Hepatocellular carcinoma (HCC) is a primary liver cancer with a prognosis influenced by clinical and gene expression factors. Using RNA-seq data from TCGA-LIHC, we found 3,623 genes expressed differently and used survival analysis, machine learning, and pathway enrichment to discover markers related to tumor growth. Three prognostic genes (CA9, AP1M1, and CASC9) were associated with survival, while diagnostic markers highlighted metabolic dysfunction in HCC. Our study demonstrates the power of integrative bioinformatics in identifying key molecular targets, offering insights for precision oncology and improved patient management.
