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Liu C, Xie J, Lin B, Tian W, Wu Y, Xin S, Hong L, Li X, Liu L, Jin Y, Tang H, Deng X, Zou Y, Zheng S, Fang W, Cheng J, Dai X, Bao X, Zhao P. Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401061. [PMID: 38569519 PMCID: PMC11186051 DOI: 10.1002/advs.202401061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/13/2024] [Indexed: 04/05/2024]
Abstract
The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.
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Affiliation(s)
- Chuan Liu
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Jindong Xie
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Bo Lin
- College of Computer Science and TechnologyZhejiang UniversityHangzhou310053China
- Innovation Centre for InformationBinjiang Institute of Zhejiang UniversityHangzhou310053China
| | - Weihong Tian
- Changzhou Third People's HospitalChangzhou Medical CenterNanjing Medical UniversityChangzhou213000China
| | - Yifan Wu
- School of softwareZhejiang UniversityNingbo315100China
| | - Shan Xin
- Department of GeneticsYale School of medicineNew HavenCT06510USA
| | - Libing Hong
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Xin Li
- Department Chronic Inflammation and CancerGerman Cancer Research Center (DKFZ)69120HeidelbergGermany
| | - Lulu Liu
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Yuzhi Jin
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Hailin Tang
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Xinpei Deng
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Yutian Zou
- State Key Laboratory of Oncology in South ChinaGuangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhou510060China
| | - Shaoquan Zheng
- Breast Disease CenterThe First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510060China
| | - Weijia Fang
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Jinlin Cheng
- State Key Laboratory for Diagnosis and Treatment of Infectious DiseasesNational Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalZhejiang University School of MedicineZhejiang UniversityHangzhou310003China
| | - Xiaomeng Dai
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Xuanwen Bao
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
| | - Peng Zhao
- Department of Medical OncologyThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310003China
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Liu J, Shu J. Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging. Crit Rev Oncol Hematol 2024; 194:104235. [PMID: 38220125 DOI: 10.1016/j.critrevonc.2023.104235] [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: 07/19/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/16/2024] Open
Abstract
Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.
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Affiliation(s)
- Jiong Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, PR China; Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan 646000, PR China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, PR China; Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan 646000, PR China.
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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [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: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Li X, Lv X, Li H, Zhang G, Long Y, Li K, Fan Y, Jin D, Zhou F, Liu H. Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer. Adv Biol (Weinh) 2023; 7:e2300189. [PMID: 37423953 DOI: 10.1002/adbi.202300189] [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: 05/19/2023] [Revised: 06/17/2023] [Indexed: 07/11/2023]
Abstract
This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.
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Affiliation(s)
- Xue Li
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Xiaoying Lv
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Haijun Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Yaohang Long
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, 130012, China
| | - Dawei Jin
- Research Institute of Guizhou Huada Life Big Data, Guiyang, Guizhou, 550025, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
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Sun S, Cai X, Shao J, Zhang G, Liu S, Wang H. Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20599-20623. [PMID: 38124567 DOI: 10.3934/mbe.2023911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC.
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Affiliation(s)
- Shuo Sun
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Beihua University, Beihua University, Jilin 132013, China
| | - Xiaoni Cai
- Department of General Surgery, Shangyu People's Hospital of Shaoxing, the Second Affiliated Hospital of Zhejiang University Medical College Hospital, Shaoxing 312399, China
| | - Jinhai Shao
- Department of General Surgery, Shangyu People's Hospital of Shaoxing, the Second Affiliated Hospital of Zhejiang University Medical College Hospital, Shaoxing 312399, China
| | - Guimei Zhang
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun 130061, China
| | - Shan Liu
- Department of Nuclear Medicine, The Second Hospital of Jilin University, Jilin University, Changchun 130041, China
| | - Hongsheng Wang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Beihua University, Beihua University, Jilin 132013, China
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Chen X, Tan Z, Huo Y, Song J, Xu Q, Yang C, Jhanji V, Li J, Hou J, Zou H, Ali Khan G, Alzogool M, Wang R, Wang Y. Localized Corneal Biomechanical Alteration Detected In Early Keratoconus Based on Corneal Deformation Using Artificial Intelligence. Asia Pac J Ophthalmol (Phila) 2023; 12:574-581. [PMID: 37973045 DOI: 10.1097/apo.0000000000000644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/27/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE This study aimed to develop a novel method to diagnose early keratoconus by detecting localized corneal biomechanical changes based on dynamic deformation videos using machine learning. DESIGN Diagnostic research study. METHODS We included 917 corneal videos from the Tianjin Eye Hospital (Tianjin, China) and Shanxi Eye Hospital (Xi'an, China) from February 6, 2015, to August 25, 2022. Scheimpflug technology was used to obtain dynamic deformation videos under forced puffs of air. Fourteen new pixel-level biomechanical parameters were calculated based on a spline curve equation fitting by 115,200-pixel points from the corneal contour extracted from videos to characterize localized biomechanics. An ensemble learning model was developed, external validation was performed, and the diagnostic performance was compared with that of existing clinical diagnostic indices. The performance of the developed machine learning model was evaluated using precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS The ensemble learning model successfully diagnosed early keratoconus (area under the curve = 0.9997) with 95.73% precision, 95.61% recall, and 95.50% F1 score in the sample set (n=802). External validation on an independent dataset (n=115) achieved 91.38% precision, 92.11% recall, and 91.18% F1 score. Diagnostic accuracy was significantly better than that of existing clinical diagnostic indices (from 86.28% to 93.36%, all P <0.01). CONCLUSIONS Localized corneal biomechanical changes detected using dynamic deformation videos combined with machine learning algorithms were useful for diagnosing early keratoconus. Focusing on localized biomechanical changes may guide ophthalmologists, aiding the timely diagnosis of early keratoconus and benefiting the patient's vision.
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Affiliation(s)
- Xuan Chen
- School of Medicine, Nankai University, Tianjin, China
| | - Zuoping Tan
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Yan Huo
- School of Medicine, Nankai University, Tianjin, China
| | - Jiaxin Song
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Can Yang
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Vishal Jhanji
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Jing Li
- Shanxi Eye Hospital, Xi'an People's Hospital, Xi'an, China
| | - Jie Hou
- Jinan Mingshui Eye Hospital, Ji'nan, Shandong, China
| | - Haohan Zou
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Gauhar Ali Khan
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | | | - Riwei Wang
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Yan Wang
- School of Medicine, Nankai University, Tianjin, China
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
- Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin Eye Institute, Nankai University Affiliated Eye Hospital, Tianjin, China
- Nankai Eye Institute, Nankai University, Tianjin, China
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