Hepatocellular Carcinoma: An Analysis of the Expression Status of Stress Granules

Abstract

Abstract Hepatocellular carcinoma (HCC) is a commonly occurring cancer distinguished by a bleak prognosis. Stress particles can protect cancer cells from apoptosis. This investigation aimed to analyze the impacts of stress granule genes on overall survival(OS), survival time, and prognosis in HCC. The combined TCGA-LIHC, GSE25097, and GSE36376 datasets were utilized to obtain genetic and clinical information. Optimal hub gene numbers and corresponding coefficients were determined using the LASSO model approach, and genes for constructing risk scores and corresponding correlation coefficients were calculated according to multivariate COX regression, respectively. The clusterProfiler R package was utilized to conduct an enrichment analysis of differentially expressed genes (DEGs), which utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases to detect biological processes that exhibit significant enrichment. Protein-protein interaction networks (PPI) according to stress granule genes that show differential expression within the high-risk and low-risk groups in the combined datasets of TCGA-LIHC, and with the use of the STRING website, the GSE25097 and GSE36376 datasets were constructed, and the data obtained was analyzed and visualized using the Cytoscape software. The prognostic model's receiver operating characteristic (ROC) curve was produced and plotted utilizing the timeROC software package. Nomogram models were constructed to predict the outcomes at 1, 3, and 5-year overall survival(OS) prognostications with good prediction accuracy. We identified seven stress granule genes (DDX1、DKC1、BICC1、HNRNPUL1、CNOT6、DYRK3、CCDC124)having a prognostic significance and developed a risk score model. In accordance with the findings obtained from the ROC analysis, the risk score model was able to anticipate 1-, 3-accurately, and 5-year OS in individuals suffering from HCC. The findings of KM analysis indicated that the group with a high risk exhibited significantly reduced overall survival (OS) in comparison with those of the low-risk group(p < 0.001). The nomogram model's findings indicate a significant enhancement in the accuracy of OS prediction for individuals with HCC in the TCGA-HCC cohort. GO and Gene Set EnrichmentAnalysis(GSEA) analysis suggested that these stress granules might be involved in the cell cycle, RNA editing, and other biological processes. Based on the impact of stress granule genes on HCC prognosis, it is possible that in the future, it will be used as a biomarker as well as a unique therapeutic target for the identification and treatment of HCC.