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Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer

Background: Cancer-associated fibroblasts (CAFs) play an important role in the tumorigenesis, immunosuppression and metastasis of colorectal cancer (CRC), and can predict poor prognosis in patients with CRC. The present study aimed to construct a CAFs-related prognostic signature for CRC. Methods: T...

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Autores principales: Zhang, Lei, Xu, Chao, Wang, Si-Han, Ge, Qin-Wen, Wang, Xiao-Wei, Xiao, Pan, Yao, Qing-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732269/
https://www.ncbi.nlm.nih.gov/pubmed/36506313
http://dx.doi.org/10.3389/fgene.2022.1054152
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author Zhang, Lei
Xu, Chao
Wang, Si-Han
Ge, Qin-Wen
Wang, Xiao-Wei
Xiao, Pan
Yao, Qing-Hua
author_facet Zhang, Lei
Xu, Chao
Wang, Si-Han
Ge, Qin-Wen
Wang, Xiao-Wei
Xiao, Pan
Yao, Qing-Hua
author_sort Zhang, Lei
collection PubMed
description Background: Cancer-associated fibroblasts (CAFs) play an important role in the tumorigenesis, immunosuppression and metastasis of colorectal cancer (CRC), and can predict poor prognosis in patients with CRC. The present study aimed to construct a CAFs-related prognostic signature for CRC. Methods: The clinical information and corresponding RNA data of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATES) and xCell methods were applied to evaluate the tumor microenvironment infiltration from bulk gene expression data. Weighted gene co-expression network analysis (WGCNA) was used to construct co-expression modules. The key module was identified by calculating the module-trait correlations. The univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were combined to develop a CAFs-related signature for the prognostic model. Moreover, pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases were employed to evaluate the protein expressions. Results: ESTIMATES and xCell analysis showed that high CAFs infiltration was associated with adverse prognoses. A twenty-gene CAFs-related prognostic signature (CAFPS) was established in the training cohort. Kaplan-Meier survival analyses reveled that CRC patients with higher CAFs risk scores were associated with poor prognosis in each cohort. Univariate and multivariate Cox regression analyses verified that CAFPS was as an independent prognostic factor in predicting overall survival, and a nomogram was built for clinical utility in predicting CRC prognosis. Patients with higher CAFs risk scores tended to not respond to immunotherapy, but were more sensitive to five conventional chemotherapeutic drugs. Conclusion: In summary, the CAFPS could serve as a robust prognostic indicator in CRC patients, which might help to optimize risk stratification and provide a new insight into individual treatments for CRC.
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spelling pubmed-97322692022-12-10 Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer Zhang, Lei Xu, Chao Wang, Si-Han Ge, Qin-Wen Wang, Xiao-Wei Xiao, Pan Yao, Qing-Hua Front Genet Genetics Background: Cancer-associated fibroblasts (CAFs) play an important role in the tumorigenesis, immunosuppression and metastasis of colorectal cancer (CRC), and can predict poor prognosis in patients with CRC. The present study aimed to construct a CAFs-related prognostic signature for CRC. Methods: The clinical information and corresponding RNA data of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATES) and xCell methods were applied to evaluate the tumor microenvironment infiltration from bulk gene expression data. Weighted gene co-expression network analysis (WGCNA) was used to construct co-expression modules. The key module was identified by calculating the module-trait correlations. The univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were combined to develop a CAFs-related signature for the prognostic model. Moreover, pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases were employed to evaluate the protein expressions. Results: ESTIMATES and xCell analysis showed that high CAFs infiltration was associated with adverse prognoses. A twenty-gene CAFs-related prognostic signature (CAFPS) was established in the training cohort. Kaplan-Meier survival analyses reveled that CRC patients with higher CAFs risk scores were associated with poor prognosis in each cohort. Univariate and multivariate Cox regression analyses verified that CAFPS was as an independent prognostic factor in predicting overall survival, and a nomogram was built for clinical utility in predicting CRC prognosis. Patients with higher CAFs risk scores tended to not respond to immunotherapy, but were more sensitive to five conventional chemotherapeutic drugs. Conclusion: In summary, the CAFPS could serve as a robust prognostic indicator in CRC patients, which might help to optimize risk stratification and provide a new insight into individual treatments for CRC. Frontiers Media S.A. 2022-11-25 /pmc/articles/PMC9732269/ /pubmed/36506313 http://dx.doi.org/10.3389/fgene.2022.1054152 Text en Copyright © 2022 Zhang, Xu, Wang, Ge, Wang, Xiao and Yao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhang, Lei
Xu, Chao
Wang, Si-Han
Ge, Qin-Wen
Wang, Xiao-Wei
Xiao, Pan
Yao, Qing-Hua
Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title_full Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title_fullStr Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title_full_unstemmed Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title_short Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
title_sort cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732269/
https://www.ncbi.nlm.nih.gov/pubmed/36506313
http://dx.doi.org/10.3389/fgene.2022.1054152
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