Table1_The construction and analysis of tricarboxylic acid cycle related prognostic model for cervical cancer.XLS

Introduction: Cervical cancer (CC) is the fourth most common malignant tumor in term of in incidence and mortality among women worldwide. The tricarboxylic acid (TCA) cycle is an important hub of energy metabolism, networking one-carbon metabolism, fatty acyl metabolism and glycolysis. It can be seen that the reprogramming of cell metabolism including TCA cycle plays an indispensable role in tumorigenesis and development. We aimed to identify genes related to the TCA cycle as prognostic markers in CC.
Methods: Firstly, we performed the differential expressed analysis the gene expression profiles associated with TCA cycle obtained from The Cancer Genome Atlas (TCGA) database. Differential gene list was generated and cluster analysis was performed using genes with detected fold changes >1.5. Based on the subclusters of CC, we analysed the relationship between different clusters and clinical information. Next, Cox univariate and multivariate regression analysis were used to screen genes with prognostic characteristics, and risk scores were calculated according to the genes with prognostic characteristics. Additionally, we analyzed the correlation between the predictive signature and the treatment response of CC patients. Finally, we detected the expression of ench prognostic gene in clinical CC samples by quantitative polymerase chain reaction (RT-qPCR).
Results: We constructed a prognostic model consist of seven TCA cycle associated gene (ACSL1, ALDOA, FOXK2, GPI, MDH1B, MDH2, and MTHFD1). Patients with CC were separated into two groups according to median risk score, and high-risk group had a worse prognosis compared to the low-risk group. High risk group had lower level of sensitivity to the conventional chemotherapy drugs including cisplatin, paclitaxel, sunitinib and docetaxel. The expression of ench prognostic signature in clinical CC samples was verified by qRT-PCR.
Conclusion: There are several differentially expressed genes (DEGs) related to TCA cycle in CC. The risk score model based on these genes can effectively predict the prognosis of patients and provide tumor markers for predicting the prognosis of CC.

引言：宫颈癌（Cervical cancer, CC）是全球女性中发病率与死亡率均位列第四的常见恶性肿瘤。三羧酸循环（tricarboxylic acid cycle, TCA cycle）是能量代谢的核心枢纽，串联了一碳代谢、脂酰代谢与糖酵解通路。可见，包括TCA循环在内的细胞代谢重编程在肿瘤发生与发展进程中发挥着不可或缺的作用。本研究旨在筛选与TCA循环相关的基因，将其作为宫颈癌的预后标志物。
方法：首先，我们对从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中获取的TCA循环相关基因表达谱进行差异表达分析，生成差异基因集，并以折叠变化>1.5的基因进行聚类分析。基于宫颈癌样本的亚群分类，我们分析了不同亚群与临床信息之间的关联。随后，采用Cox单因素与多因素回归分析筛选具有预后特征的基因，并基于这些基因计算风险评分。此外，我们还分析了该预测特征与宫颈癌患者治疗应答之间的相关性。最终，我们通过定量聚合酶链式反应（quantitative polymerase chain reaction, RT-qPCR）检测了各预后基因在临床宫颈癌样本中的表达水平。
结果：本研究构建了包含7个TCA循环相关基因的预后模型，分别为ACSL1、ALDOA、FOXK2、GPI、MDH1B、MDH2与MTHFD1。依据风险评分的中位数将宫颈癌患者分为两组，高风险组患者的预后较低风险组更差。高风险组患者对顺铂、紫杉醇、舒尼替尼与多西他赛等常规化疗药物的敏感性更低。我们通过qRT-PCR验证了各预后特征基因在临床宫颈癌样本中的表达情况。
结论：宫颈癌组织中存在多个与TCA循环相关的差异表达基因（differentially expressed genes, DEGs）。基于这些基因构建的风险评分模型可有效预测患者的预后，为宫颈癌的预后评估提供潜在的肿瘤标志物。