Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China.
Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China.
J Gene Med. 2024 Jan;26(1):e3620. doi: 10.1002/jgm.3620. Epub 2023 Nov 16.
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.
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.
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 agents, and there were differences in functional enrichment pathways.
This unique signature based on glucose metabolism may potentially provide a basis for predicting patient prognosis, biological characteristics and more effective immunotherapy strategies for COAD.
结肠腺癌（COAD）的全球患病率和转移率都很高，治疗效果有限。虽然之前的研究主要探讨了基因表型的变化，但 COAD 的发病率仍未改变。代谢重编程是癌症研究和治疗的重要方面。本研究旨在基于葡萄糖代谢途径开发 COAD 的聚类和多基因风险预测模型，以评估患者的生存状况，并可能识别新的免疫治疗策略和相关治疗靶点。
从癌症基因组图谱（TCGA）和两个基因表达综合数据库（GEO）（GSE33113 和 GSE39582）中获取 COAD 特异性数据（包括临床病理信息和基因表达谱）。从 MSigDB 数据库中获取与葡萄糖代谢相关的基因集。使用基因集变异分析（GSVA）方法计算葡萄糖代谢途径的途径评分。使用 R 中的 hclust 函数（属于 Pheatmap 包的一部分）建立聚类系统。使用 MOVICS 软件评估鉴定出的聚类的突变特征，并使用 limma 软件筛选差异表达基因（DEGs）。使用最小绝对收缩和选择算子（LASSO）方法进行特征分析。使用生存曲线、生存接收者操作特征（ROC）曲线和多变量 Cox 回归分析来评估特征对预后预测的功效和准确性。使用来自癌症药物敏感性基因组学（GDSC）数据库的 pRRophetic 程序预测药物敏感性。
基于葡萄糖代谢，鉴定出四个 COAD 亚组（C1、C2、C3 和 C4），其中 C4 组的生存率较高。这四个聚类进一步分为新的 Clust2 系统（C1+C2+C3 和 C4）。共获得 2175 个差异表达基因（C1+C2+C3 与 C4 相比），其中鉴定出 139 个与预后相关的基因。基于包含 9 个基因的特征预测 1 年、3 年和 5 年生存率的 ROC 曲线显示曲线下面积大于 0.7。同时，该研究还发现该特征是 COAD 预后的重要预测因子，并相应评估了风险评分，高风险评分与预后较差相关。高危和低危组对免疫治疗和化疗药物的反应不同，功能富集途径也存在差异。
基于葡萄糖代谢的这种独特特征可能为预测 COAD 患者的预后、生物学特征和更有效的免疫治疗策略提供依据。
