.g., HBV and HCV, non-response to UDCA, and high alcohol intake, as well as some metabolic-associated factors. Overall, HCC development is significantly more frequent in patients with advanced histological stages, being related to liver cirrhosis. It seems to be of fundamental importance to unravel eventual dysfunctional molecular pathways in PBC patients that may be used as biomarkers for HCC development. In the near future, this will possibly take advantage of artificial intelligence-designed algorithms.
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Affiliation(s)
- Annarosa Floreani
- University of Padova, 35122 Padova, Italy;
- Scientific Consultant IRCCS Negrar, 37024 Verona, Italy
| | - Daniela Gabbia
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy;
| | - Sara De Martin
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy;
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25
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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He J, Wu F, Li J, Deng Q, Chen J, Li P, Jiang X, Yang K, Xu S, Jiang Z, Li X, Jiang Z. Tumor suppressor CLCA1 inhibits angiogenesis via TGFB1/SMAD/VEGF cascade and sensitizes hepatocellular carcinoma cells to Sorafenib. Dig Liver Dis 2024; 56:176-186. [PMID: 37230858 DOI: 10.1016/j.dld.2023.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/18/2023] [Accepted: 05/05/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a highly vascularized tumor with a poor prognosis. Novel vascular-related therapeutic targets and prognostic markers remain urgently needed. AIMS To investigate the role and mechanism of CLCA1 in hepatocellular carcinoma. METHODS Immunofluorescence, Co-immunoprecipitation and rescue experiment were used to determine the specific mechanisms of CLCA1. Chemosensitivity assay was used to measure the impact of CLCA1 on Sorafenib. RESULTS CLCA1 was dramatically downregulated in hepatocellular carcinoma cell lines and tissues. Ectopic expression of CLCA1 induced cell apoptosis and G0/G1 phase arrest while suppressed cell growth, inhibited migration and invasion, reversal of epithelial mesenchymal transition in vitro and reduced xenograft tumor growth in vivo. Mechanistically, CLCA1 could co-localize and interact with TGFB1, thereby suppressing HCC angiogenesis through the TGFB1/SMAD/VEGF signaling cascade in vitro and in vivo. Moreover, CLCA1 also enhanced the sensitivity of HCC cells to the first-line targeted therapy, Sorafenib. CONCLUSION CLCA1 sensitizes HCC cells to Sorafenib and suppresses hepatocellular carcinoma angiogenesis through downregulating TGFB1 signaling cascade. This newly identified CLCA1 signaling pathway may help guide the anti-angiogenesis therapies for hepatocellular carcinoma. We also support the possibility of CLCA1 being a prognostic biomarker for hepatocellular carcinoma.
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Affiliation(s)
- Jin He
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Fan Wu
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Junfeng Li
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Qianxi Deng
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jun Chen
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Pengtao Li
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing 100044, China
| | - Xianyao Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Haikou 570100, China
| | - Kun Yang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Shuman Xu
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zhongxiang Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaoqing Li
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zheng Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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Wiest IC, Gilbert S, Kather JN. From research to reality: The role of artificial intelligence applications in HCC care. Clin Liver Dis (Hoboken) 2024; 23:e0136. [PMID: 38567094 PMCID: PMC10986906 DOI: 10.1097/cld.0000000000000136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Isabella C. Wiest
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephen Gilbert
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
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28
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Lu MY, Huang CF, Hung CH, Tai C, Mo LR, Kuo HT, Tseng KC, Lo CC, Bair MJ, Wang SJ, Huang JF, Yeh ML, Chen CT, Tsai MC, Huang CW, Lee PL, Yang TH, Huang YH, Chong LW, Chen CL, Yang CC, Yang S, Cheng PN, Hsieh TY, Hu JT, Wu WC, Cheng CY, Chen GY, Zhou GX, Tsai WL, Kao CN, Lin CL, Wang CC, Lin TY, Lin C, Su WW, Lee TH, Chang TS, Liu CJ, Dai CY, Kao JH, Lin HC, Chuang WL, Peng CY, Tsai CW, Chen CY, Yu ML. Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program. Clin Mol Hepatol 2024; 30:64-79. [PMID: 38195113 PMCID: PMC10776298 DOI: 10.3350/cmh.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/02/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND/AIMS Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
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Affiliation(s)
- Ming-Ying Lu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chung-Feng Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
| | - Chao-Hung Hung
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chi‐Ming Tai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Lein-Ray Mo
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
| | - Hsing-Tao Kuo
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
| | - Kuo-Chih Tseng
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
| | - Ching-Chu Lo
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
| | - Ming-Jong Bair
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
| | - Szu-Jen Wang
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
| | - Jee-Fu Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Lun Yeh
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Ting Chen
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Chang Tsai
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chien-Wei Huang
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
| | - Pei-Lun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | | | - Yi-Hsiang Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Lee-Won Chong
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Chien-Lin Chen
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
| | - Chi-Chieh Yang
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Sheng‐Shun Yang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Pin-Nan Cheng
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tsai-Yuan Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jui-Ting Hu
- Liver Center, Cathay General Hospital, Taipei, Taiwan
| | - Wen-Chih Wu
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
| | - Chien-Yu Cheng
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Guei-Ying Chen
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
| | | | - Wei-Lun Tsai
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chien-Neng Kao
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
| | - Chih-Lang Lin
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
| | - Chia-Chi Wang
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
| | - Ta-Ya Lin
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
| | - Chih‐Lin Lin
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
| | - Wei-Wen Su
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
| | - Tzong-Hsi Lee
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Te-Sheng Chang
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chun-Jen Liu
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chia-Yen Dai
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jia-Horng Kao
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Chieh Lin
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Wan-Long Chuang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Cheng-Yuan Peng
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Chun-Wei- Tsai
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Chi-Yi Chen
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
| | - Ming-Lung Yu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - TACR Study Group
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
- Lotung Poh-Ai Hospital, Yilan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Liver Center, Cathay General Hospital, Taipei, Taiwan
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
- Zhou Guoxiong Clinic, Penghu, Taiwan
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
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Wang Z, Shi P, Huang P, Xu C, He Y, Lei W. Identification of secretory pathway-related genes based on Random Forest algorithm to predict the prognosis and immunotherapy response of hepatocellular carcinoma. J Gene Med 2024; 26:e3593. [PMID: 37730948 DOI: 10.1002/jgm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/31/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND The dysfunction of secretory pathways may represent biomarkers or therapeutic targets of cancer. The hepatocellular carcinoma (HCC) phenotype was studied in relation to the genes in the secretory pathway and to screen for a combination of genes that may be a viable therapeutic target for HCC and connected to the pathophysiological features of the tumor. METHODS Using the HCC information from The Cancer Genome Atlas, somatic mutation and prognostic association analysis were performed on the secretory pathway genes. Based on prognostic genes in the secretory pathway, the samples were consensus clustered, and a Random Forest model was built. The clinical characteristics, tumor mutation burden, functional status and potential responses to immunotherapy and tumor suppressor medications of various subtypes and risk groups were discussed. RESULTS Of the 84 genes for secretory pathway, 32 were prognostic genes related to HCC, which divided HCC into two categories: C1 and C2. By comparing the two types of HCC samples, it was found that the survival outcome of C1 was inferior, with stronger adaptive and innate immunity, but less sensitive to immunotherapy than C2. The constructed prognostic signature included seven of the 32 prognostic genes in the secretory pathway, which showed significant correlation with the prognosis, somatic mutation, biological pathway status, potential response to immunotherapy and sensitivity of 72 tumor suppressor drugs from different HCC cohorts, and had a feasible prognostic effect for 31 types of cancer and immunotherapy cohorts. CONCLUSIONS In this study, HCC was divided into two molecular subtypes according to prognostic genes in the secretory pathway, and seven of them were combined into one signature, which produced significant results in evaluating the prognosis of different HCC cohorts, pan-cancer cohorts and immunotherapy cohorts, and had potential guiding significance for prophylactic immunotherapy in patients with HCC.
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Affiliation(s)
- Zanzhi Wang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Pengwei Shi
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Huang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Xu
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yaoquan He
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenxiong Lei
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
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30
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Zhang CS, Zeng ZM, Zhuo MY, Luo JR, Zhuang XH, Xu JN, Zeng J, Ma J, Lin HF. Anlotinib Combined With Toripalimab as First-Line Therapy for Unresectable Hepatocellular Carcinoma: A Prospective, Multicenter, Phase II Study. Oncologist 2023; 28:e1239-e1247. [PMID: 37329569 PMCID: PMC10712726 DOI: 10.1093/oncolo/oyad169] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/01/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND For patients with unresectable hepatocellular carcinoma (HCC), the first-line therapeutic options are still relatively limited, and treatment outcomes remain poor. We aimed to assess the efficacy and safety of anlotinib combined with toripalimab as first-line therapy for unresectable HCC. METHODS In this single-arm, multicenter, phase II study (ALTER-H-003), patients with advanced HCC without previous systemic anticancer therapy were recruited. Eligible patients were given anlotinib (12 mg on days 1-14) combined with toripalimab (240 mg on day 1) in a 3-week cycle. The primary endpoint was the objective response rate (ORR) by immune-related Response Evaluation Criteria in Solid Tumours (irRECIST)/RECIST v1.1 and modified RECIST (mRECIST). Secondary endpoints included disease control rate (DCR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety. RESULTS Between January 2020 and Jul 2021, 31 eligible patients were treated and included in the full analysis set. At data cutoff (January 10, 2023), the ORR was 29.0% (95% CI: 12.1%-46.0%) by irRECIST/RECIST v1.1, and 32.3% (95% CI: 14.8%-49.7%) by mRECIST criteria, respectively. Confirmed DCR and median DoR by irRECIST/RECIST v1.1 and mRECIST criteria were 77.4 % (95% CI: 61.8%-93.0%) and not reached (range: 3.0-22.5+ months), respectively. Median PFS was 11.0 months (95% CI: 3.4-18.5 months) and median OS was 18.2 months (95% CI: 15.8-20.5 months). Of the 31 patients assessed for adverse events (AEs), the most common grade ≥ 3 treatment-related AEs were hand-foot syndrome (9.7%, 3/31), hypertension (9.7%, 3/31), arthralgia (9.7%, 3/31), abnormal liver function (6.5%, 2/31), and decreased neutrophil counts (6.5%, 2/31). CONCLUSIONS Anlotinib combined with toripalimab showed promising efficacy and manageable safety in Chinese patients with unresectable HCC in the first-line setting. This combination therapy may offer a potential new therapeutic approach for patients with unresectable HCC.
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Affiliation(s)
- Cheng-Sheng Zhang
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Zhi-Ming Zeng
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Man-Yun Zhuo
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jing-Ru Luo
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Xiao-Hong Zhuang
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jun-Nv Xu
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jie Zeng
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jie Ma
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hai-Feng Lin
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
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Su Y, Lu Y, An H, Liu J, Ye F, Shen J, Ni Z, Huang B, Lin J. MicroRNA-204-5p Inhibits Hepatocellular Carcinoma by Targeting the Regulator of G Protein Signaling 20. ACS Pharmacol Transl Sci 2023; 6:1817-1828. [PMID: 38093845 PMCID: PMC10714421 DOI: 10.1021/acsptsci.3c00114] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 03/14/2024]
Abstract
Although the oncogenic roles of regulator of G protein signaling 20 (RGS20) and its upstream microRNAs (miRNAs) have been reported, their involvement in hepatocellular carcinoma (HCC) remains unexplored. We utilized the starBase, miRDB, TargetScan, and mirDIP databases, along with a dual-luciferase reporter assay and cDNA chip analysis to identify miRNAs targeting RGS20. miR-204-5p was selected for further experiments to confirm its direct targeting and downregulation of the RGS20 expression. To study the miR-204-5p/RGS20 axis in HCC, RGS20 and miR-204-5p were increased in PLC/PRF/5/Hep3B cells, and the viability, hyperplasia, apoptosis, cell cycle, and invasion/migration of the cells were assessed. RGS20 exhibited optimism, while miR-204-5p exhibited pessimism in tumors. miR-204-5p directly targeted RGS20 and downregulated its expression, whereas high RGS20 expression indicated a poor prognosis. Transfection of miR-204-5p inhibited the hyperplasia, migration, and invasion of HCC cells, but promoted apoptosis and influenced the levels of cyclin-dependent kinase 2 (CDK2), cyclin E1, B-cell lymphoma-2 (Bcl-2), Bax, and cleaved caspase-3/8. These effects were reversed by overexpression of RGS20. We recognized miR-204-5p as an upstream regulator targeting RGS20, thereby inhibiting HCC progression by downregulating RGS20 expression. RGS20 may prove to be a potential target for HCC treatment, and miR-204-5p might seem like to be a potential miRNA in gene therapy.
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Affiliation(s)
- Yanqing Su
- Department
of Pharmacy, Xiamen Children’s Hospital, Xiamen, Fujian 361006, China
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Yao Lu
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Hebei
Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, Hebei 050011, China
| | - Honglin An
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Jinhong Liu
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Feimin Ye
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Jiayu Shen
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Zhuona Ni
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Bin Huang
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Jiumao Lin
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
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Zhao J, Luo Z, Fu R, Zhou J, Chen S, Wang J, Chen D, Xie X. Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma. Eur J Med Res 2023; 28:571. [PMID: 38057871 DOI: 10.1186/s40001-023-01535-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated. METHODS Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified. CONCLUSIONS High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.
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Affiliation(s)
- Jiajing Zhao
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Zeminshan Luo
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Ruizhi Fu
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Jinghong Zhou
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Shubiao Chen
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Jianjie Wang
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Dewang Chen
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Xiaojun Xie
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China.
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Carr BI, Rui F, Ince V, Yilmaz S, Zhao X, Feng Y, Li J. Comparison of patients with hepatitis B virus-associated hepatocellular carcinoma: Data from two hospitals from Turkey and China. PORTAL HYPERTENSION & CIRRHOSIS 2023; 2:165-170. [PMID: 38179146 PMCID: PMC10766430 DOI: 10.1002/poh2.60] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/25/2023] [Indexed: 01/06/2024]
Abstract
Aims There are many studies on the incidence of hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC), but very little is known about the HCC features in different populations. The study aimed to compare characteristics in two cohorts of patients with HBV-associated hepatocellular carcinoma, from Turkey and China. Methods Data on patients with HBV-associated HCC diagnosed by imaging or liver biopsy were retrospectively collected from Shandong Provincial Hospital (n = 578) and Inonu University Hospital (n = 359) between January 2002 and December 2020, and the liver function and HCC characteristics were compared. Continuous variables were compared using Student's t-test or Mann-Whitney U test and categorical variables were compared using the χ2 test or Fisher's exact test. Results The patients in the Turkish cohort had significantly worse Child-Pugh scores (Child-Pugh A: 38.3% vs. 87.9%; Child-Pugh B: 40.3% vs. 11.1%; Child-Pugh A: 24.1% vs. 1.0%; p < 0.001) and significantly higher levels of aspartate aminotransferase (66.5 vs. 36.0; p < 0.001), alanine aminotransferase (47.5 vs. 33.0; p < 0.001), total bilirubin (20.8 vs. 17.9; p < 0.001), and lower albumin levels (32.0 vs. 40.0; p < 0.001) than patients in Chinese cohort. The tumor characteristics showed the Barcelona Clinic Liver Cancer (BCLC) score (BCLC 1: 5.1% vs. 71.8%; BCLC 2: 48.7% vs. 24.4%; BCLC 3: 24.4% vs. 3.8%; BCLC 4: 21.8% vs. 0; all p < 0.001), maximum tumor diameter (5.0 vs. 3.5; p < 0.001), alpha-fetoprotein values (27.7 vs. 13.2; p < 0.001), and percentage of patients with portal vein tumor thrombus (33% vs. 6.1%; p < 0.001) were all significantly worse in the Turkish cohort compared with Chinese cohort. Conclusions HBV-associated HCC from the Turkish cohort had worse liver function and more aggressive clinical characteristics than patients from the Chinese cohort.
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Affiliation(s)
- Brian I. Carr
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Fajuan Rui
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Volkan Ince
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Sezai Yilmaz
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Xinya Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Yuemin Feng
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
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Gu HX, Huang XS, Xu JX, Zhu P, Xu JF, Fan SF. Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study. J Digit Imaging 2023; 36:2554-2566. [PMID: 37578576 PMCID: PMC10584802 DOI: 10.1007/s10278-023-00888-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023] Open
Abstract
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-blind method. The MRI features and key laboratory and clinical indicators were compared between the groups. The potentially valuable MRI features and key laboratory and clinical characteristics for predicting DPHCC were identified by univariate and multivariate analyses, and the odds ratios (ORs) were recorded. In multivariate analysis, tumour without capsule (P = 0.046, OR = 9.777), dynamic persistent enhancement (P = 0.006, OR = 46.941), and targetoid appearance on diffusion-weighted imaging (DWI) (P = 0.021, OR = 30.566) were independently significant factors in the detection of DPHCC compared to NCPHCC. Serum alpha-fetoprotein (AFP) > 20 µg/L (P = 0.036, OR = 67.097) and prevalence of hepatitis B virus (HBV) infection (P = 0.020, OR = 153.633) were independent significant factors in predicting DPHCC compared to ICC. The differences in other tumour marker levels and imaging features between the groups were not significant. In MR enhanced and diffusion imaging, tumour without capsule, persistent enhancement and DWI targetoid findings, combined with AFP > 20 µg/L and HBV infection-positive laboratory results, can help to diagnose DPHCC and differentiate it from NCPHCC and ICC. These results suggest that clinical, laboratory and MRI features should be integrated to construct an AI diagnostic model for DPHCC.
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Affiliation(s)
- Hong-Xian Gu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
- Department of Radiology, the People's Hospital of Jianyang City, Chengdu, 641499, China
| | - Xiao-Shan Huang
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Xia Xu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Ping Zhu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Feng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital, Hangzhou, 310000, China.
| | - Shu-Feng Fan
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China.
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Suo L, Gao M, Ma T, Gao Z. Effect of RPL27 knockdown on the proliferation and apoptosis of human liver cancer cells. Biochem Biophys Res Commun 2023; 682:156-162. [PMID: 37812860 DOI: 10.1016/j.bbrc.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 10/11/2023]
Abstract
RPL27 is linked to the development of various diseases including malignant tumors. RPL27 may play an oncogenic function in hepatocellular carcinoma (HCC), but this is unknown. So, the aim of this study was to investigate how the human liver cancer cell lines SNU449 and HepG2 responded to RPL27 knockdown in terms of proliferation and apoptosis. SNU449 and HepG2 were cultured and infected with shCon and shRPL27 lentiviral particles to induce RPL27 knockdown, and then RPL27 expression was detected using qPCR and Western blot. Cell proliferation was measured using CCK8, cell cloning, cell scraping, and transwell migration and invasion, while apoptosis was measured using flow cytometry (FCM). The qPCR revealed that mRNA expression of RPL27 decreased after knocking down RPL27 in cells. The CCK8 and cell cloning assay confirmed that knocking down RPL27 significantly reduced cell viability. The cell scratch assay and transwell assays showed that the proliferation rate decreased after knocking down RPL27. A substantial increase in apoptotic cells was discovered by FCM. According to WB, RPL27 knockdown increased the expression of Bax and Caspase-3 while decreasing the expression of bcl-2. The findings showed that RPL27 knockdown inhibited cell proliferation in SNU449 and HepG2 via inducing apoptosis, proving that RPL27 is a novel gene linked with HCC and is crucial for both proliferation and apoptosis. These outcomes imply that RPL27 may be a potential target for liver cancer diagnosis and therapy.
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Affiliation(s)
- Lida Suo
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Mingwei Gao
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Taiheng Ma
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Zhenming Gao
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
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Ding, D, Wang, D, Qin Y. Development and validation of multi-omic prognostic signature of anoikis-related genes in liver hepatocellular carcinoma. Medicine (Baltimore) 2023; 102:e36190. [PMID: 37986299 PMCID: PMC10659623 DOI: 10.1097/md.0000000000036190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/22/2023] Open
Abstract
Liver hepatocellular carcinoma (LIHC) is characterized by high morbidity, rapid progression and early metastasis. Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. Inability to initiate anoikis process is closely associated with cancer proliferation and metastasis, affecting patients' prognosis. In this study, a corresponding gene signature was constructed to comprehensively assess the prognostic value of anoikis-related genes (ARGs) in LIHC. Using TCGA-LIHC dataset, the mRNA levels of the differentially expressed ARGs in LIHC and normal tissues were compared by Student t test. And prognostic ARGs were identified through Cox regression analysis. Prognostic signature was established and then externally verified by ICGC-LIRI-JP dataset and GES14520 dataset via LASSO Cox regression model. Potential functions and mechanisms of ARGs in LIHC were evaluated by functional enrichment analyses. And the immune infiltration status in prognostic signature was analyzed by ESTIMATE algorithm and ssGSEA algorithm. Furthermore, ARGs expression in LIHC tissues was validated via qRT-PCR and IHC staining from the HPA website. A total of 97 differentially expressed ARGs were detected in LIHC tissues. Functional enrichment analysis revealed these genes were mainly involved in MAP kinase activity, apoptotic signaling pathway, anoikis and PI3K-Akt signaling pathway. Afterward, the prognostic signature consisting of BSG, ETV4, EZH2, NQO1, PLK1, PBK, and SPP1 had a moderate to high predictive accuracy and served as an independent prognostic indicator for LIHC. The prognostic signature was also applicable to patients with distinct clinical parameters in subgroup survival analysis. And it could reflect the specific immune microenvironment in LIHC, which indicated high-risk group tended to profit from ICI treatment. Moreover, qRT-PCR and IHC staining showed increasing expression of BSG, ETV4, EZH2, NQO1, PLK1, PBK and SPP1in LIHC tissues, which were consistent to the results from TCGA database. The current study developed a novel prognostic signature comprising of 7 ARGs, which could stratify the risk and effectively predict the prognosis of LIHC patients. Furthermore, it also offered a potential indicator for immunotherapy of LIHC.
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Affiliation(s)
- Dongxiao Ding,
- Department of Thoracic Surgery, The People’s Hospital of Beilun District, Ningbo, Zhejiang, China
| | - Dianqian Wang,
- Health Science Center, Ningbo University, Zhejiang, China
| | - Yunsheng Qin
- Department of Hepatological Surgery, First Affiliated Hospital of Medical School of Zhejiang University, Hangzhou, Zhejiang, China
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Hsieh C, Laguna A, Ikeda I, Maxwell AWP, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology 2023; 309:e222891. [PMID: 37934098 DOI: 10.1148/radiol.222891] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.
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Affiliation(s)
- Celina Hsieh
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Amanda Laguna
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Ian Ikeda
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Aaron W P Maxwell
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Julius Chapiro
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Gregory Nadolski
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Zhicheng Jiao
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Harrison X Bai
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
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Jia W, Shi W, Yao Q, Mao Z, Chen C, Fan AQ, Wang Y, Zhao Z, Li J, Song W. Identifying immune infiltration by deep learning to assess the prognosis of patients with hepatocellular carcinoma. J Cancer Res Clin Oncol 2023; 149:12621-12635. [PMID: 37450030 DOI: 10.1007/s00432-023-05097-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND The treatment situation for hepatocellular carcinoma remains critical. The use of deep learning algorithms to assess immune infiltration is a promising new diagnostic tool. METHODS Patient data and whole slide images (WSIs) were obtained for the Xijing Hospital (XJH) cohort and TCGA cohort. We wrote programs using Visual studio 2022 with C# language to segment the WSI into tiles. Pathologists classified the tiles and later trained deep learning models using the ResNet 101V2 network via ML.NET with the TensorFlow framework. Model performance was evaluated using AccuracyMicro versus AccuracyMacro. Model performance was examined using ROC curves versus PR curves. The percentage of immune infiltration was calculated using the R package survminer to calculate the intergroup cutoff, and the Kaplan‒Meier method was used to plot the overall survival curve of patients. Cox regression was used to determine whether the percentage of immune infiltration was an independent risk factor for prognosis. A nomogram was constructed, and its accuracy was verified using time-dependent ROC curves with calibration curves. The CIBERSORT algorithm was used to assess immune infiltration between groups. Gene Ontology was used to explore the pathways of differentially expressed genes. RESULTS There were 100 WSIs and 165,293 tiles in the training set. The final deep learning models had an AccuracyMicro of 97.46% and an AccuracyMacro of 82.28%. The AUCs of the ROC curves on both the training and validation sets exceeded 0.95. The areas under the classification PR curves exceeded 0.85, except that of the TLS on the validation set, which might have had poor results (0.713) due to too few samples. There was a significant difference in OS between the TIL classification groups (p < 0.001), while there was no significant difference in OS between the TLS groups (p = 0.294). Cox regression showed that TIL percentage was an independent risk factor for prognosis in HCC patients (p = 0.015). The AUCs according to the nomogram were 0.714, 0.690, and 0.676 for the 1-year, 2-year, and 5-year AUCs in the TCGA cohort and 0.756, 0.797, and 0.883 in the XJH cohort, respectively. There were significant differences in the levels of infiltration of seven immune cell types between the two groups of samples, and gene ontology showed that the differentially expressed genes between the groups were immune related. Their expression levels of PD-1 and CTLA4 were also significantly different. CONCLUSION We constructed and tested a deep learning model that evaluates the immune infiltration of liver cancer tissue in HCC patients. Our findings demonstrate the value of the model in assessing patient prognosis, immune infiltration and immune checkpoint expression levels.
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Affiliation(s)
- Weili Jia
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wen Shi
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | | | - Zhenzhen Mao
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Chao Chen
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - AQiang Fan
- Xi'an Medical University, Xi'an, China
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yanfang Wang
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zihao Zhao
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jipeng Li
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Wenjie Song
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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Hu M, Xia X, Chen L, Jin Y, Hu Z, Xia S, Yao X. Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma. Ann Hepatol 2023; 28:101137. [PMID: 37451515 DOI: 10.1016/j.aohep.2023.101137] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/17/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.
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Affiliation(s)
- Miner Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Xiaojun Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Lichao Chen
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yunpeng Jin
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Zhenhua Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
| | - Shudong Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Xudong Yao
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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Guo Q, Huang Y, Zhan X. Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes. Med Princ Pract 2023; 32:332-342. [PMID: 37848003 PMCID: PMC10727522 DOI: 10.1159/000534537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/09/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC). MATERIALS AND METHODS We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years. RESULTS We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets. CONCLUSION This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.
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Affiliation(s)
- Qiusheng Guo
- Department of Medical Oncology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China,
| | - Yangyang Huang
- Pharmacy Department, Zhejiang Jinhua Guangfu Tumor Hospital, Jinhua, China
| | - Xiaoan Zhan
- Department of Oncology Surgery, Zhejiang Jinhua Guangfu Tumor Hospital, Jinhua, China
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Chen Z, Li X, Zhang Y, Yang Y, Zhang Y, Zhou D, Yang Y, Zhang S, Liu Y. MRI Features for Predicting Microvascular Invasion and Postoperative Recurrence in Hepatocellular Carcinoma Without Peritumoral Hypointensity. J Hepatocell Carcinoma 2023; 10:1595-1608. [PMID: 37786565 PMCID: PMC10541533 DOI: 10.2147/jhc.s422632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Purpose To identify MRI features of hepatocellular carcinoma (HCC) that predict microvascular invasion (MVI) and postoperative intrahepatic recurrence in patients without peritumoral hepatobiliary phase (HBP) hypointensity. Patients and Methods One hundred and thirty patients with HCC who underwent preoperative gadoxetate-enhanced MRI and curative hepatic resection were retrospectively reviewed. Two radiologists reviewed all preoperative MR images and assessed the radiological features of HCCs. The ability of peritumoral HBP hypointensity to identify MVI and intrahepatic recurrence was analyzed. We then assessed the MRI features of HCC that predicted the MVI and intrahepatic recurrence-free survival (RFS) in the subgroup without peritumoral HBP hypointensity. Finally, a two-step flowchart was constructed to assist in clinical decision-making. Results Peritumoral HBP hypointensity (odds ratio, 3.019; 95% confidence interval: 1.071-8.512; P=0.037) was an independent predictor of MVI. The sensitivity, specificity, positive predictive value, negative predictive value, and AUROC of peritumoral HBP hypointensity in predicting MVI were 23.80%, 91.04%, 71.23%, 55.96%, and 0.574, respectively. Intrahepatic RFS was significantly shorter in patients with peritumoral HBP hypointensity (P<0.001). In patients without peritumoral HBP hypointensity, the only significant difference between MVI-positive and MVI-negative HCCs was the presence of a radiological capsule (P=0.038). Satellite nodule was an independent risk factor for intrahepatic RFS (hazard ratio,3.324; 95% CI: 1.733-6.378; P<0.001). The high-risk HCC detection rate was significantly higher when using the two-step flowchart that incorporated peritumoral HBP hypointensity and satellite nodule than when using peritumoral HBP hypointensity alone (P<0.001). Conclusion In patients without peritumoral HBP hypointensity, a radiological capsule is useful for identifying MVI and satellite nodule is an independent risk factor for intrahepatic RFS.
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Affiliation(s)
- Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Xiaohuan Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yu Zhang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yiming Yang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yan Zhang
- Integrated Department, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Dongjing Zhou
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yu Yang
- Department of Pathology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Shuping Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
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Chen Y, Yang C, Sheng L, Jiang H, Song B. The Era of Immunotherapy in Hepatocellular Carcinoma: The New Mission and Challenges of Magnetic Resonance Imaging. Cancers (Basel) 2023; 15:4677. [PMID: 37835371 PMCID: PMC10572030 DOI: 10.3390/cancers15194677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 10/15/2023] Open
Abstract
In recent years, significant advancements in immunotherapy for hepatocellular carcinoma (HCC) have shown the potential to further improve the prognosis of patients with advanced HCC. However, in clinical practice, there is still a lack of effective biomarkers for identifying the patient who would benefit from immunotherapy and predicting the tumor response to immunotherapy. The immune microenvironment of HCC plays a crucial role in tumor development and drug responses. However, due to the complexity of immune microenvironment, currently, no single pathological or molecular biomarker can effectively predict tumor responses to immunotherapy. Magnetic resonance imaging (MRI) images provide rich biological information; existing studies suggest the feasibility of using MRI to assess the immune microenvironment of HCC and predict tumor responses to immunotherapy. Nevertheless, there are limitations, such as the suboptimal performance of conventional MRI sequences, incomplete feature extraction in previous deep learning methods, and limited interpretability. Further study needs to combine qualitative features, quantitative parameters, multi-omics characteristics related to the HCC immune microenvironment, and various deep learning techniques in multi-center research cohorts. Subsequently, efforts should also be undertaken to construct and validate a visual predictive tool of tumor response, and assess its predictive value for patient survival benefits. Additionally, future research endeavors must aim to provide an accurate, efficient, non-invasive, and highly interpretable method for predicting the effectiveness of immune therapy.
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Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Choi JH, Thung SN. Advances in Histological and Molecular Classification of Hepatocellular Carcinoma. Biomedicines 2023; 11:2582. [PMID: 37761023 PMCID: PMC10526317 DOI: 10.3390/biomedicines11092582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a primary liver cancer characterized by hepatocellular differentiation. HCC is molecularly heterogeneous with a wide spectrum of histopathology. The prognosis of patients with HCC is generally poor, especially in those with advanced stages. HCC remains a diagnostic challenge for pathologists because of its morphological and phenotypic diversity. However, recent advances have enhanced our understanding of the molecular genetics and histological subtypes of HCC. Accurate diagnosis of HCC is important for patient management and prognosis. This review provides an update on HCC pathology, focusing on molecular genetics, histological subtypes, and diagnostic approaches.
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Affiliation(s)
- Joon Hyuk Choi
- Department of Pathology, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea
| | - Swan N. Thung
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA;
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He Z, Shen X, Wang B, Xu L, Ling Q. CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. PLoS One 2023; 18:e0290900. [PMID: 37695786 PMCID: PMC10495018 DOI: 10.1371/journal.pone.0290900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.
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Affiliation(s)
- Zenglei He
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Bin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Li Xu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Qi Ling
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
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Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
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Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
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Zhang Z, Mao M, Wang F, Zhang Y, Shi J, Chang L, Wu X, Zhang Z, Xu P, Lu S. Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma. Front Immunol 2023; 14:1164669. [PMID: 37545521 PMCID: PMC10399597 DOI: 10.3389/fimmu.2023.1164669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Background Despite encouraging results from immunotherapy combined with targeted therapy for hepatocellular carcinoma (HCC), the prognosis remains poor. Chemokines and their receptors are an essential component in the development of HCC, but their significance in HCC have not yet been fully elucidated. We aimed to establish chemokine-related prognostic signature and investigate the association between the genes and tumor immune microenvironment (TIME). Methods 342 HCC patients have screened from the TCGA cohort. A prognostic signature was developed using least absolute shrinkage and selection operator regression and Cox proportional risk regression analysis. External validation was performed using the LIHC-JP cohort deployed from the ICGC database. Single-cell RNA sequencing (scRNA-seq) data from the GEO database. Two nomograms were developed to estimate the outcome of HCC patients. RT-qPCR was used to validate the differences in the expression of genes contained in the signature. Results The prognostic signature containing two chemokines-(CCL14, CCL20) and one chemokine receptor-(CCR3) was successfully established. The HCC patients were stratified into high- and low-risk groups according to their median risk scores. We found that patients in the low-risk group had better outcomes than those in the high-risk group. The results of univariate and multivariate Cox regression analyses suggested that this prognostic signature could be considered an independent risk factor for the outcome of HCC patients. We discovered significant differences in the infiltration of various immune cell subtypes, tumor mutation burden, biological pathways, the expression of immune activation or suppression genes, and the sensitivity of different groups to chemotherapy agents and small molecule-targeted drugs in the high- and low-risk groups. Subsequently, single-cell analysis results showed that the higher expression of CCL20 was associated with HCC metastasis. The RT-qPCR results demonstrated remarkable discrepancies in the expression of CCL14, CCL20, and CCR3 between HCC and its paired adjacent non-tumor tissues. Conclusion In this study, a novel prognostic biomarker explored in depth the association between the prognostic model and TIME was developed and verified. These results may be applied in the future to improve the efficacy of immunotherapy or targeted therapy for HCC.
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Affiliation(s)
- Ze Zhang
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Mingsong Mao
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Fangzhou Wang
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Jihang Shi
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Lei Chang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Xiaolin Wu
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Ping Xu
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Shichun Lu
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
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Schattenberg JM, Chalasani N, Alkhouri N. Artificial Intelligence Applications in Hepatology. Clin Gastroenterol Hepatol 2023; 21:2015-2025. [PMID: 37088460 DOI: 10.1016/j.cgh.2023.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/16/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.
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Affiliation(s)
- Jörn M Schattenberg
- Metabolic Liver Research Program, I. Department of Medicine, University Medical Center Mainz, Mainz, Germany
| | - Naga Chalasani
- Indiana University School of Medicine and Indiana University Health, Indianapolis, Indiana
| | - Naim Alkhouri
- Arizona Liver Health and University of Arizona, Tucson, Arizona.
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Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:cancers15112928. [PMID: 37296890 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
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Affiliation(s)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Jonathan P Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
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Liang Y, Wang Z, Peng Y, Dai Z, Lai C, Qiu Y, Yao Y, Shi Y, Shang J, Huang X. Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization. Front Oncol 2023; 13:1169102. [PMID: 37305570 PMCID: PMC10254793 DOI: 10.3389/fonc.2023.1169102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Background Postoperative adjuvant transarterial chemoembolization (PA-TACE) has been increasing widely used to improve the prognosis of hepatocellular carcinoma (HCC) patients. However, clinical outcomes vary from patient to patient, which calls for individualized prognostic prediction and early management. Methods A total of 274 HCC patients who underwent PA-TACE were enrolled in this study. The prediction performance of five machine learning models was compared and the prognostic variables of postoperative outcomes were identified. Results Compared with other machine learning models, the risk prediction model based on ensemble learning strategies, including Boosting, Bagging, and Stacking algorithms, presented better prediction performance for overall mortality and HCC recurrence. Moreover, the results showed that the Stacking algorithm had relatively low time consumption, good discriminative ability, and the best prediction performance. In addition, according to time-dependent ROC analysis, the ensemble learning strategies were found to perform well in predicting both OS and RFS for the patients. Our study also found that BCLC Stage, hsCRP/ALB and frequency of PA-TACE were relatively important variables in both overall mortality and recurrence, while MVI contributed more to the recurrence of the patients. Conclusion Among the five machine learning models, the ensemble learning strategies, especially the Stacking algorithm, could better predict the prognosis of HCC patients following PA-TACE. Machine learning models could also help clinicians identify the important prognostic factors that are clinically useful in individualized patient monitoring and management.
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Affiliation(s)
- Yuxin Liang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Zirui Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yujiao Peng
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zonglin Dai
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyou Lai
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuqin Qiu
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yutong Yao
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Shi
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jin Shang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaolun Huang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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50
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Li J, Li Y, Li F, Xu L. NK cell marker gene-based model shows good predictive ability in prognosis and response to immunotherapies in hepatocellular carcinoma. Sci Rep 2023; 13:7294. [PMID: 37147523 PMCID: PMC10163253 DOI: 10.1038/s41598-023-34602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fourth leading cause of malignancy worldwide, and its progression is influenced by the immune microenvironment. Natural killer (NK) cells are essential in the anti-tumor response and have been linked to immunotherapies for cancers. Therefore, it is important to unify and validate the role of NK cell-related gene signatures in HCC. In this study, we used RNA-seq analysis on HCC samples from public databases. We applied the ConsensusClusterPlus tool to construct the consensus matrix and cluster the samples based on their NK cell-related expression profile data. We employed the least absolute shrinkage and selection operator regression analysis to identify the hub genes. Additionally, we utilized the CIBERSORT and ESTIMATE web-based methods to perform immune-related evaluations. Our results showed that the NK cell-related gene-based classification divided HCC patients into three clusters. The C3 cluster was activated in immune activation signaling pathways and showed better prognosis and good clinical features. In contrast, the C1 cluster was remarkably enriched in cell cycle pathways. The stromal score, immune score, and ESTIMATE score in C3 were much higher than those in C2 and C1. Furthermore, we identified six hub genes: CDC20, HMOX1, S100A9, CFHR3, PCN1, and GZMA. The NK cell-related genes-based risk score subgroups demonstrated that a higher risk score subgroup showed poorer prognosis. In summary, our findings suggest that NK cell-related genes play an essential role in HCC prognosis prediction and have therapeutic potential in promoting NK cell antitumor immunity. The six identified hub genes may serve as useful biomarkers for novel therapeutic targets.
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Affiliation(s)
- Juan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China.
| | - Yi Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Fulei Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Lixia Xu
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
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