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J Cancer 2024; 15(10):3199-3214. doi:10.7150/jca.91687 This issue Cite

Research Paper

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis

Yang Xie^1#, Jun Li^1#, Qing Tao¹, Yonghui Wu¹, Zide Liu¹, Chunyan Zeng^1,2✉, Youxiang Chen^1,2✉

1. Department of Gastroenterology, digestive disease Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang China.
2. Jiangxi Clinical Research Center for Gastroenterology, Nanchang, Jiangxi, China.
# Contributed equally.

✉ Corresponding authors: Dr Chunyan Zeng and Dr Youxiang Chen, Department of Gastroenterology, Digestive Disease Hospital, the First Affiliated Hospital of Nanchang University, 17 Yongwaizheng Street, Nanchang330006, Jiangxi, China. Phone: +86-791-8869-2540; Fax: +86-791-8869-2540; Email: chenyx102edu.cn; zengcy896edu.cn. More

Abstract

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC.

Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes.

Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level.

Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Keywords: Glutamine metabolism-related genes, Colorectal cancer, Prognosis, Survival analysis, Immune cell infiltration.

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