Construction and validation of an immune-related genes prognostic index (IRGPI) model in colon cancer

Abstract

Background: Though immunotherapy has become one of the standard therapies for colon cancer, the overall effective rate of immunotherapy is very low. Constructing an immune-related genes prognostic index (IRGPI) model may help to predict the response to immunotherapy and clinical outcomes.

Methods: Differentially expressed immune-related genes (DEIRGs) between normal tissues and colon cancer tissues were identified and used to construct the co-expression network. Genes in the module with the most significant differences were further analyzed. Independent prognostic immune-related genes (IRGs) were identified by univariate and multivariate cox regression analysis. Independent prognostic IRGs were used to construct the IRGPI model using the multivariate cox proportional hazards regression model, and the IRGPI model was validated by independent dataset. ROC curves were plotted and AUCs were calculated to estimate the predictive power of the IRGPI model to prognosis. Gene set enrichment analysis (GSEA) was performed to screen the enriched KEGG pathways in the high-risk and low-risk phenotype. Correlations between IRGPI and clinical characteristic, immune checkpoint expression, TMB, immune cell infiltration, immune function, immune dysfunction, immune exclusion, immune subtype were analyzed.

Results: Totally 680 DEIRGs were identified. Three independent IRGs,NR5A2, PPARGC1A and LGALS4, were independently related to survival. NR5A2, PPARGC1A and LGALS4 were used to establish the IRGPI model. Survival analysis showed that patients with high-risk showed worse survival than patients in the low-risk group. The AUC of the IRGPI model for 1-year, 3-year and 5-year were 0.584, 0.608 and 0.697, respectively. Univariate analysis and multivariate cox regression analysis indicated that IRGPI were independent prognostic factors for survival. Stratified survival analysis showed that patients with IRGPI low-risk and low TMB had the best survival, which suggested that combination of TMB and IRGPI can better predict clinical outcome. Immune cell infiltration, immune function, immune checkpoint expression and immune exclusion were different between IRGPI high-risk and low-risk patients.

Conclusion: An immune-related genes prognostic index (IRGPI) was constructed and validated in the current study and the IRGPI maybe a potential biomarker for evaluating response to immunotherapy and clinical outcome for colon cancer patients.

Keywords: biomarker; colon cancer; immune cells; prognostic model; tumor microenvironment (TME).

Figure 1

Identification of differentially expressed immune-related genes (DEIRGs) and construction of co-expression modules. 680DEIRGs were identified. (A) Heatmap of the 680 DEIRGs. The DEIRGs were enriched in GO terms (B) related with humoral immune response, complement activation, adaptive immune response, B cell mediated immunity, lymphocyte mediated immunity and immunoglobulin mediate immune response and KEGG pathways (C) related with cytokine-cytokine receptor interaction, complement and coagulation cascades, viral protein interaction with cytokine and cytokine receptor, IL-17 signaling pathway, chemokine signaling pathway, MAPK signal pathway. Six co-expression modules were constructed through WGCNA. The yellow module has the strongest correlation with colon cancer. (D) Dendrogram of the six modules. (E) Heatmap of the correlation between module eigengenes and sample types.

Figure 2

Univariate cox regression analysis was performed to identify survival-related IRGs. (A) FABP2, F2RL1, NR3C2, NR5A2, PPARGC1A, LGALS4 and XDH were identified as survival-related genes. Patients with high FABP2 (B), F2RL1 (C), NR3C2 (D), NR5A2 (E), PPARGC1A (F), LGALS4 (G) and XDH (H) expression had better survival than patients with low expression of those IRGs.

Figure 3

Construction and validation of immune-related prognostic index (IRGPI) model. IRGPI model was constructed using the multivariate cox proportional hazards model. Patients in the TCGA-COAD cohort were used as training set and patients in the GEO cohort were used as the validation set. ROC curves were plotted and AUCs were calculated to estimate the power of the IRGPI model to predict prognosis. (A) Survival of colon patients in high-risk and low risk groups in the TCGA-COAD cohort. Patients in the high risk group showed worse survival than patients in the low risk group. (B) Survival of colon patients in high-risk and low risk group in the GEO cohort. Patients in the high risk group showed worse survival than patients in the low risk group. (C) ROC curves of the IRGPI model for 1-year, 3-year and 5-year. The AUCs of ROC for 1-year, 3-year and 5-year were 0.584,0.608 and 0.697, respectively. (D) ROC curves of IRGPI model, TIS model and TIDE model for 5-years. The IRGPI model showed a stronger predictive power than the TIS and TIDE model. (E) Univariate cox regression analysis showed that IRGPI was correlated to survival. (F) Multivariate cox regression analysis showed that IRGPI was an independent prognostic factor for survival.

Figure 4

Correlation of IRGPI with clinical features. (A) TMB in high-risk and low-risk group showed no statistical difference. (B) Spearman correlation analysis of the correlation between IRGPI and TMB showed that the prognostic index was not correlated with TMB. (C) Survival of high -risk and low-risk group patients with different TMB levels. Patients with IRGPI low-risk and low TMB had the best survival. The 20 genes with the highest mutation frequency in high-risk (D) and low-risk (E) group. High-risk group had higher mutation frequency of APC, TP53, TTN, OBSCN and lower mutation frequency of SYNE1, PIK3CA and FAT4.

Figure 5

Correlation of IRGPI with immune features. (A) Immune cell infiltrations in high-risk and low-risk patients. High-risk group patients had higher infiltration level of M0 macrophages, M1 macrophages and lower infiltration level of naïve B cells, plasma cells and resting CD4+ memory T cells. (B) Immune function score in high-risk and low-risk patients. High-risk group had higher aDCs, HLA, macrophages, pDCs and type I IFN-response. (C) PD-L2 expression in high-risk and low-risk patients. PD-L2 expression was higher in high-risk group (D) Spearman correlation analysis of the correlation between PD-L2 and IRGPI. PD-L2 expression was positively related to IRGPI (E) Immune subtypes in high-risk and low-risk patients. No significant difference was observed in the proportion of each immune subtype between the high-risk and low-risk groups. (F) Immune exclusion score in high-risk and low-risk patients. Immune exclusion score was higher in high-risk group. *p<0.05; **p<0.01; ***p<0.001.

Figure 6

Pathways related to high-risk and low-risk phenotype. Pathways related to Cytokine-cytokine receptor interaction, ECM receptor interaction, focal adhesion were enriched in high-risk phenotype (A). Pathways correlated with ascorbate and aldarate metabolism, butanoate metabolism, drug metabolism cytochrome p450, retinol metabolism and starch and sucrose metabolism were enriched in low-risk phenotype (B).