Background: Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract. Its clinical symptoms are obscure and atypical. It is difficult to diagnose and treat. Tumor cells mainly obtain energy through glycolysis to promote their growth. Inhibiting glycolysis can inhibit proliferation and kill tumor cells. Methods: Using bioinformatics method, we investigate the relationships between glycolysis-related genes and PC tumor samples' epidemiologic information comprehensively. Results: Different expression levels of 27 genes were identified. Using bioinformatics methods, we plotted two subgroup curves based on glycolysis-related gene expression level. Potential predictive genes were screened and their prognostic values were analyzed. Survival among high-risk group and low risk group had significant difference. Receiver operating characteristic (ROC) curve analysis indicated that area under curve (AUC) of 10 genes was greater than 0.8. These genes could be used for clinical diagnosis and prediction for PC. Two potential predictors [Kinesin Family Member 20A (KIF20A) and MET Proto-Oncogene, Receptor Tyrosine Kinase (MET)] that met the independent predictive value were selected. In univariate analysis, we screened out 3 regulators MET, protein kinase CAMP-activated catalytic subunit alpha (PRKACA) and KIF20A. According to the 3 regulatory factors, the prognostic signals of PC were constructed, by which the samples with good prognosis and poor prognosis can be clearly distinguished independently of potential confounding factors. Conclusions: Our results indicate that for PC, glycolysis -related genes could be promising therapeutic targets or prognostic indicators. 2022 Journal of Gastrointestinal Oncology. All rights reserved.
Background: Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract. Its clinical symptoms are obscure and atypical. It is difficult to diagnose and treat. Tumor cells mainly obtain energy through glycolysis to promote their growth. Inhibiting glycolysis can inhibit proliferation and kill tumor cells. Methods: Using bioinformatics method, we investigate the relationships between glycolysis-related genes and PC tumor samples' epidemiologic information comprehensively. Results: Different expression levels of 27 genes were identified. Using bioinformatics methods, we plotted two subgroup curves based on glycolysis-related gene expression level. Potential predictive genes were screened and their prognostic values were analyzed. Survival among high-risk group and low risk group had significant difference. Receiver operating characteristic (ROC) curve analysis indicated that area under curve (AUC) of 10 genes was greater than 0.8. These genes could be used for clinical diagnosis and prediction for PC. Two potential predictors [Kinesin Family Member 20A (KIF20A) and MET Proto-Oncogene, Receptor Tyrosine Kinase (MET)] that met the independent predictive value were selected. In univariate analysis, we screened out 3 regulators MET, protein kinase CAMP-activated catalytic subunit alpha (PRKACA) and KIF20A. According to the 3 regulatory factors, the prognostic signals of PC were constructed, by which the samples with good prognosis and poor prognosis can be clearly distinguished independently of potential confounding factors. Conclusions: Our results indicate that for PC, glycolysis -related genes could be promising therapeutic targets or prognostic indicators. 2022 Journal of Gastrointestinal Oncology. All rights reserved.
Entities:
Keywords:
The Cancer Genome Atlas (TCGA); bioinformatics; glycolysis; pancreatic cancer (PC); prognostic signature
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