Bioinformatics screened of biomarkers for the prognosis of hepatocellular carcinoma.

BackgroundThis study aimed to identify hub genes linked to hepatocellular carcinoma (LIHC) pathogenesis using bioinformatics analysis.MethodA total of 3865 samples from 12 datasets in the HCCDB database were analyzed to identify prognostic expression genes (PDGs). Enrichment analysis using DAVID and GSCA databases unveiled biological processes and signaling pathways associated with PDGs. Cytohubba app was utilized to identify 6 hub genes from the PDGs. Verification of hub genes was conducted using three GEO datasets and Western blot. Histopathological staining data of hub genes in LIHC patients were retrieved from the Human Protein Atlas database. Comprehensive analyses of hub genes were performed, including immune infiltration, prognosis, survival, methylation, gene mutation, related miRNA, and single-cell type. Potential therapeutic drugs were predicted using GDSC and CTRP databases.ResultA total of 1259 differential genes were screened, yielding 82 PDGs (36 up-regulated and 46 down-regulated genes). Hub genes identified included CDC20, TOP2A, CDK1 (up-regulated), and CAT, TAT, FTCD (down-regulated). These hub genes exhibited strong associations with immune cells and showed promising prognostic value based on AUC analysis. Reduced promoter methylation levels of TOP2A, CDK1, and FTCD in LIHC were observed. Single nucleotide polymorphisms analysis highlighted prevalent variants and miRNA expression associations impacting patient survival. Hub genes were enriched in various cell types. Trametinib, selumetinib, RDEA119, and teniposide were identified as potential drugs for LIHC treatment.ConclusionCDC20, TOP2A, CDK1, CAT, TAT, and FTCD may contribute to LIHC development and serve as novel prognostic biomarkers.