Glucose metabolism‐based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma

• Published in: The journal of gene medicine 2024-01, Vol.26 (1), p.e3620-n/a
• Main Authors: Bai, Zilong, Yan, Chunyu, Nie, Yuanhua, Zeng, Qingnuo, Xu, Longwen, Wang, Shilong, Chang, Dongmin
• Format: Article
• Language: English
• Subjects: Adenocarcinoma - diagnosis; Adenocarcinoma - genetics; Adenocarcinoma - therapy; Carbohydrate Metabolism; colon adenocarcinoma (COAD); Colonic Neoplasms - diagnosis; Colonic Neoplasms - genetics; Colonic Neoplasms - therapy; Glucose; glucose metabolism; Humans; Immunotherapy; metabolic reprogramming; prognostic signature
• ISSN: 1099-498X; 1521-2254
• DOI: 10.1002/jgm.3620

Background The global prevalence and metastasis rates of colon adenocarcinoma (COAD) are high, and therapeutic success is limited. Although previous research has primarily explored changes in gene phenotypes, the incidence rate of COAD remains unchanged. Metabolic reprogramming is a crucial aspect of cancer research and therapy. The present study aims to develop cluster and polygenic risk prediction models for COAD based on glucose metabolism pathways to assess the survival status of patients and potentially identify novel immunotherapy strategies and related therapeutic targets. Methods COAD‐specific data (including clinicopathological information and gene expression profiles) were sourced from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE39582). Gene sets related to glucose metabolism were obtained from the MSigDB database. The Gene Set Variation Analysis (GSVA) method was utilized to calculate pathway scores for glucose metabolism. The hclust function in R, part of the Pheatmap package, was used to establish a clustering system. The mutation characteristics of identified clusters were assessed via MOVICS software, and differentially expressed genes (DEGs) were filtered using limma software. Signature analysis was performed using the least absolute shrinkage and selection operator (LASSO) method. Survival curves, survival receiver operating characteristic (ROC) curves and multivariate Cox regression were analyzed to assess the efficacy and accuracy of the signature for prognostic prediction. The pRRophetic program was employed to predict drug sensitivity, with data sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results Four COAD subgroups (i.e., C1, C2, C3 and C4) were identified based on glucose metabolism, with the C4 group having higher survival rates. These four clusters were bifurcated into a new Clust2 system (C1 + C2 + C3 and C4). In total, 2175 DEGs were obtained (C1 + C2 + C3 vs. C4), from which 139 prognosis‐related genes were identified. ROC curves predicting 1‐, 3‐ and 5‐year survival based on a signature containing nine genes showed an area under the curve greater than 0.7. Meanwhile, the study also found this feature to be an important predictor of prognosis in COAD and accordingly assessed the risk score, with higher risk scores being associated with a worse prognosis. The high‐risk and low‐risk groups responded differently to immunotherapy and chemotherapeutic age