Six genes as potential diagnosis and prognosis biomarkers for hepatocellular carcinoma through data mining.

Abstract

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and the third of cancer mortality worldwide. Although the study of HCC has made great progress, the molecular mechanism and signal pathways of HCC are not yet clear. Therefore, it is necessary to investigate the early diagnosis and prognosis biomarkers for HCC. The aim of this study is to screen the relevant genes and study the association of gene expression with the survival status of HCC patients using bioinformatics approaches, in the hope of establishing marker genes for diagnosis and prognosis of HCC. The gene expression data and corresponding clinical information of HCC samples were downloaded from the The Cancer Genome Atlas database. We performed to study the relationship between gene expression and prognosis of HCC and screen significantly relevant genes associated with prognosis of HCC by analyzing survival and function enrichment of genes. In this study, we collected 421 samples with gene expression data, including 371 tumor samples and 50 normal samples. By using single factor Cox regression analysis, we screened 1,197 genes significantly associated with survival time in the modeling data containing 117 samples and also searched six genes as the best markers to predict living status of HCC patients. Besides, we established score system of survival risk of HCC. Our study recognized six genes (PGBD3, PGM5P3-AS1, RNF5, UTP11, BAG6, and KCND2) to be significantly associated with diagnosis and prognosis of HCC, providing novel targets for studying potential mechanism about the progression of HCC.