Identification and validation of glucose metabolism-related gene signature in endometrial cancer
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Updated: Aug 12, 2026
Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
Published on: March 17, 2016
Juan Jiang1, Nan Xia2, Mei Yang3
1Department of Obstetrics and Gynecology, Jingjiang People's Hospital Affiliated to Yangzhou University, Taizhou, China.
Background:
Metabolic syndrome associated with glucose metabolism plays a pivotal role in tumorigenesis, potentially elevating the risk of endometrial cancer (EC). This study sought to establish a glucose metabolism-related gene (GMRG) signature linked to EC.
Methods:
Differential analysis was conducted to identify differentially expressed genes (DEGs) between EC and normal samples from the TCGA-EC dataset. Glucose metabolism-related DEGs (GMR-DEGs) were then derived by intersecting these DEGs with GMRGs. A prognostic signature for EC was developed through the Least Absolute Shrinkage and Selection Operator (LASSO) regression and univariate Cox analysis. Additionally, immune profiling and immunotherapy responsiveness were evaluated across two distinct risk subgroups, accompanied by a single-cell analysis of prognostic genes. The expression levels of these prognostic genes were quantified at both transcriptional and translational stages using reverse transcription quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) in clinical samples. Furthermore, the functional significance of key genes was explored through in vitro assays.
Results:
2,912 DEGs and 202 GMR-DEGs were identified between the EC and normal groups. Subsequently, six prognostic genes were derived, including ASRGL1, SLC38A3, SLC2A1, ALDH1B1, GAD1, and GLYATL1. EC patients were classified into high and low-risk subgroups based on the six genes. Independent prognostic analysis indicated that risk score and disease stage were significant independent prognostic factors. Single-cell analysis revealed that the six prognostic genes were highly expressed in ciliated and epithelial cells. Immune cell infiltration was generally lower in the high-risk group, where tumor purity was elevated. The expression levels of SLC38A3, SLC2A1, and ASRGL1 are higher in tumor samples by RT-qPCR, with IHC confirming increased SLC38A3 expression. Finally, SLC38A3 may function as oncogenes in EC, as revealed by the results of in vitro experiments.
Conclusions:
In this study, we developed six novel prognostic genes in EC based on glycolysis, and corresponding prognostic models were developed. Notably, we identified SLC38A3 as the key gene, which offers valuable insights for further research into EC.
