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. 2023 Mar 8;23(1):58.
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

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

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 et al. BMC Gastroenterol. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Diagram of the study
Fig. 2
Fig. 2
Identification of differentially expressed (DE) immune genes. a Heat map of the DE genes. b Volcano plot of the DE genes. c Heat map of the DE immune genes. d Volcano plot of the DE immune genes. e Univariate Cox analysis
Fig. 3
Fig. 3
Identification of differentially expressed (DE) transcription factors (TFs). a Heat map of the DE TFs. b Volcano plot of the DE TFs. c A regulatory network of TFs and immune genes
Fig. 4
Fig. 4
Construction of the prognostic risk model. a Multivariate Cox analysis. b Overall survival (OS) in the training cohort. c Time-dependent receiver operating characteristic (ROC) curve analysis (c-1:ROC 1 year, c-2:ROC 3 year, c-3:ROC 5 year) in the training cohort. d Risk score distribution plot in the training cohort. e Survival status scatter plots in the training cohort. f Heatmap of risk genes
Fig. 5
Fig. 5
Validation of the prognostic risk model. a Overall survival (OS) in the validation cohort. b Time-dependent receiver operating characteristic (ROC) curve analysis (b-1:ROC 1 year, b-2:ROC 3 year, b-3:ROC 5 year) in the validation cohort. c Risk score distribution plot in the validation cohort. d Survival status scatter plots in the validation cohort
Fig. 6
Fig. 6
Independent prognostic value and clinical utility of the risk model. a Univariate analysis. b Multivariate Cox analyses. c Clinical correlation analysis of immune genes (c-1: Correlation analysis between the risk score and T, c-2: Correlation analysis between the VIP expression and T, c-3: Correlation analysis between the CCL28expression and M, c-4: Correlation analysis between the ESM1 expression and T, c-5: Correlation analysis between the FABP4 expression and T)
Fig. 7
Fig. 7
Heat map of immune cell infiltration between High and Low risk
Fig. 8
Fig. 8
Correlation analysis between the risk score and B cell
Fig. 9
Fig. 9
Correlation analysis between the risk score and Macrophage cell
Fig. 10
Fig. 10
Correlation analysis between the risk score and Myeloid dendritic cell
Fig. 11
Fig. 11
Correlation analysis between the risk score and CD4+T, CD8+T cell
Fig. 12
Fig. 12
Immune checkpoint genes that differed in between high and low-risk groups
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