Yan Jia, Shu Ming, Li Xiang, Yu Hua, Chen Shuhuai, Xie Shujie
Department of Hepatobiliary Pancreatic Surgery, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
Evol Bioinform Online. 2020 Sep 22;16:1176934320951571. doi: 10.1177/1176934320951571. eCollection 2020.
Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in "oxidation reduction," "steroid hormone biosynthesis," "fatty acid metabolic process," and "linoleic acid metabolism." Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.
肝细胞癌（HCC）是一种常见的恶性肿瘤，占原发性肝癌的90%以上。本研究旨在通过开发新的预后评分（PS）模型来识别具有预后价值的代谢相关生物标志物。分析来自TCGA和EBIArray数据库的转录组谱，以识别HCC肿瘤样本与正常样本中差异表达的基因（DEG）。筛选DEG与代谢相关基因（关键基因）之间的重叠基因并进行功能分析。构建了一个新的PS模型以识别最佳特征基因。进行Cox回归分析以识别与预后相关的独立临床因素。构建列线图模型以评估临床因素的预测能力。最后，在人类蛋白质图谱（HPA）的不同癌组织和细胞类型中探索关键基因的蛋白质表达。我们总共筛选出305个重叠基因（差异表达的代谢相关基因）。这些基因主要参与“氧化还原”、“类固醇激素生物合成”、“脂肪酸代谢过程”和“亚油酸代谢”。此外，我们使用PS模型筛选出十个最佳DEG（如CYP2C9、CYP3A4和TKT等）。通过Cox回归分析，病理分期（P <.001，HR：1.512 [1.219 - 1.875]）和PS状态（P <.001，HR：2.259 [1.522 - 3.354]）这两个临床因素是独立的预后预测指标。列线图模型显示总生存时间的预测概率较高，AUC值为0.837。7种蛋白质的表达状态在正常或不同肿瘤组织（如肝癌和胃癌样本）中经常发生改变。我们基于PS模型确定了几种用于HCC预后预测的代谢相关生物标志物。病理分期和PS状态（高/低风险）这两个临床因素是独立的预后预测指标。本研究中描述的预后预测模型是一种用于识别新型生物标志物的有用且稳定的方法。
