Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2020-09-22 eCollection Date: 2020-01-01 DOI:10.1177/1176934320951571

Jia Yan, Ming Shu, Xiang Li, Hua Yu, Shuhuai Chen, Shujie Xie

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Abstract

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.

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基于预后评分的临床因素和代谢相关生物标记物预测肝细胞癌的进展情况

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。本研究旨在通过建立新的预后评分（PS）模型，确定具有预后价值的代谢相关生物标志物。研究人员分析了来自TCGA和EBIArray数据库的转录组图谱，以确定与正常样本相比，HCC肿瘤样本中的差异表达基因（DEGs）。筛选了 DEGs 与代谢相关基因（关键基因）之间的重叠基因，并对其进行了功能分析。构建了一个新的 PS 模型，以确定最佳特征基因。通过 Cox 回归分析确定了与预后相关的独立临床因素。建立了提名图模型来估计临床因素的可预测性。最后，从人类蛋白质图谱（HPA）中探索了不同癌症组织和细胞类型中关键基因的蛋白质表达。我们共筛选出 305 个重叠基因（差异表达的代谢相关基因）。这些基因主要涉及 "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，我们还利用 PS 模型筛选出了 10 个最佳 DEGs（CYP2C9、CYP3A4 和 TKT 等）。病理分期（P < .001，HR：1.512 [1.219-1.875]）和 PS 状态（P

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.

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