Combining Single-Cell and Transcriptomic Data Revealed the Prognostic Significance of Glycolysis in Pancreatic Cancer - Chen, Liang; Lin, Yunhua; Wei, Wei; Wang, Yue; Li, Fangyue; Du, Wang; Yang, Zhonghua; Hu, Yiming; Ying, Xiaomei; Tang, Qikai; Xie, Jiaheng; Yu, Hongzhu (Frontiers in Genetics, 2022)
Background: The most fatal common solid malignancy, pancreatic cancer (PC), 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 suggest that glycolysis is important in the onset and progression of cancer, there are few publications on its impact in PC. Methods: To begin, the single sample gene set enrichment (ssGSEA) approach was used to quantify the glycolysis pathway enrichment fraction in PC patients and establish its prognostic significance. The genes most related with 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 quasi-time series analysis on the activated cell subtypes, and detected gene changes in the signature during cell development. Finally, we constructed a decision tree and a nomogram that could divide patients into different risk subtypes according to the signature score and their different clinical characteristics and assessed the prognosis of PC patients. Results: Glycolysis plays a risk 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. Survival analysis and multivariate cox analysis indicated this gene signature to be an independent prognostic factor in PC. Prognostic ROC curve analysis suggested a high accuracy of this gene signature in predicting patient prognosis in PC. 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. Decision tree analysis could effectively identify patients in different risk subgroups, and the nomograms clearly show the prognostic assessment of PC patients. Conclusion: Our study developed a glycolysis-related signature, which contributes to the risk subtypes assessment of patients with PC and to the individualized management of patients in the clinical setting.