Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer

Abstract

Background: Microsatellite instability (MSI) significantly impacts treatment response and outcomes in colon cancer; however, its underlying molecular mechanisms remain unclear. This study aimed to identify prognostic biomarkers by comparing MSI and microsatellite stability (MSS).

Methods: Data from the GSE39582 dataset downloaded from the Gene Expression Omnibus database were analyzed for differentially expressed genes (DEGs) and immune cell infiltration between MSI and MSS. Then, weighted gene co-expression network analysis (WGCNA) was utilized to identify the key modules, and the modules related to immune infiltration phenotypes were considered as the immune-related gene modules, followed by enrichment analysis of immune-related module genes. Prognostic signatures were derived using Cox regression, and their correlation with immune features and clinical features was assessed, followed by a nomogram construction.

Results: A total of 857 DEGs and 14 differential immune cell infiltration between MSI and MSS were obtained. Then, WGCNA identified two immune-related modules comprising 356 genes, namely MEturquoise and MEbrown. Eight signature genes were identified, namely PLK2, VSIG4, LY75, GZMB, GAS1, LIPG, ANG, and AMACR, followed by prognostic model construction. Both training and validation cohorts revealed that these eight signature genes have prognostic value, and the prognostic model showed superior predictive performance for colon cancer prognosis and distinguished the clinical characteristics of colon cancer patients. Notably, VSIG4 among the signature genes correlated significantly with immune infiltration, human leukocyte antigen expression, and immune pathway enrichment. Finally, the constructed nomogram model could significantly predict the prognosis of colorectal cancer.

Conclusion: This study identifies eight prognostic signature genes associated with MSI and immune infiltration in colon cancer, suggesting their potential for predicting prognostic risk.

Keywords: colon cancer; immune microenvironment; microsatellite instability; prognostic model; weighted gene co-expression network analysis.

Figure 1

Differential analysis of gene expression profiles and immune cell infiltration between patients with MSI and MSS. (a) The volcano plot shows 857 DEGs between MSI and MSS screened with an adj. p < 0.05 and |log fold change (FC)| > 0.585. (b) The CIBERSORT algorithm was used to calculate the ratio of 22 types of immune cells in 510 samples from the GSE39582 dataset. (c) Box plots depict 14 types of immune cells with significant differences in infiltration between MSI and MSS. *p < 0.05, **p < 0.01, and ***p < 0.001. p < 0.05 indicates statistical significance.

Figure 2

WGCNA and screening of immune-related modules. (a) The soft threshold was determined according to an R-square = 0.85. (b) Four clustering modules, MEblue, MEbrown, MEgrey, and MEturquoise, were identified using WGCNA. (c) MEblue, MEbrown, and MEturquoise modules are significantly correlated. (d) Correlation between four clustering modules and phenotypes. (e) Immune-related modules were identified by evaluating Spearman’s correlation between the 4 key modules and 14 immune cells.

Figure 3

Gene ontology (GO) functions and KEGG pathway enrichment analyses of genes in immune-related modules. (a) Top five enriched GO and KEGG terms for the 51 genes in the MEbrown module. (b) Top five enriched GO and KEGG terms for the 305 genes in the MEturquoise module.

Figure 4

Prognostic model construction and validation. Based on the eight immune-related prognostic signatures, the prognostic model was constructed and validated on the GSE39582 and GSE17536 datasets, respectively. (a) The KM curve based on the training set shows the survival differences between the high- and low-risk groups. (b) Regression coefficients of the eight prognostic signatures in the GSE39582 dataset. (c) 1-, 3-, and 5-year ROC curves of the prognostic model. (d) Expression distribution of the eight signatures in high- and low-risk samples from the GSE39582 dataset. (e) KM survival validation of the prognostic model using the GSE17536 dataset. p < 0.05 indicates statistical significance.

Figure 5

Differences in immune characteristics between high- and low-risk groups and their correlation with the eight prognostic signatures related to microsatellite status. (a) Nine immune cell types with significant differences in infiltration between high- and low-risk groups. (b) Box plots showing significant differences in ImmuneScore and TumorPurity between the two groups. (c) Correlations between the expression of the eight signatures and immune cell infiltration, stromal, immune, and ESTIMATE scores, as well as tumor purity. (d) Nine HLA genes with significant differences between the two groups. (e) Spearman’s correlation of expression levels between HLA family genes and the eight prognostic signatures. (f) Seven of the 17 immune pathways showed significant enrichment score differences between the two groups. (g) Spearman’s correlation between 17 GSVA pathways and the eight model signature genes. p < 0.05 indicates statistical significance.

Figure 6

Identification of independent prognostic factors and construction of a nomogram predictor. (a) The pathological T, N, M, and risk scores were defined as independent prognostic factors using univariate and multivariate Cox regression analyses. (b) Six prognostic factors were included to construct a nomogram. (c) Calibration curves showing a high fit between the predicted and actual values for the 3- and 5-year survival probabilities. (d) 3- and 5-year ROC curves were used in the nomogram to evaluate the predictive performance of the model.

Figure A1

Expression differences between MSI and MSS of eight prognostic signatures.