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Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer

BACKGROUND: The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. METHODS: We calculated immune an...

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Detalles Bibliográficos
Autores principales: Chen, Wenzheng, Huang, Jianfeng, Xiong, Jianbo, Fu, Pengcheng, Chen, Changyu, Liu, Yi, Li, Zhengrong, Jie, Zhigang, Cao, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420973/
https://www.ncbi.nlm.nih.gov/pubmed/34497681
http://dx.doi.org/10.1155/2021/6290261
Descripción
Sumario:BACKGROUND: The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. METHODS: We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. RESULTS: High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group. CONCLUSIONS: We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.