Abstract
BACKGROUND: RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed. METHODS: RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database. RESULTS: We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy. CONCLUSIONS: Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.