{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lin J"],"funding":["Natural Science Foundation of Education Department of Anhui Province","Natural Science Foundation of Anhui Province","National Natural Science Foundation of China","Anhui Provincial Special Fund for Clinical Medical Research Transformation"],"pagination":["2202"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12708435"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(1)"],"pubmed_abstract":["Background
Hepatocellular carcinoma (HCC) remains one of the deadliest malignancies globally, posing substantial health threats and economic burdens. Despite advances in therapeutic modalities-including surgical resection, liver transplantation, and targeted therapies-HCC prognosis remains unsatisfactory. This highlights an urgent need for novel biomarkers to improve prognostic stratification. Senescence-related genes (SRGs) have emerged as promising candidates for HCC prognostic exploration, making their association with clinical outcomes the focus of this study..Methods
Comprehensive machine learning was applied to screen SRGs associated with HCC survival, identifying key prognostic genes. A clinical nomogram based on SRG scores was constructed and validated. NAP1L4 expr"],"journal":["Discover oncology"],"pubmed_title":["NAP1L4 in hepatocellular carcinoma progression and treatment from gene expression to clinical impact."],"pmcid":["PMC12708435"],"funding_grant_id":["No.202304295107020040","No. 2022AH040160","No. 82370608","No.2208085MH204"],"pubmed_authors":["Kong L","Shen J","Han K","Liu M","Xie Z","Chen G","Yao R","Xu H","Yin H","Li Y","Wang Q","Gao Y","Lin J"],"additional_accession":[]},"is_claimable":false,"name":"NAP1L4 in hepatocellular carcinoma progression and treatment from gene expression to clinical impact.","description":"Background
Hepatocellular carcinoma (HCC) remains one of the deadliest malignancies globally, posing substantial health threats and economic burdens. Despite advances in therapeutic modalities-including surgical resection, liver transplantation, and targeted therapies-HCC prognosis remains unsatisfactory. This highlights an urgent need for novel biomarkers to improve prognostic stratification. Senescence-related genes (SRGs) have emerged as promising candidates for HCC prognostic exploration, making their association with clinical outcomes the focus of this study..Methods
Comprehensive machine learning was applied to screen SRGs associated with HCC survival, identifying key prognostic genes. A clinical nomogram based on SRG scores was constructed and validated. NAP1L4 expr","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-09T03:15:15.435Z","creation":"2026-06-09T03:07:29.745Z"},"accession":"S-EPMC12708435","cross_references":{"pubmed":["41258556"],"doi":["10.1007/s12672-025-04016-7"]}}