Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer.

Xin Zhao,Ziwei Wang,Ge Li,Jiaxuan Zou,Yi Lei,Syed Sameer Aga

doi:10.1155/2021/9656947

Abstract

Background Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. Results We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.

Highlights

Gastric cancer (GC) is one of the most common malignancies throughout the world
The results showed that GMPPA, GPC3, NUP50, VCAN, and TPST1 and GMPPA, GPC3, NUP50, and VCAN both reached the best result with the lowest Akaike Information Criterion (AIC) value of 1488.9 among all combinations (Supplementary Figure 1A)
In the early 20th century, German scientist Warburg discovered that when the cancer cells proliferate rapidly, glycolysis was the preferred metabolic pathway even there is an adequate supply of oxygen

Summary

Introduction

Gastric cancer (GC) is one of the most common malignancies throughout the world. the incidence of GC has been declined in recent year, GC remains one of the most aggressive malignant tumors that severely threaten human health [1, 2]. GC patients at the progressive stage usually have a low fiveyear overall survival (OS) due to recurrence and metastasis. Efforts should be made to look for useful biomarkers to evaluate the prognosis of GC patients and to identify potential high-risk GC patients. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients’ prognosis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. The signature consisting of four glycolysis-related genes could be used to forecast the GC patients’ prognosis

Methods

Results

Conclusion