{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15"],"submitter":["Tian J"],"pubmed_abstract":["<h4>Instruction</h4>Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation.<h4>Methods</h4>In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set varia"],"journal":["Frontiers in immunology"],"pagination":["1411161"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11116790"],"repository":["biostudies-literature"],"pubmed_title":["Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes."],"pmcid":["PMC11116790"],"pubmed_authors":["Tian J","Song L","Yang Z","Zou J","Jiang X","Meng J"],"additional_accession":[]},"is_claimable":false,"name":"Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes.","description":"<h4>Instruction</h4>Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation.<h4>Methods</h4>In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set varia","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-04-08T18:35:22.667Z","creation":"2026-04-08T09:40:46.299Z"},"accession":"S-EPMC11116790","cross_references":{"pubmed":["38799437"],"doi":["10.3389/fimmu.2024.1411161"]}}