Research Paper Advance Articles

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway

Qi-Ying Yu1, *, , Zhi-Wen Wang3, *, , Meng-Ying Zhou1, *, , Shang-Fu Li2, , Xing-Hua Liao1, ,

  • 1 Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Hubei 430081, China
  • 2 Yueyang People’s Hospital, Yueyang Hospital Affiliated to Hunan Normal University Neoplasm Ward 1, Yueyang 414000, Hunan, China
  • 3 Yueyang Vocational and Technical College, Yueyang Key Laboratory of Chronic Noncommunicable Diseases, Yueyang 414000, Hunan, China
* Equal contribution

Received: June 9, 2022       Accepted: October 27, 2022       Published: November 8, 2022      

https://doi.org/10.18632/aging.204373
How to Cite

Copyright: © 2022 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Gastric cancer remains a malignant disease of the digestive tract with high mortality and morbidity worldwide. However, due to its complex pathological mechanisms and lack of effective clinical therapies, the survival rate of patients after receiving treatment is not satisfactory. A increasing number of studies have focused on cancer stem cells and their regulatory properties. In this study, we first constructed a co-expression network based on the WGCNA algorithm to identify modules with different degrees of association with tumor stemness indices. After selecting the most positively correlated modules of the stemness index, we performed a consensus clustering analysis on gastric cancer samples and constructed the co-expression network again. We then selected the modules of interest and applied univariate COX regression analysis to the genes in this module for preliminary screening. The results of the screening were then used in LASSO regression analysis to construct a risk prognostic model and subsequently a sixteen-gene model was obtained. Finally, after verifying the accuracy of the module and screening for risk genes, we identified MAGE-A3 as the final study subject. We then performed in vivo and in vitro experiments to verify its effect on tumor stemness and tumour proliferation. Our data supports that MAGE-A3 is a tumor stemness regulator and a potent prognostic biomarker which can help the prediction and treatment of gastric cancer patients.

Abbreviations

OS: overall survival; mRNAsi: mRNA expression based-stemness index; GS: Gene significance; WGCNA: Weighted gene co-expression network analysis; DEG: Differentially expressed genes; GEO: Gene Expression Omnibus.