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Wang Z, Zheng Z, Wang B, Zhan C, Yuan X, Lin X, Xin Q, Zhong Z, Qiu X. Characterization of a G2M checkpoint-related gene model and subtypes associated with immunotherapy response for clear cell renal cell carcinoma. Heliyon 2024; 10:e29289. [PMID: 38617927 PMCID: PMC11015143 DOI: 10.1016/j.heliyon.2024.e29289] [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: 02/06/2024] [Revised: 03/28/2024] [Accepted: 04/04/2024] [Indexed: 04/16/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) presents challenges in early diagnosis and effective treatment. In this study, we aimed to establish a prognostic model based on G2M checkpoint-related genes and identify associated clusters in ccRCC through clinical bioinformatic analysis and experimental validation. Utilizing a single-cell RNA dataset (GSE159115) and bulk-sequencing data from The Cancer Genome Atlas (TCGA) database, we analyzed the G2M checkpoint pathway in ccRCC. Differential expression analysis identified 45 genes associated with the G2M checkpoint, leading to the construction of a predictive model with four key genes (E2F2, GTSE1, RAD54L, and UBE2C). The model demonstrated reliable predictive ability for 1-, 3-, and 5-year overall survival, with AUC values of 0.794, 0.790, and 0.794, respectively. Patients in the high-risk group exhibited a worse prognosis, accompanied by significant differences in immune cell infiltration, immune function, TIDE and IPS scores, and drug sensitivities. Two clusters of ccRCC were identified using the "ConsensusClusterPlus" package, cluster 1 exhibited a worse survival rate and was resistant to chemotherapeutic drugs of Axitinib, Erlotinib, Pazopanib, Sunitinib, and Temsirolimus, but not Sorafenib. Targeted experiments on RAD54L, a gene involved in DNA repair processes, revealed its crucial role in inhibiting proliferation, invasion, and migration in 786-O cells. In conclusion, our study offers valuable insights into the molecular mechanisms underlying ccRCC, identifying potential prognostic genes and molecular subtypes associated with the G2M checkpoint. These findings hold promise for guiding personalized treatment strategies in the management of ccRCC.
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Affiliation(s)
- Zhenwei Wang
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
| | - Zongtai Zheng
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
| | - Bangqi Wang
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
| | - Changxin Zhan
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
- The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China
| | - Xuefeng Yuan
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Xiaoqi Lin
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
| | - Qifan Xin
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
| | - Zhihui Zhong
- Center of Stem Cell and Regenerative Medicine, Gaozhou People's Hospital, Gaozhou, 525200, Guangdong, China
| | - Xiaofu Qiu
- Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China
- The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
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Dwivedi K, Rajpal A, Rajpal S, Kumar V, Agarwal M, Kumar N. Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107864. [PMID: 37866126 DOI: 10.1016/j.cmpb.2023.107864] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/07/2023] [Accepted: 10/11/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND AND OBJECTIVE The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and quality of life. Being a heterogeneous disease at the molecular and cellular level, the biomarkers responsible for the heterogeneity aid in distinguishing NSCLC into its prominent subtypes-adenocarcinoma and squamous cell carcinoma. Moreover, if identified, these biomarkers could pave the path to targeted therapy. Through this work, a novel explainable AI (XAI)-guided deep learning framework is proposed that assists in discovering a set of significant NSCLC-relevant biomarkers using methylation data. METHODS The proposed framework is divided into two blocks- the first block combines an autoencoder and a neural network to classify NSCLC instances. The second block utilizes various eXplainable AI (XAI) methods, namely IntegratedGradients, GradientSHAP, and DeepLIFT, to discover a set of seven significant biomarkers. RESULTS The classification performance of the biomarkers discovered using the proposed framework is evaluated by employing multiple machine learning algorithms, among which the Multilayer Perceptron (MLP) algorithm-based model outperforms others, yielding a 10-fold cross-validation accuracy of 91.53%. An improved accuracy of 96.37% is achieved by integrating RNA-Seq, CNV, and methylation data. On performing statistical analysis using the Friedman and Nemenyi tests, the MLP model is found to be significantly better than other machine learning-based models. Further, the clinical efficacy of the resultant biomarkers is established based on their potential druggability, the likelihood of predicting NSCLC patients' survival, gene-disease association, and biological pathways targeted by them. While the biomarkers C18orf18, CCNT2, THOP1, and TNPO2, are found potentially druggable, the biomarkers CCDC15, SNORA9, THOP1, and TNPO2 are found prognostically relevant. On further analysis, some of the discovered biomarkers are found to be associated with around 104 diseases. Moreover, five KEGG, ten Reactome, and three Wiki pathways are found to be triggered by the biomarkers discovered. CONCLUSION In summary, the proposed framework uncovers a set of clinically effective biomarkers that accurately classify NSCLC. As a future course of work, efforts would be made to combine a variety of omics data with histopathological data to unveil more precise biomarkers for devising personalized therapy.
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Affiliation(s)
- Kountay Dwivedi
- Department of Computer Science, University of Delhi, Delhi, India.
| | - Ankit Rajpal
- Department of Computer Science, University of Delhi, Delhi, India.
| | - Sheetal Rajpal
- Department of Computer Science, Dyal Singh College, Delhi, India.
| | - Virendra Kumar
- Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India.
| | - Manoj Agarwal
- Department of Computer Science, Hans Raj College, University of Delhi, Delhi, India.
| | - Naveen Kumar
- Department of Computer Science, University of Delhi, Delhi, India.
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Wang JM, Li X, Yang P, Geng WB, Wang XY. Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients. BMC Gastroenterol 2022; 22:76. [PMID: 35189810 PMCID: PMC8862389 DOI: 10.1186/s12876-022-02159-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/15/2022] [Indexed: 02/08/2023] Open
Abstract
Background Accumulating studies have demonstrated that lncRNAs play vital roles in the prognosis of gastric cancer (GC); however, the prognostic value of N6-methyladenosine-related lncRNAs has not been fully reported in GC. This study aimed to construct and validate an m6A-related lncRNA pair signature (m6A-LPS) for predicting the prognosis of GC patients. Methods GC cohort primary data were downloaded from The Cancer Genome Atlas. We analysed the coexpression of m6A regulators and lncRNAs to identify m6A-related lncRNAs. Based on cyclical single pairing along with a 0-or-1 matrix and least absolute shrinkage and selection operator-penalized regression analyses, we constructed a novel prognostic signature of m6A-related lncRNA pairs with no dependence upon specific lncRNA expression levels. All patients were divided into high-risk and low-risk group based on the median risk score. The predictive reliability was evaluated in the testing dataset and whole dataset with receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis was used to identify potential pathways. Results Fourteen m6A-related lncRNA pairs consisting of 25 unique lncRNAs were used to construct the m6A-LPS. Kaplan–Meier analysis showed that the high-risk group had poor prognosis. The area under the curve for 5-year overall survival was 0.906, 0.827, and 0.882 in the training dataset, testing dataset, and whole dataset, respectively, meaning that the m6A-LPS was highly accurate in predicting GC patient prognosis. The m6A-LPS served as an independent prognostic factor for GC patients after adjusting for other clinical factors (p < 0.05). The m6A-LPS had more accuracy and a higher ROC value than other prognostic models for GC. Functional analysis revealed that high-risk group samples mainly showed enrichment of extracellular matrix receptor interactions and focal adhesion. Moreover, N-cadherin and vimentin, known biomarkers of epithelial–mesenchymal transition, were highly expressed in high-risk group samples. The immune infiltration analysis showed that resting dendritic cells, monocytes, and resting memory CD4 T cells were significantly positively related to the risk score. Thus, m6A-LPS reflected the infiltration of several types of immune cells. Conclusions The signature established by pairing m6A-related lncRNAs regardless of expression levels showed high and independent clinical prediction value in GC patients.
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Affiliation(s)
- Jun-Mei Wang
- Department of Gastroenterology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, China.,Dalian Medical University, Dalian, 116044, China
| | - Xuan Li
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210000, China
| | - Peng Yang
- Department of Gastroenterology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, China.,Dalian Medical University, Dalian, 116044, China
| | - Wen-Bin Geng
- Department of Gastroenterology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, China.,Dalian Medical University, Dalian, 116044, China
| | - Xiao-Yong Wang
- Department of Gastroenterology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, China.
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Chang JJ, Wang XY, Zhang W, Tan C, Sheng WQ, Xu MD. Comprehensive molecular characterization and identification of prognostic signature in stomach adenocarcinoma on the basis of energy-metabolism-related genes. World J Gastrointest Oncol 2022; 14:478-497. [PMID: 35317313 PMCID: PMC8919002 DOI: 10.4251/wjgo.v14.i2.478] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/09/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, but its molecular and prognostic characteristics has never been fully illustrated.
AIM To describe a molecular evaluation of primary STAD and develop new therapies and identify promising prognostic signatures.
METHODS We describe a comprehensive molecular evaluation of primary STAD based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.
RESULTS On the basis of 86 EMRGs that were significantly associated to patients’ progression-free survival (PFS), we propose a molecular classification dividing gastric cancer into two subtypes: Cluster 1, most of which are young patients and display more immune and stromal cell components in tumor microenvironment and lower tumor priority; and Cluster 2, which show early stages and better PFS. Moreover, we construct a 6-gene signature that can classify the prognostic risk of patients after a three-phase training test and validation process. Compared with patients with low-risk score, patients with high-risk score had shorter overall survival. Furthermore, calibration and DCA analysis plots indicate the excellent predictive performance of the 6-gene signature, and which present higher robustness and clinical usability compared with three previous reported prognostic gene signatures. According to gene set enrichment analysis, gene sets related to the high-risk group were participated in the ECM receptor interaction and hedgehog signaling pathway.
CONCLUSION Identification of the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.
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Affiliation(s)
- Jin-Jia Chang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiao-Yu Wang
- Laboratory of Immunology and Virology, Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Wei Zhang
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Cong Tan
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Institute of Pathology, Fudan University, Shanghai 200032, China
| | - Wei-Qi Sheng
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Institute of Pathology, Fudan University, Shanghai 200032, China
| | - Mi-Die Xu
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Institute of Pathology, Fudan University, Shanghai 200032, China
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Liu Z, Li J, Hu X, Xu H. Helicobacter pylori-induced protein tyrosine phosphatase receptor type C as a prognostic biomarker for gastric cancer. J Gastrointest Oncol 2021; 12:1058-1073. [PMID: 34295557 DOI: 10.21037/jgo-21-305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Background Helicobacter pylori (H. pylori) infection is closely associated with the tumorigenesis of gastric cancer. The aim of the present study was to identify the key regulator in H. pylori-related gastric cancer and to study the expression level and clinical value of the indicated key regulator in gastric cancer. Methods The GSE6143 dataset was used to identify differentially expressed genes (DEGs) with limma R package, and enrichment analysis was done using the Metascape web-based portal. The protein-protein interaction analysis was done using Search Tool for the Retrieval of Interacting Genes/Proteins. Gastric adenocarcinoma AGS and BGC-823 cells were treated with H. pylori strain 26695 to construct the in vitro H. pylori infection model, and quantitative reverse transcription polymerase chain reaction was used to analyze the mRNA levels of indicated genes. The correlation analysis between two genes in gastric cancer was done by GEPIA. Furthermore, the PTPRC expression by pathological features analysis was conducted in UALCAN, an easy to use, interactive web-portal (http://ualcan.path.uab.edu). The survival analysis for gastric cancer, based on PTPRC expression levels, was done using the Kaplan-Meier plotter. Results DEGs in gastric mucosa with or without H. pylori infection were identified and enriched in immune-related pathways and cancer pathways. The protein-protein interaction analysis confirmed the enrichment analysis of gene ontology. H. pylori strain 26695 exposure also confirmed the alteration of gene expression levels in AGS and BGC-823 cells. PTPRC was co-expressed with CSF2RB and TNFRSF7, indicating a significant positive correlation in gastric cancer. PTPRC was overexpressed in gastric cancer, and the overexpression of PTPRC was positively correlated with the progression of gastric cancer. Furthermore, the high expression of PTPRC could act as a poor prognostic factor for gastric cancer patients, especially for those at advanced stage. Conclusions H. pylori-induced PTPRC is overexpressed in gastric cancer, and the overexpression of PTPRC is positively associated with the development of gastric cancer. The high expression of PTPRC could serve as poor prognostic biomarker for gastric cancer patients, especially for those at advanced stage. H. pylori-induced PTPRC is a prognostic biomarker for gastric cancer.
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Affiliation(s)
- Zichuan Liu
- Department of Internal Medicine, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Jianchang Li
- Department of Internal Medicine, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Xiaoshan Hu
- Department of Gastrointestinal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Houwei Xu
- Department of Gastrointestinal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
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Wang G, Zhan T, Li F, Shen J, Gao X, Xu L, Li Y, Zhang J. The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature. J Cancer 2021; 12:3344-3353. [PMID: 33976744 PMCID: PMC8100809 DOI: 10.7150/jca.49658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 03/27/2021] [Indexed: 12/13/2022] Open
Abstract
Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.
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Affiliation(s)
- Guoguang Wang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tian Zhan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fan Li
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jian Shen
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiang Gao
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuan Li
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center For Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Cui L, Wang P, Ning D, Shao J, Tan G, Li D, Zhong X, Mi W, Zhang C, Jin S. Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization. Front Cell Dev Biol 2021; 9:631534. [PMID: 33912555 PMCID: PMC8072341 DOI: 10.3389/fcell.2021.631534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/22/2021] [Indexed: 02/03/2023] Open
Abstract
Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and non-coding RNA (ncRNA) levels have been identified by high-throughput expression profiling for GC. To date, there have been no reports on integrated optimization analysis based on the GC global lncRNA-miRNA-mRNA network and the prognostic mechanism has not been studied. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was constructed based on the ceRNA hypothesis by combining miRNA-target interactions and data on the expression of GC. To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. We further obtained candidate prognostic signatures by calculating the integrated score and analyzed the robustness of these signatures by combination strategy. The biological roles of key candidate signatures were also explored. Finally, we targeted the PHF10 gene and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guidelines.
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Affiliation(s)
- Lin Cui
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Ping Wang
- Department of Interventional Radiology, The Third Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Dandan Ning
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Jing Shao
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Guiyuan Tan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Dajian Li
- Department of Gastroenterology and Hepatology, The First Hospital Of Harbin, Harbin, China
| | - Xiaoling Zhong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wanqi Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shizhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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