Prognostic prediction and gene regulation network of EIF2S2 in hepatocellular carcinoma based on data mining

Abstract

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 (EIF2S2) 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 EIF2S2 in HCC using bioinformatics data.

Methods: We screened EIF2S2 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 EIF2S2 and patients' survival was made using the Kaplan-Meier curves and Cox proportional hazards regression model. Then, the influence of EIF2S2 gene mutations on the prognosis of patients was explored by cBioPortal. The protein-protein interaction network of 50 similar genes related to EIF2S2 was implemented by GEPIA2 and Metascape. The LinkedOmics database allowed us to carry out Gene Set Enrichment Analysis. Finally, we constructed the EIF2S2 kinase, miRNA, and transcription factor target networks using GeneMANIA.

Results: EIF2S2 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 EIF2S2 genetic mutations and prognostic survival. Combining Cox proportional hazards regression model analyses, high-expressed EIF2S2 predicted poor prognosis in HCC patients. Additionally, we screened the top three EIF2S2-related genes (PFDN4, HM13, and SNRPD1), 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. EIF2S2 target networks in HCC were identified in kinase, miRNA, and transcription factor networks, including the mitogen-activated protein kinase 1 (MAPK1), miRNAs (Mir-144), and transcription factors (GGAANCGGAANY_UNKNOWN) using GeneMANIA.

Conclusions: EIF2S2 plays a crucial role in the gene-regulating network of HCC and may be a potential prognostic marker or therapeutic target for HCC patients.

Keywords: Hepatocellular carcinoma (HCC); bioinformatics; eukaryotic translation initiation factor 2 subunit beta (EIF2S2); prediction; prognosis.