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The Cancer-Associated Fibroblasts-Related Gene COMP Is a Novel Predictor for Prognosis and Immunotherapy Efficacy and Is Correlated with M2 Macrophage Infiltration in Colon Cancer.
Ma, He; Qiu, Qingqing; Tan, Dan; Chen, Qiaofeng; Liu, Yaping; Chen, Bing; Wang, Mingliang.
Afiliação
  • Ma H; Department of General Surgery, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Qiu Q; Department of General Surgery, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Tan D; Department of General Surgery, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Chen Q; Department of General Surgery, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Liu Y; Department of General Surgery, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Chen B; Central Laboratory, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai 200020, China.
  • Wang M; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Biomolecules ; 13(1)2022 12 28.
Article em En | MEDLINE | ID: mdl-36671447
ABSTRACT

BACKGROUND:

Colon cancer is characterized by a sophisticated tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which make up the majority of the stromal cells in TME, participate in tumor development and immune regulation. Further investigations of CAFs would facilitate an in-depth understanding of its role in colon cancer TME.

METHODS:

In this study, we estimated CAF abundance based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using the Microenvironment Cell Populations-counter (MCP-counter) algorithm. CAF-related genes were identified by differential gene expression analysis combined with weighted gene coexpression network analysis. For further selection, the least absolute shrinkage and selection operator (LASSO)-Cox regression was used, and the prognostic value of the selected gene was confirmed in numerous external cohorts. The function enrichment, immunological characteristics, tumor mutation signature, immunotherapy response, and drug sensitivity of the selected gene were subsequently explored. The bioinformatics analysis results were validated using immunohistochemistry on clinical samples from our institution.

RESULTS:

According to our findings, cartilage oligomeric matrix protein (COMP) was uncovered as a candidate CAFs-driven biomarker in colon cancer and plays an important role in predicting prognosis in colon cancer. COMP upregulation was associated with enhanced stromal and immune activation, and immune cell infiltration, especially M2 macrophages. Genes that mutated differently between the high- and low-COMP expression subgroups may be correlated with TME change. Following verification, COMP reliably predicted the immunotherapy response and drug response. In addition, our experimental validation demonstrated that COMP overexpression is associated with colon cancer carcinogenesis and is strongly associated with CAFs and M2 macrophage infiltration.

CONCLUSION:

Our study uncovered that COMP was a key CAFs-driven gene associated with M2 macrophage infiltration and acted as a convincing predictor for prognosis and immunotherapy response in colon cancer patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias do Colo / Fibroblastos Associados a Câncer Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias do Colo / Fibroblastos Associados a Câncer Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article