Table_2_A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma.xlsx

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, 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.
MethodsThe 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) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (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 model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.
ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (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 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. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.
ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

背景：肝细胞癌（Hepatocellular carcinoma, HCC）是全球范围内最常见且致死率最高的恶性肿瘤之一。值得关注的是，肿瘤饥饿微环境与癌症恶性程度密切相关。本研究构建了饥饿相关基因签名，用于预测肝癌患者的预后。
方法：本研究从国际癌症基因组联盟（International Cancer Genome Consortium, ICGC）及癌症基因组图谱（The Cancer Genome Atlas, TCGA）获取肝癌患者的mRNA表达矩阵及对应临床信息。采用基因集富集分析（Gene Set Enrichment Analysis, GSEA）区分肝癌组织与癌旁组织中饥饿代谢相关基因的表达差异；利用GSEA识别高、低风险样本间的生物学差异。通过单因素与多因素分析构建饥饿相关基因的预后模型，采用Kaplan-Meier（KM）法及受试者工作特征（receiver-operating characteristic, ROC）曲线评估模型的预测准确性。结合该预后模型与相关临床信息构建列线图；采用蛋白质印迹（western blot, WB）检测蛋白表达水平，通过Transwell实验评估细胞的侵袭与转移能力。
结果：首先，本研究通过单因素分析筛选出35个预后相关基因，经京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）及基因本体（Gene Ontology, GO）富集分析证实，这些基因均与饥饿代谢过程相关。随后通过多因素分析构建了包含9个基因的预后模型。依据风险评分的中位数将样本划分为高风险组与低风险组，KM分析结果显示两组患者的预后存在显著差异，高风险组患者的预后更差。我们在验证数据集中进一步验证了该9基因mRNA签名的预后预测效能。通过GSEA分析识别出与该9基因相关的典型通路及生物学过程，包括细胞周期、缺氧应答、p53通路及PI3K/AKT/mTOR通路，同时明确了与该模型相关的生物学过程。蛋白质印迹实验结果证实，饥饿处理后EIF2S1的表达水平显著升高，综上表明EIF2S1在肝癌细胞的侵袭与转移过程中发挥重要作用。
结论：本研究构建的9基因mRNA签名可作为精准的预后预测工具，用于肝癌患者的预后评估，但其具体作用机制仍需进一步深入研究。