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Wei Y, Ma L, Peng Q, Lu L. Establishing an oxidative stress mitochondria-related prognostic model in hepatocellular carcinoma based on multi-omics characteristics and machine learning computational framework. Discov Oncol 2024; 15:287. [PMID: 39014263 PMCID: PMC11252104 DOI: 10.1007/s12672-024-01147-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 07/05/2024] [Indexed: 07/18/2024] Open
Abstract
Hepatocellular carcinoma (HCC) has high incidence and mortality rates worldwide. Damaged mitochondria are characterized by the overproduction of reactive oxygen species (ROS), which can promote cancer development. The prognostic value of the interplay between mitochondrial function and oxidative stress in HCC requires further investigation. Gene expression data of HCC samples were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). We screened prognostic oxidative stress mitochondria-related (OSMT) genes at the bulk transcriptome level. Based on multiple machine learning algorithms, we constructed a consensus oxidative stress mitochondria-related signature (OSMTS), which contained 26 genes. In addition, we identified six of these genes as having a suitable prognostic value for OSMTS to reduce the difficulty of clinical application. Univariate and multivariate analyses verified the OSMTS as an independent prognostic factor for overall survival (OS) in HCC patients. The OSMTS-related nomogram demonstrated to be a powerful tool for the clinical diagnosis of HCC. We observed differences in biological function and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. The highest expression of the OSMTS was detected in hepatocytes at the single-cell transcriptome level. Hepatocytes in the high- and low-risk groups differed significantly in terms of biological function and intercellular communication. Moreover, at the spatial transcriptome level, high expression of OSMTS was mainly in regions enriched in hepatocytes and B cells. Potential drugs targeting specific risk subgroups were identified. Our study revealed that the OSMTS can serve as a promising tool for prognosis prediction and precise intervention in HCC patients.
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Affiliation(s)
- Yitian Wei
- Department of Medical Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lujuan Ma
- Department of Medical Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qian Peng
- Department of Medical Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lin Lu
- Department of Medical Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
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2
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Yu J, Gong Y, Xu Z, Chen L, Li S, Cui Y. Prognostic and therapeutic insights into colorectal carcinoma through immunogenic cell death gene profiling. PeerJ 2024; 12:e17629. [PMID: 38938617 PMCID: PMC11210462 DOI: 10.7717/peerj.17629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/03/2024] [Indexed: 06/29/2024] Open
Abstract
While the significance of immunogenic cell death (ICD) in oncology is acknowledged, its specific impact on colorectal carcinoma remains underexplored. In this study, we delved into the role of ICD in colorectal carcinoma, a topic not yet comprehensively explored. A novel ICD quantification system was developed to forecast patient outcomes and the effectiveness of immunotherapy. Utilizing single-cell sequencing, we constructed an ICD score within the tumor immune microenvironment (TIME) and examined immunogenic cell death related genes (ICDRGs). Using data from TCGA and GEO, we discovered two separate molecular subcategories within 1,184 patients diagnosed with colon adenocarcinoma/rectum adenocarcinoma (COADREAD). The ICD score was established by principal component analysis (PCA), which classified patients into groups with low and high ICD scores. Further validation in three independent cohorts confirmed the model's accuracy in predicting immunotherapy success. Patients with higher ICD scores exhibited a "hot" immune phenotype and showed increased responsiveness to immunotherapy. Key genes in the model, such as AKAP12, CALB2, CYR61, and MEIS2, were found to enhance COADREAD cell proliferation, invasion, and PD-L1 expression. These insights offered a new avenue for anti-tumor strategies by targeting ICD, marking advances in colorectal carcinoma treatment.
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Affiliation(s)
- Jinglu Yu
- PuDong Traditional Chinese Medicine Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China, Shanghai, Pudong, China
| | - Yabin Gong
- Department of Oncology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui District, China
| | - Zhenye Xu
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui District, China
| | - Lei Chen
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui District, China
| | - Shuang Li
- Department of Gastroenterology, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yongkang Cui
- Department of Gastroenterology, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Chen B, Zhou M, Guo L, Sun X, Huang H, Wu K, Chen W, Wu D. A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma. Aging (Albany NY) 2024; 16:10033-10062. [PMID: 38862242 PMCID: PMC11210246 DOI: 10.18632/aging.205916] [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: 11/15/2023] [Accepted: 05/03/2024] [Indexed: 06/13/2024]
Abstract
Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.
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Affiliation(s)
- Bohong Chen
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Mingguo Zhou
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Li Guo
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Xinyue Sun
- Department of neurology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Haoxiang Huang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Kaijie Wu
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Wei Chen
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
| | - Dapeng Wu
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China
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Hu D, Shen X, Gao P, Mao T, Chen Y, Li X, Shen W, Zhuang Y, Ding J. Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine. EPMA J 2024; 15:321-343. [PMID: 38841626 PMCID: PMC11147991 DOI: 10.1007/s13167-024-00362-8] [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: 03/14/2024] [Accepted: 04/17/2024] [Indexed: 06/07/2024]
Abstract
Background Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC. Methods Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation. Results Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts. Conclusion This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00362-8.
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Affiliation(s)
- Dingtao Hu
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433 China
| | - Xu Shen
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433 China
| | - Peng Gao
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433 China
| | - Tiantian Mao
- Department of Emergency, Shanghai Tenth People’s Hospital, Tongji University, School of Medicine, 301 Yanchang Middle Road, Shanghai, 200072 China
| | - Yuan Chen
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433 China
- University of Shanghai for Science and Technology, Shanghai, 200093 China
| | - Xiaofeng Li
- Department of Emergency, Shanghai Tenth People’s Hospital, Tongji University, School of Medicine, 301 Yanchang Middle Road, Shanghai, 200072 China
| | - Weifeng Shen
- The Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Yugang Zhuang
- Department of Emergency, Shanghai Tenth People’s Hospital, Tongji University, School of Medicine, 301 Yanchang Middle Road, Shanghai, 200072 China
| | - Jin Ding
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, 800 Xiangyin Road, Shanghai, 200433 China
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Lv F, Xiong Q, Qi M, Dai C, Zhang X, Cheng S. Unraveling neoantigen-associated genes in bladder cancer: An in-depth analysis employing 101 machine learning algorithms. ENVIRONMENTAL TOXICOLOGY 2024; 39:2528-2544. [PMID: 38189174 DOI: 10.1002/tox.24123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/12/2023] [Accepted: 12/25/2023] [Indexed: 01/09/2024]
Abstract
The therapeutic outcomes for bladder cancer (BLCA) remain suboptimal. Concurrently, there is a growing appreciation for the role of neoantigens in tumors. In this study, we explored the mechanisms underlying the involvement of neoantigen-associated genes in BLCA and their impact on prognosis. Our analysis incorporated both single-cell sequencing and bulk sequencing data sourced from publicly available databases. By employing a comprehensive set of 10 machine learning algorithms, we generated 101 algorithm combinations. The optimal combination, determined based on consistency indices, was utilized to construct a prognostic model comprising nine genes (CAPG, ACTA2, PDIA6, AKNA, PTMS, SNAP23, ID2, CD3G, SP140). Subsequently, we validated this model in an independent cohort, demonstrating its robust testing efficacy. Moreover, we explored the correlations between various clinical traits, model scores, and genes. Leveraging extensive public data resources, we conducted a drug sensitivity analysis to provide insights for targeted drug screening. Additionally, consensus clustering analysis and immune infiltration analysis were performed on bulk sequencing datasets and immunotherapy cohorts. These analyses yield valuable insights into the role of neoantigens in BLCA, guiding future research endeavors.
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Affiliation(s)
- Fang Lv
- Department of General Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Qi Xiong
- Department of Urology, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Meiying Qi
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Caixia Dai
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiuhong Zhang
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shunhua Cheng
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Guan H, Yuan Q, Lv K, Qi Y, Jiang Y, Zhang S, Miao D, Wang Z, Lin J. Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework. J Cancer 2024; 15:3350-3361. [PMID: 38817855 PMCID: PMC11134443 DOI: 10.7150/jca.94759] [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: 01/27/2024] [Accepted: 03/13/2024] [Indexed: 06/01/2024] Open
Abstract
This study has used machine learning algorithms to develop a predictive model for differentiating between dermoscopic images of basal cell carcinoma (BCC) and actinic keratosis (AK). We compiled a total of 904 dermoscopic images from two sources - the public dataset (HAM10000) and our proprietary dataset from the First Affiliated Hospital of Dalian Medical University (DAYISET 1) - and subsequently categorised these images into four distinct cohorts. The study developed a deep learning model for quantitative analysis of image features and integrated 15 machine learning algorithms, generating 207 algorithmic combinations through random combinations and cross-validation. The final predictive model, formed by integrating XGBoost with Lasso regression, exhibited effective performance in the differential diagnosis of BCC and AK. The model demonstrated high sensitivity in the training set and maintained stable performance in three validation sets. The area under the curve (AUC) value reached 1.000 in the training set and an average of 0.695 in the validation sets. The study concludes that the constructed discriminative diagnostic model based on machine learning algorithms has excellent predictive capabilities that could enhance clinical decision-making efficiency, reduce unnecessary biopsies, and provide valuable guidance for further treatment.
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Affiliation(s)
- Hewen Guan
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Qihang Yuan
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Kejia Lv
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yushuo Qi
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yuankuan Jiang
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Shumeng Zhang
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Dong Miao
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Zhiyi Wang
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Jingrong Lin
- Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
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Xiao L, He R, Hu K, Song G, Han S, Lin J, Chen Y, Zhang D, Wang W, Peng Y, Zhang J, Yu P. Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning. Apoptosis 2024:10.1007/s10495-024-01960-7. [PMID: 38615305 DOI: 10.1007/s10495-024-01960-7] [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] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.
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Affiliation(s)
- Leyang Xiao
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ruifeng He
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Kaibo Hu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Gelin Song
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Shengye Han
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jitao Lin
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yixuan Chen
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Deju Zhang
- Food and Nutritional Sciences, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, 999077, Hong Kong, Hong Kong
| | - Wuming Wang
- Department of Thoracic Surgery, Jiangxi Provincial Chest Hospital, Nanchang, 330006, People's Republic of China
| | - Yating Peng
- Department of Dermatology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jing Zhang
- Department of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, 332000, People's Republic of China.
| | - Peng Yu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, 332000, People's Republic of China.
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Xie J, Wu D, Zhang P, Zhao S, Qi M. Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms. Exp Dermatol 2024; 33:e15070. [PMID: 38570935 DOI: 10.1111/exd.15070] [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: 01/25/2024] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Dan Wu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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Yang R, Sun F, Shi Y, Wang H, Fan Y, Wu Y, Fan R, Wu S, Sun L. Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma. J Cancer 2024; 15:2810-2828. [PMID: 38577599 PMCID: PMC10988306 DOI: 10.7150/jca.92698] [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: 11/28/2023] [Accepted: 02/22/2024] [Indexed: 04/06/2024] Open
Abstract
Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.
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Affiliation(s)
- Ruida Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Feidi Sun
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yu Shi
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Huanhuan Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yangwei Fan
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yinying Wu
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Ruihan Fan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Shaobo Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Liankang Sun
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
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Wang X, Chan S, Chen J, Xu Y, Dai L, Han Q, Wang Z, Zuo X, Yang Y, Zhao H, Wang M, Wang C, Li Z, Zhang H, Chen W. Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer. Cancer Cell Int 2024; 24:52. [PMID: 38297270 PMCID: PMC10829178 DOI: 10.1186/s12935-024-03239-y] [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: 12/04/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). METHODS 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified. RESULTS The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. CONCLUSION We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Jiajie Chen
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yang Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Hu Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Chen Wang
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zichen Li
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Huabing Zhang
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, 230032, Anhui, China.
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, 239000, Anhui, China.
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China.
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Zou M, Li H, Su D, Xiong Y, Wei H, Wang S, Sun H, Wang T, Xi Q, Zuo Y, Yang L. Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes. Brief Bioinform 2023; 25:bbad430. [PMID: 38040491 PMCID: PMC10783866 DOI: 10.1093/bib/bbad430] [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: 08/16/2023] [Revised: 09/29/2023] [Accepted: 11/05/2023] [Indexed: 12/03/2023] Open
Abstract
Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.
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Affiliation(s)
- Min Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongmei Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qilemuge Xi
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot 010010, China
- Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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