Exploring novel independent prognostic biomarkers for hepatocellular carcinoma based on TCGA and GEO databases

Hepatocellular carcinoma (HCC) has become the fifth most common cancer globally, with the second-highest mortality rate and poor survival outcomes. In our research, we aimed to use The Cancer Genome Atlas and gene expression omnibus databases to identify potential genetic biomarkers to predict and improve the survival rate of HCC patients.

In GSE60502, GSE76427, and GSE84402, we performed differential expression analysis to obtain differentially expressed genes (DEGs). In the The Cancer Genome Atlas database, the FPKM expression profile was subjected to weighted gene co-expression analysis to obtain modules closely related to HCC. We received common genes by intersecting the genes in the module with the differential genes. Then, we fused the common genes' expression profiles, survival time, and survival status for univariate, Least Absolute Shrinkage and Selection Operator, and multivariate COX regression analysis to obtain prognostic genes. Predictive genes were performed in K-M survival analysis and combined with clinical data for independent predictive analysis.

After differential expression analysis, GSE60502 obtained 1107 DEGs, GSE76427 obtained 424 DEGs, and GSE84402 obtained 1668 DEGs. Through weighted gene co-expression analysis analysis, we can see that the blue and brown modules were closely associated with HCC. After single and multivariate COX regression analysis, we found that suppressor of cytokine signaling 2 (SOCS2) and SERPINF2 were independent prognostic genes for HCC. After survival analysis, HCC patients with high expression of SOCS2 and SERPINF2 had a longer survival time. These 2 genes in normal liver tissues were higher than in HCC at the transcriptional level.

SOCS2 and SERPINF2 were new independent prognostic genes of HCC. So, they may provide new treatment methods and measures for diagnosing HCC.