Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Aging (Albany NY). 2021 Apr 26;13(9):13318-13332. doi: 10.18632/aging.203021.
Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. Currently, recent risk stratification has only focused on liver function and tumor characteristics. Thus, the purpose of this study was to develop a prognostic model based on genes involved in aerobic respiration. Matched tumor and normal tissues from TCGA and ICGC cohorts were analyzed to identify 15 overlapping differential expressed genes. Cox univariate analysis of the 15 genes in the TCGA cohort revealed they were all associated with disease-specific survival (DSS) in HCC patients. Using LASSO estimation and the optimal value for penalization coefficient lambda 12 genes were selected for the prognostic model, and then HCC patients in the TCGA cohort were dichotomized into low-risk and high-risk groups. Univariate and multivariate Cox analysis demonstrated patients in low-risk group had better survival. Validation of the risk score model with the ICGC cohort produces results consistent with those of the TCGA cohort. In conclusion, this study developed and validated a prognostic model of HCC through a comprehensive analysis of genes involved in aerobic respiration. This model may help develop personalized treatments for patients with HCC.
肝细胞癌（HCC）是全球癌症相关死亡的第二大主要原因。目前，最近的风险分层仅集中在肝功能和肿瘤特征上。因此，本研究的目的是开发一种基于涉及有氧呼吸的基因的预后模型。分析 TCGA 和 ICGC 队列中的配对肿瘤和正常组织，以确定 15 个重叠的差异表达基因。TCGA 队列中 15 个基因的 Cox 单因素分析表明，它们都与 HCC 患者的疾病特异性生存（DSS）相关。使用 LASSO 估计和最优惩罚系数 lambda 值选择 12 个基因用于预后模型，然后将 TCGA 队列中的 HCC 患者分为低风险和高风险组。单因素和多因素 Cox 分析表明，低风险组的患者具有更好的生存。用 ICGC 队列验证风险评分模型的结果与 TCGA 队列的结果一致。总之，本研究通过对涉及有氧呼吸的基因进行全面分析，开发并验证了 HCC 的预后模型。该模型可能有助于为 HCC 患者制定个性化治疗方案。
