Table_3.xlsx

Abstract

Background: Hepatocellular carcinoma(HCC) is one of the most prevalent and lethal cancers in the world. The microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients. Methods: The mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas(TCGA). Gene set enrichment analysis(GSEA) is used to distinguish the different genes in the hunger metabolism gene in liver cancer and adjacent tissues. GSEA was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. (Kaplan-Meier)KM and receiver operating characteristic(ROC) were used to assess the accuracy of the model. The model and relevant clinical information were used to construct the nomogram, protein expression was detected by western blot(WB), and transwell assay was used to evaluate the invasive and metastatic ability of the cells. Results: First, we used a univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through KEGG and GO. We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify the typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway as well as biological processes related to the model. The expression of EIF2S1 was increased by WB after starvation. Experiments showed that EIF2S1 plays an important role in the invasion and metastasis of liver cancer. Conclusions: The 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients, but its mechanism of action needs to be further proven by experiments.