Department of Liver Surgery, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China.
Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University & Research Unit of Liver Transplantation and Transplant Immunology, Chinese Academy of Medical Sciences, Nanjing, China.
J Cell Mol Med. 2023 Apr;27(7):1006-1020. doi: 10.1111/jcmm.17718. Epub 2023 Mar 15.
Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.
肝细胞癌（HCC）是全球最常见的恶性肿瘤之一。鉴于肿瘤的代谢重编程是一个关键标志，因此有几项研究已经证明了其在恶性肿瘤的诊断和监测中的价值。本研究旨在确定一组与代谢相关的基因，以构建 HCC 预后预测模型。本分析纳入了来自公共数据集的多个 HCC 病例队列（466 例）。（GEO 队列）在确定与预后相关的一系列代谢相关基因后，通过 LASSO-Cox 和 LASSO-pcvl 算法制定了基于代谢相关基因的风险评分。根据风险评分，将患者分为低风险和高风险组，并进行进一步的分析和验证。结果表明，在 GEO 队列中，高风险患者的 5 年总生存率（OS）明显低于低风险患者（30.0% vs. 57.8%；风险比[HR]，0.411；95%置信区间[95%CI]，0.302-0.651；p<0.001）。这一观察结果在外部 TCGA-LIHC 队列中得到了证实（34.5% vs. 54.4%；HR 0.452；95%CI，0.299-0.681；p<0.001）。为了提高模型的预测能力，将风险评分、年龄、性别和肿瘤分期整合到一个列线图中。根据受试者工作特征曲线和决策曲线分析的结果，列线图评分具有优于传统因素的预测能力，这表明风险评分与临床病理特征相结合能够对 OS 进行稳健预测，并改善 HCC 患者的个体化临床决策。总之，本研究确定了与 OS 相关的代谢基因，并开发了一种基于代谢的 HCC 预测模型。通过一系列生物信息学和统计分析，验证了该模型的预测能力。
