Prognostic prediction and gene regulation network of in hepatocellular carcinoma based on data mining.

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with a high fatality rate, predicting poor prognosis and therapeutic effect. Screening potential prognostic genes in HCC could be a creative way to advance clinical treatment. Eukaryotic translation initiation factor 2 subunit beta () has reportedly been linked to several tumors, including liver cancer, but the prognostic predictions remain unknown. Therefore, we aimed to clarify the prognostic role and interaction network of in HCC using bioinformatics data.

Methods: We screened using the Oncomine, Ualcan, and TCGA databases. R software was used to analyze the mRNA level and clinicopathological characteristics of hepatocellular carcinoma. Evaluation of the correlations between and patients' survival was made using the Kaplan-Meier curves and Cox proportional hazards regression model. Then, the influence of gene mutations on the prognosis of patients was explored by cBioPortal. The protein-protein interaction network of 50 similar genes related to was implemented by GEPIA2 and Metascape. The LinkedOmics database allowed us to carry out Gene Set Enrichment Analysis. Finally, we constructed the kinase, miRNA, and transcription factor target networks using GeneMANIA.

Results: mRNA was overexpressed in HCC and was closely associated with clinicopathological features, including gender, age, race, tumor grade, and stage. There was no correlation between genetic mutations and prognostic survival. Combining Cox proportional hazards regression model analyses, high-expressed predicted poor prognosis in HCC patients. Additionally, we screened the top three -related genes (, , and ), the 50 similar genes, and then constructed a 50-similar-gene protein-protein interaction network identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Metascape. target networks in HCC were identified in kinase, miRNA, and transcription factor networks, including the mitogen-activated protein kinase 1 (), miRNAs (), and transcription factors (GGAANCGGAANY_UNKNOWN) using GeneMANIA.

Conclusions: plays a crucial role in the gene-regulating network of HCC and may be a potential prognostic marker or therapeutic target for HCC patients.