Novel six-gene prognostic signature based on colon adenocarcinoma immune-related genes

Colon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to dete...

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Bibliographic Details
Published in:BMC bioinformatics 2022-10, Vol.23 (1), p.1-418, Article 418
Main Authors: Zhou, Rui, Gao, Zhuowei, Ju, Yongle
Format: Article
Language:English
Subjects:
Adenocarcinoma
Cancer
Cancer prognosis
Cancer therapies
Colon
Colon adenocarcinoma
Colon cancer
Colorectal cancer
Cytokines
Datasets
Fibroblast growth factor receptor 9
Gene expression
Genes
Genetic aspects
Genetic engineering
Genomes
Health aspects
Immune response
Immune system
Immune-related genes
Immunoglobulins
Immunology
Immunotherapy
Medical prognosis
Mutation
Network analysis
Oncology, Experimental
Patients
Prognosis
Prognostic prediction model
Prognostic risk model
Regression analysis
Risk
Risk groups
Software
Survival analysis
Transcription factors
Tumor-infiltrating lymphocytes
Tumors
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Summary:Colon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation. Survival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups. This immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04909-2