biostudies-literatureLin JNatural Science Foundation of Education Department of Anhui ProvinceNatural Science Foundation of Anhui ProvinceNational Natural Science Foundation of ChinaAnhui Provincial Special Fund for Clinical Medical Research Transformation2202https://www.ebi.ac.uk/biostudies/studies/S-EPMC12708435biostudies-literatureUnknown16(1)<h4>Background</h4>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.​.<h4>Methods</h4>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 exprDiscover oncologyNAP1L4 in hepatocellular carcinoma progression and treatment from gene expression to clinical impact.PMC12708435No.202304295107020040No. 2022AH040160No. 82370608No.2208085MH204Kong LShen JHan KLiu MXie ZChen GYao RXu HYin HLi YWang QGao YLin JfalseNAP1L4 in hepatocellular carcinoma progression and treatment from gene expression to clinical impact.<h4>Background</h4>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.​.<h4>Methods</h4>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 expr2025-01-01T00:00:00Z2025 Nov2026-06-09T03:15:15.435Z2026-06-09T03:07:29.745ZS-EPMC127084354125855610.1007/s12672-025-04016-7