Development and validation of a novel glycolysis-related risk signature for predicting survival in pancreatic adenocarcinoma.

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer-related deaths worldwide. Through data mining, an increasing number of biomarkers have been identified to predict the survival of patients with PAAD. However, the ability of single gene biomarkers to predict patient survival is still insufficient. This study aimed to develop a novel risk signature for predicting the survival of patients with PAAD.

METHODS

mRNA expression profiling was performed for a large PAAD cohort (n = 177) identified using The Cancer Genome Atlas database (TCGA). Gene set enrichment analysis (GSEA) was performed to detect whether the gene sets showed significant differences between PAAD and adjacent normal tissues. Univariate Cox regression was used to analyze and identify genes related to overall survival (OS). Multivariate Cox regression was subsequently used to confirm the prognostic genes and obtain the coefficients. By analyzing the expression level of selected genes weighted by their coefficients through linearly combining, we constructed a risk score formula for prognostic prediction. The three-mRNA signature for survival prediction was validated using the Kaplan-Meier method.

RESULTS

We demonstrated that a set of three genes (KIF20A, CHST2, and MET) were significantly associated with OS. Based on this three-gene signature, 177 PAAD patients were classified into high-risk and low-risk groups using the median risk score as the cut-off value. We also validated the reliability of this three-gene signature in the GSE28735 dataset from the Gene Expression Omnibus (GEO) database. Additionally, multivariate Cox regression analysis revealed that the three-gene signature had an independent prognostic value.

CONCLUSION

To the best of our knowledge, this is the first study to develop a glycolysis-related risk signature for predicting the survival of patients with pancreatic adenocarcinoma. Our findings provide insight into the identification of PAAD patients with poor prognosis. We also identified novel therapeutic targets for this disease.