Creation of an innovative diagnostic framework for hepatocellular carcinoma employing bioinformatics techniques focused on senescence-related and pyroptosis-related genes.

Background: Liver hepatocellular carcinoma (LIHC) continues to pose a major global health concern and is characterized by elevated mortality rates and a lack of effective therapies. This study aimed to explore differential gene expression linked to cellular senescence and pyroptosis in LIHC and to develop a prognostic risk model for use in clinical settings. Methods: We acquired datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). DESeq2 was used to identify differentially expressed genes associated with cell senescence and pyrodeath. The least absolute shrinkage and selection operator (LASSO) regression model was developed using cellular senescence- and pyroptosis-related differentially expressed genes (CSR&PRDEGs), and its predictive performance was evaluated with Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves. We also performed various functional analyses of the genes. These findings were validated by real-time fluorescence quantitative polymerase chain reaction (PCR). Results: Using bioinformatics analysis, we developed a prognostic risk framework incorporating six critical genes: Conclusions: This study systematically elucidated the functions of genes associated with senescence and pyroptosis in LIHC cells. The constructed prognostic risk model serves to guide the development of personalized treatment plans, enhance patient management via risk stratification, facilitate the identification of high-risk patients, intensify monitoring or implement proactive interventions, thereby providing a novel perspective for the diagnosis and treatment of LIHC.