Chen Liang, Lin Yunhua, Wei Wei, Wang Yue, Li Fangyue, Du Wang, Yang Zhonghua, Hu Yiming, Ying Xiaomei, Tang Qikai, Xie Jiaheng, Yu Hongzhu
Department of General Surgery, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, China.
The First Clinical Medical College, Guangxi Medical University, Nanning, China.
Front Genet. 2022 Jul 5;13:903783. doi: 10.3389/fgene.2022.903783. eCollection 2022.
Pancreatic cancer (PC), the most common fatal solid malignancy, has a very dismal prognosis. Clinical computerized tomography (CT) and pathological TNM staging are no longer sufficient for determining a patient's prognosis. Although numerous studies have suggested that glycolysis is important in the onset and progression of cancer, there are few publications on its impact on PC. To begin, the single-sample gene set enrichment analysis (ssGSEA) approach was used to quantify the glycolysis pathway enrichment fraction in PC patients and establish its prognostic significance. The genes most related to the glycolytic pathway were then identified using weighted gene co-expression network analysis (WGCNA). The glycolysis-associated prognostic signature in PC patients was then constructed using univariate Cox regression and lasso regression methods, which were validated in numerous external validation cohorts. Furthermore, we investigated the activation of the glycolysis pathway in PC cell subtypes at the single-cell level, performed a quasi-time series analysis on the activated cell subtypes and then detected gene changes in the signature during cell development. Finally, we constructed a decision tree and a nomogram that could divide the patients into different risk subtypes, according to the signature score and their different clinical characteristics and assessed the prognosis of PC patients. Glycolysis plays a risky role in PC patients. Our glycolysis-related signature could effectively discriminate the high-risk and low-risk patients in both the trained cohort and the independent externally validated cohort. The survival analysis and multivariate Cox analysis indicated this gene signature to be an independent prognostic factor in PC. The prognostic ROC curve analysis suggested a high accuracy of this gene signature in predicting the patient prognosis in PC. The single-cell analysis suggested that the glycolytic pathway may be more activated in epithelial cells and that the genes in the signature were also mainly expressed in epithelial cells. The decision tree analysis could effectively identify patients in different risk subgroups, and the nomograms clearly show the prognostic assessment of PC patients. Our study developed a glycolysis-related signature, which contributes to the risk subtype assessment of patients with PC and to the individualized management of patients in the clinical setting.
胰腺癌（PC）是最常见的致命实体恶性肿瘤，预后极差。临床计算机断层扫描（CT）和病理TNM分期已不足以确定患者的预后。尽管众多研究表明糖酵解在癌症的发生和发展中起重要作用，但关于其对胰腺癌影响的出版物却很少。首先，采用单样本基因集富集分析（ssGSEA）方法量化胰腺癌患者的糖酵解途径富集分数，并确定其预后意义。然后使用加权基因共表达网络分析（WGCNA）确定与糖酵解途径最相关的基因。接着，使用单变量Cox回归和套索回归方法构建胰腺癌患者的糖酵解相关预后特征，并在多个外部验证队列中进行验证。此外，我们在单细胞水平研究了胰腺癌细胞亚型中糖酵解途径的激活情况，对激活的细胞亚型进行了拟时间序列分析，然后检测了细胞发育过程中特征基因的变化。最后，我们构建了一个决策树和一个列线图，根据特征分数及其不同的临床特征将患者分为不同的风险亚型，并评估胰腺癌患者的预后。糖酵解在胰腺癌患者中起着风险作用。我们的糖酵解相关特征能够有效区分训练队列和独立外部验证队列中的高风险和低风险患者。生存分析和多变量Cox分析表明，该基因特征是胰腺癌的独立预后因素。预后ROC曲线分析表明，该基因特征在预测胰腺癌患者预后方面具有较高的准确性。单细胞分析表明，糖酵解途径可能在上皮细胞中更活跃，且特征基因也主要在上皮细胞中表达。决策树分析能够有效识别不同风险亚组的患者，列线图清楚地显示了胰腺癌患者的预后评估。我们的研究开发了一种糖酵解相关特征，有助于胰腺癌患者的风险亚型评估和临床环境中患者的个体化管理。
