Data mining of key genes expression in hepatocellular carcinoma: novel potential biomarkers of diagnosis prognosis or progression

Abstract

AbstractHepatocellular carcinoma (HCC) is one of the main cancer-related causes of death worldwide. The study aimed to perform a data mining analysis of the expression and regulatory role of key genes in HCC to reveal novel potential biomarkers of diagnosis prognosis, or progression since their availability is still almost lacking. Starting from data of our cohort of patients (HCV-positive HCC pts undergoing liver transplantation (LR, n = 10) and donors (LD, n = 14), deeply analyzed previously, in which apelin, osteopontin, osteoprotegerin, NOTCH-1, CASP-3, Bcl-2, BAX, PTX3, and NPTX2 were analyzed, we applied statistical analysis and in-silico tools (Gene Expression Profiling Interactive Analysis, HCCDB database and GeneMania, UALCAN) to screen and identify the key genes. Firstly, we performed a stepwise regression analysis using our mRNA-datasets which revealed that higher expression levels of apelin and osteopontin were positively associated with the HCC and identified that the most consistently differentially expressed gene across multiple HCC expression datasets was only OPN. This comprehensive strategy of data mining evidenced that OPN might have a potential function as an important tumor marker-driven oncogenesis being associated with poor prognosis of HCC patients.