Comprehensive analysis to develop a stromal senescence-associated gene signature for predicting hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) continues to pose a major health concern for global public health, characterized by pronounced clinical heterogeneity in survival outcomes and therapeutic efficacies. The stromal senescence constitutes a major impediment in constructing robust prognostic models. This study aimed to develop and validate a robust prognostic prediction model to accurately assess the prognostic risk of HCC patients and facilitate personalized precision treatment. Methods: We obtained data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. Area Under the Curve Cell (AUCell) was used to quantify senescence in single-cell RNA sequencing (scRNA-seq) of HCC. A stromal-senescence prognostic signature was then built by using Cox and least absolute shrinkage and selection operator (LASSO) analyses. High- and low-risk stratification was interrogated with clusterProfiler, maftools, immune-checkpoint analysis, and oncoPredict. Signature expression was cross-validated in scRNA-seq, bulk RNA sequencing (RNA-seq), proteomic, and spatial transcriptomic datasets. Small-molecule ligands targeting DNASE1L3 were identified by virtual screening, and their binding was verified by molecular docking and dynamics simulations. Results: It was found that HCC-associated stromal cells exhibited the most pronounced levels of senescence among cell populations. Using machine-learning algorithms, we successfully established Conclusions: This study developed a robust stromal senescence-associated gene signature that accurately predicts survival outcomes in HCC patients and identified candidate stromal senescence-related molecules for more effective personalized therapy.