Abstract
ObjectivesTo identify the key glycolysis-related genes (GRGs) in the occurrence and development of pancreatic ductal carcinoma (PDAC), and to construct a glycolysis-related gene model for predicting the prognosis of PDAC patients.MethodologyPancreatic ductal carcinoma (PDAC) data and that of normal individuals were downloaded from the TCGA database and Genotype-Tissue Expression database, respectively. GSEA analysis of glycolysis-related pathways was then performed on PDAC data to identify significantly enriched GRGs. The genes were combined with other patient’s clinical information and used to construct a glycolysis-related gene model using cox regression analysis. The model was further evaluated using data from the validation group. Mutations in the model genes were subsequently identified using the cBioPortal. In the same line, the expression levels of glycolysis related model genes in PDAC were analyzed and verified using immunohistochemical images. Model prediction for PDAC patients with different clinical characteristics was then done and the relationship between gene expression level, clinical stage and prognosis further discussed. Finally, a nomogram map of the predictive model was constructed to evaluate the prognosis of patients with PDAC.ResultsGSEA results of the training set revealed that genes in the training set were significantly related to glycolysis pathway and iconic glycolysis pathway. There were 108 differentially expressed GRGs. Among them, 29 GRGs were closely related to prognosis based on clinical survival time. Risk regression analysis further revealed that there were seven significantly expressed glycolysis related genes. The genes were subsequently used to construct a predictive model. The model had an AUC value of more than 0.85. It was also significantly correlated with survival time. Further expression analysis revealed that CDK1, DSC2, ERO1A, MET, PYGL, and SLC35A3 were highly expressed in PDAC and CHST12 was highly expressed in normal pancreatic tissues. These results were confirmed using immunohistochemistry images of normal and diseases cells. The model could effectively evaluate the prognosis of PDAC patients with different clinical characteristics.ConclusionThe constructed glycolysis-related gene model effectively predicts the occurrence and development of PDAC. As such, it can be used as a prognostic marker to diagnose patients with PDAC.
Highlights
Pancreatic ductal carcinoma (PDAC) is one of the most fatal malignant tumors
We use the glycolysis related genes (GRGs) model constructed in this study to predict the prognosis of PDAC samples, 11 clinical groups showed a close correlation with patients’ survival after grouping using the GRGs model. These findings demonstrated that the GRGs model had high accuracy and could better predict the prognosis of PDAC patients than the current generally applicable grading and staging methods
Our study creatively studied the relationship between glycolysis-related genes (GRGs) and the clinical risk of PDAC patients, and the constructed model can predict the prognosis of PDAC patients well
Summary
Pancreatic ductal carcinoma (PDAC) is one of the most fatal malignant tumors. It is ranks fourth in cancer related deaths in the United States. Most PDAC patients have metastases by the time they are diagnosed. Surgical resection is the main treatment method for early PDAC. The 5-year survival rate of PDAC patients undergoing surgical resection is only 20%. Combination chemotherapy such as FOLFIRINOX combination therapy that consists of folic acid, fluorouracil, irinotecan and oxaliplatin (Conroy et al, 2011), and combined use of gemcitabine and nab- paclitaxel (von Hoff et al, 2013) have become the preferred treatment option for patients with advanced PDAC
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