In silico identification of a prognostic gene signature and virtual screening for hepatocellular carcinoma
Abstract
Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related deaths globally, with limited treatment options and a poor prognosis. The identification of reliable prognostic biomarkers and novel therapeutic candidates is essential for improving patient outcomes. This study employed an integrative bioinformatics and molecular simulation approach to uncover key gene signatures and explore potential plant-based therapeutics for HCC. Gene expression profiles from the GEO dataset GSE121248 were analyzed using R-based statistical tools to identify differentially expressed genes (DEGs). Key hub genes were selected through protein-protein interaction network analysis and functional enrichment. Three hub genesCOL1A1, NQO1, and FOS-were identified, and their expression was validated using the TCGA, GEPIA, and HPA databases. Notably, COL1A1 and NQO1 were upregulated in tumor tissues and associated with poor survival outcomes, while FOS was downregulated. To identify therapeutic candidates, bioactive compounds from Astragalus membranaceus were screened using ADMET profiling. Selected compounds were docked with the hub proteins, and molecular dynamics simulations, MM/GBSA binding energy calculations, and quantum chemical descriptors were employed to evaluate stability and reactivity. Among the candidates, isorhamnetin demonstrated strong and stable binding to all three hub targets, suggesting its potential as a multi-target inhibitor for HCC therapy. These findings…
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