Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer

Mengdi Hao; Huimin Li; Meng Yi; Yubing Zhu; Kun Wang; Yin Liu; Xiaoqing Liang; Lei Ding

doi:10.1186/s12876-023-02679-6

Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer

BMC Gastroenterol. 2023 Mar 8;23(1):58. doi: 10.1186/s12876-023-02679-6.

Authors

Mengdi Hao^#^{1

2}, Huimin Li^#^{1

2}, Meng Yi^#^{1

2}, Yubing Zhu^#^{1

2}, Kun Wang^{1

2}, Yin Liu^{1

2}, Xiaoqing Liang^{1

2}, Lei Ding^{3

4}

Affiliations

¹ Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, No. 10, Tieyi Road, Haidian District, Beijing, 100038, China.
² Department of Oncology, Ninth School of Clinical Medicine, Peking University, Beijing, 100038, China.
³ Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, No. 10, Tieyi Road, Haidian District, Beijing, 100038, China. dinglei1005@126.com.
⁴ Department of Oncology, Ninth School of Clinical Medicine, Peking University, Beijing, 100038, China. dinglei1005@126.com.

^# Contributed equally.

Abstract

Background: Colon cancer is a common and highly malignant tumor. Its incidence is increasing rapidly with poor prognosis. At present, immunotherapy is a rapidly developing treatment for colon cancer. The aim of this study was to construct a prognostic risk model based on immune genes for early diagnosis and accurate prognostic prediction of colon cancer.

Methods: Transcriptome data and clinical data were downloaded from the cancer Genome Atlas database. Immunity genes were obtained from ImmPort database. The differentially expressed transcription factors (TFs) were obtained from Cistrome database. Differentially expressed (DE) immune genes were identified in 473 cases of colon cancer and 41 cases of normal adjacent tissues. An immune-related prognostic model of colon cancer was established and its clinical applicability was verified. Among 318 tumor-related transcription factors, differentially expressed transcription factors were finally obtained, and a regulatory network was constructed according to the up-down regulatory relationship.

Results: A total of 477 DE immune genes (180 up-regulated and 297 down-regulated) were detected. We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. The model was proved to be an independent prognostic variable with good prognostic ability. A total of 68 DE TFs (40 up-regulated and 23 down-regulated) were obtained. The regulation network between TF and immune genes was plotted by using TF as source node and immune genes as target node. In addition, Macrophage, Myeloid Dendritic cell and CD4⁺ T cell increased with the increase of risk score.

Conclusion: We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. This model can be used as a tool variable to predict the prognosis of colon cancer.

Keywords: Colon cancer; Immune gene; Prognosis; Risk model.

MeSH terms

Colonic Neoplasms* / genetics
Databases, Factual
Fibroblast Growth Factor 2
Humans
Prognosis
Tumor Microenvironment* / genetics

Substances

Fibroblast Growth Factor 2

Abstract

MeSH terms

Substances

Grants and funding