Research Paper Volume 15, Issue 18 pp 9521—9543
Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma
- 1 Department of Integrated Chinese and Western Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 2 Department of Gastrointestinal Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 3 Shanghai Clinical College, Anhui Medical University, Shanghai, China
- 4 Department of General Surgery, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- 5 The Fifth Clinical Medical College of Anhui Medical University, Hefei, China
- 6 Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China
Received: May 8, 2023 Accepted: August 25, 2023 Published: September 16, 2023
https://doi.org/10.18632/aging.205032How to Cite
Copyright: © 2023 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
The interaction between the tumour and the surrounding microenvironment determines the malignant biological behaviour of the tumour. Cancer-associated fibroblasts (CAFs) coordinate crosstalk between cancer cells in the tumour immune microenvironment (TIME) and are extensively involved in tumour malignant behaviours, such as immune evasion, invasion and drug resistance. Here, we performed differential and prognostic analyses of genes associated with CAFs and constructed CAF-related signatures (CAFRs) to predict clinical outcomes in individuals with colon adenocarcinoma (COAD) based on machine learning algorithms. The CAFRs were further validated in an external independent cohort, GSE17538. Additionally, Cox regression, receiver operating characteristic (ROC) and clinical correlation analysis were utilised to systematically assess the CAFRs. Moreover, CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) analysis were utilised to characterise the TIME in patients with COAD. Microsatellite instability (MSI) and tumour mutation burden were also analysed. Furthermore, Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) elucidated the biological functions and signalling pathways involved in the CAFRs. Consensus clustering analysis was used for the immunological analysis of patients with COAD. Finally, the pRRophic algorithm was used for sensitivity analysis of common drugs. The CAFRs constructed herein can better predict the prognosis in COAD. The cluster analysis based on the CAFRs can effectively differentiate between immune ‘hot’ and ‘cold’ tumours, determine the beneficiaries of immune checkpoint inhibitors (ICIs) and provide insight into individualised treatment for COAD.