Glycolysis Related Prognostic Signature Were Constructed And Validated In Pancreatic Cancer

Background: Pancreatic cancer（PC）is the most lethal common solid malignancy with a very poor prognosis as well as a low 5-year survival rate for patients.The incidence of pancreatic cancer is increasing year by year,not only in China but also worldwide,which is a serious threat to people’s health.Pancreatic cancer is a common tumor of the digestive tract and has a very poor prognosis.The main treatment means nowadays is still mainly surgery,supplemented by radiotherapy and chemotherapy,but its early diagnosis is difficult and often the best time for surgery is missed when it is detected.Nowadays,clinical CT and pathological TNM staging cannot effectively assess the prognosis of patients.Numerous literatures show that glycolysis plays a crucial role in the occurrence and development of cancer.In a variety of cancers,high glycolysis promotes tumor proliferation,invasive metastasis,and angiogenesis.Based on the investigation of the mechanism of glycolysis,a number of inhibitory enzymes have been developed clinically by inhibiting the glycolytic process,and some results have been achieved,but there are few reports on its role in PC.Objective: To investigate the role and prognostic value of glycolysis in pancreatic cancer,and to construct a prognostic model based on glycolysis-related genes to assess the risk subgroups of pancreatic cancer patients and to guide some clinical treatment decisions,so as to provide a theoretical basis for clinical management and subsequent treatment of pancreatic cancer patients.Methods: Firstly,single sample gene set enrichment（ss GSEA）method was performed to quantifie the enrichment fraction of glycolysis pathways in patients with PC and determined its prognostic impact in patients with PC.Subsequently,Weighted gene co-expression network Analysis（WGCNA）was used to identify the genes most associated with the glycolytic pathway.Then univariate cox regression and LASSO regression methods were used to construct a glycolysis-associated prognosis signature in PC patients,which was validated in multiple external validation cohorts.Furthermore,we further explored the activation of the glycolysis pathway in PC cell subtypes at the single-cell sequencing level,performed quasi-time series analysis on the activated cell subtypes,and observed the changes of genes during cell development in the signature.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.Moreover,we examined the expression of key genes in the model by q RT-PCR in pancreatic cancer and normal specimens.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.Moreover,q RT-PCR results showed that all genes in the model were highly expressed in pancreatic cancer compared to normal tissue samples.