Telomere Maintenance Characteristics Predict Prognosis and Therapeutic Response in Colorectal Cancer.
1/5 보강
[INTRODUCTION] The link between telomere length and Colorectal Cancer (CRC) risk and survival has been established.
APA
Ma Y, Fang X, Li P (2026). Telomere Maintenance Characteristics Predict Prognosis and Therapeutic Response in Colorectal Cancer.. Current topics in medicinal chemistry, 26(3), 282-294. https://doi.org/10.2174/0115680266397024250710105241
MLA
Ma Y, et al.. "Telomere Maintenance Characteristics Predict Prognosis and Therapeutic Response in Colorectal Cancer.." Current topics in medicinal chemistry, vol. 26, no. 3, 2026, pp. 282-294.
PMID
40671231
Abstract
[INTRODUCTION] The link between telomere length and Colorectal Cancer (CRC) risk and survival has been established. This study aims to investigate Telomere Maintenance-related Genes (TMGs) for predicting immunotherapy response and prognosis in CRC patients.
[METHODS] In this study, gene expression data and clinical information of CRC patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and TMG-related scores were calculated for the samples. Subsequently, Weighted Gene Co- Expression Network Analysis (WGCNA) was used to identify gene modules that were highly correlated with the TMG score and intersected with differentially expressed genes to screen for potential functionally relevant candidate genes. The key genes significantly associated with prognosis were further analyzed using Cox regression analysis, from which the key genes were identified, and a risk score model was constructed. Finally, the survival prediction ability of the model was evaluated across multiple cohorts, and differences in immune cell infiltration characteristics and drug sensitivity were analyzed within different risk groups.
[RESULTS] A higher TMG score was noticed in CRC, and the TMG score was negatively correlated with the StromalScore, ImmuneScore, and ESTIMATEScore. Gene modules significantly associated with the TMG score were identified using WGCNA. Two key genes, CDC25C and USP39, which were closely associated with prognosis, were screened through differential expression analysis, and a risk score model was constructed. The model showed good survival prediction in both TCGA and GSE17537 independent cohorts. The scores of activated CD4 T cells, Type 17 T helper cells, Type 2 T helper cells, and neutrophils in high-risk patients were lower, while that of macrophages was higher in high-risk patients. Additionally, a negative correlation was observed between the risk score and the IC50 values of most drugs, as well as the enriched pathways of patients at high risk, which included epithelial-mesenchymal transition, angiogenesis, and myogenesis.
[DISCUSSION] This study unveiled a TMG-related signature that predicts prognosis and immunotherapy in CRC. Based on the 2 prognostically relevant genes CDC25C and USP39, a reliable risk score model was established for the prognostic prediction, and the correlation between the drug sensitivity and the risk score was also explored.
[CONCLUSION] This study reveals the significant value of TMGs in CRC prognostic assessment and immunotherapy response prediction, providing a new molecular basis for the development of individualized treatment strategies.
[METHODS] In this study, gene expression data and clinical information of CRC patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and TMG-related scores were calculated for the samples. Subsequently, Weighted Gene Co- Expression Network Analysis (WGCNA) was used to identify gene modules that were highly correlated with the TMG score and intersected with differentially expressed genes to screen for potential functionally relevant candidate genes. The key genes significantly associated with prognosis were further analyzed using Cox regression analysis, from which the key genes were identified, and a risk score model was constructed. Finally, the survival prediction ability of the model was evaluated across multiple cohorts, and differences in immune cell infiltration characteristics and drug sensitivity were analyzed within different risk groups.
[RESULTS] A higher TMG score was noticed in CRC, and the TMG score was negatively correlated with the StromalScore, ImmuneScore, and ESTIMATEScore. Gene modules significantly associated with the TMG score were identified using WGCNA. Two key genes, CDC25C and USP39, which were closely associated with prognosis, were screened through differential expression analysis, and a risk score model was constructed. The model showed good survival prediction in both TCGA and GSE17537 independent cohorts. The scores of activated CD4 T cells, Type 17 T helper cells, Type 2 T helper cells, and neutrophils in high-risk patients were lower, while that of macrophages was higher in high-risk patients. Additionally, a negative correlation was observed between the risk score and the IC50 values of most drugs, as well as the enriched pathways of patients at high risk, which included epithelial-mesenchymal transition, angiogenesis, and myogenesis.
[DISCUSSION] This study unveiled a TMG-related signature that predicts prognosis and immunotherapy in CRC. Based on the 2 prognostically relevant genes CDC25C and USP39, a reliable risk score model was established for the prognostic prediction, and the correlation between the drug sensitivity and the risk score was also explored.
[CONCLUSION] This study reveals the significant value of TMGs in CRC prognostic assessment and immunotherapy response prediction, providing a new molecular basis for the development of individualized treatment strategies.
MeSH Terms
Humans; Colorectal Neoplasms; Prognosis; Telomere; Antineoplastic Agents
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