A novel m7G-related miRNA prognostic signature for predicting clinical outcome and immune microenvironment in colon cancer

Abstract

Background: Colon cancer (CC) is a highly prevalent malignancy worldwide, characterized by elevated mortality rates and poor prognosis. N7-methylguanosine (m7G) methylation is an emerging RNA modification type and involved in the development of many tumors. Despite this, the correlation between m7G-related miRNAs and CC remains to be elucidated. This research aimed to investigate the clinical significance of m7G-related miRNAs in predicting both the prognosis and tumor microenvironment (TME) of CC. Method: We retrieved transcriptome data and associated clinical information from a publicly accessible database. Using univariate Cox and LASSO regression analyses, we established a signature of m7G-related miRNAs. Additionally, we used CIBERSORT and ssGSEA algorithms to explore the association between the prognostic risk score and the TME in CC patients. By considering the risk signature and immune infiltration, we identified differentially expressed genes that contribute to the prognosis of CC. Finally, the expression patterns of prognostic miRNAs were verified using quantitative reverse transcriptase PCR (qRT-PCR) in cell lines. Results: We constructed a prognostic risk signature based on seven m7G-related miRNAs (miR-136-5p, miR-6887-3p, miR-195-5p, miR-149-3p, miR-4433a-5p, miR-31-5p, and miR-129-2-3p). Subsequently, we observed remarkable differences in patient outcomes between the high- and low-risk groups. The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.735, 0.707, and 0.632, respectively. Furthermore, our results showed that the risk score can serve as an independent prognostic biomarker for overall survival prediction. In terms of immune analysis, the results revealed a significant association between the risk signature and immune infiltration, as well as immune checkpoint expression. Finally, our study showed that CCDC160 and RLN3 is the gene most relevant to immune cells and function in CC. Conclusion: Our study conducted a comprehensive and systematic analysis of m7G-associated miRNAs to construct prognostic profiles of CC. We developed a prognostic risk model based on m7G-miRNAs, with the resulting risk scores demonstrating considerable potential as prognostic biomarkers. These findings provide substantial evidence for the critical role of m7G-related miRNAs in colon cancer and may offer new immunotherapeutic targets for patients with this disease.

Keywords: N7-methylguanosine; colon cancer; immune microenvironment; microRNA; prognostic signature.

Figure 1

The flowchart of the study.

Figure 2

The expression levels of m7G methylation regulators and m7G-related miRNAs between tumor and normal samples in TCGA CC cohort. (A) the expression difference of METTL1 and WDR4 between tumor and normal samples; (B) The volcano of differentially expressed m7G-related miRNAs between tumor and normal samples.

Figure 3

Identification of the prognostic m7G-related miRNAs. (A) The univariate Cox regression analysis of eight m7G-related DE-miRNAs; (B) The optimal λ selection by 10 cross validated partial likelihood deviance of the LASSO regression; (C) The LASSO coefficient profiles of seven prognostic m7G-related DE-miRNAs; (D) Forest plot summary of the HR values of seven m7G-related DE-miRNAs.

Figure 4

Evaluation of the prognostic m7G-related miRNAs. (A,B) Kaplan-Meier curves of different risk groups in the training and testing sets; (C,F) The distribution of risk scores ordered from low to high in the training and testing sets; (D,G) The distribution of survival time and survival state in the training and testing sets; (E,H) Heatmap of 7 risk miRNAs expression levels in the training and testing sets.

Figure 5

Validation of the prognostic m7G-related miRNAs signature. (A-B) The AUC curves to predict the sensitivity and specificity of 1-, 3- and 5-year survival according to the riskScore in training and testing sets; (C) Comparison of the AUCs of the riskScore at 1-, 3- and 5-year and clinical features; (D) ROC curves of different risk models; (E-F) Principal component analysis separated CC patients into high- and low-risk groups in the training and testing sets.

Figure 6

Kaplan-Meier analysis of miRNAs for the overall survival in CC patients. (A)miR-136-5p; (B) miR-195-5p; (C) miR-31-5p; (D) miR-6887-3p.

Figure 7

Relationship between the m7G-related miRNA signature and clinical features. (A-L) Kaplan-Meier curves for patients with different clinical features; (M-R) Distribution of risk scores stratified by different clinical subgroups.

Figure 8

Establishment and validation of a nomogram. (A) The correlation of clinical features and riskScore was analyzed by univariate Cox regression analysis related to overall survival; (B) The correlation of clinical features and riskScore was analyzed by multivariate Cox regression analysis with overall survival; (C) A nomogram based on clinical features and riskScore to predict 1-, 3- and 5-year survival; (D) Calibration curve of the nomogram.

Figure 9

Comparison of the tumor microenvironment (TME) and immune features in the two risk groups. (A-C) Analysis of differences in TME scores between different risk groups; (D) Correlation analysis of immune cells; (E) Correlation analysis of immune functions; Comparison of 16 immune cell scores (F) and 13 immune-related function scores (G) between high- and low-risk groups; (H) Expression levels of common immune checkpoints in different groups; (I) Cancer stemness feature analysis.

Figure 10

Differences in the biological pathways involved in the two risk groups. GSEA determines the first 5 hallmarks enriched in the high-risk group (A) and the low-risk group (B).

Figure 11

Identification of mRNAs related to riskScore and immunity. (A) 158 mRNAs associated with riskScore and immune infiltration; (B) GO enrichment analysis of 158 risk-immune-related mRNAs; (C) The univariate Cox regression analysis of 158 risk-immune-related mRNAs; (D) Correlation of prognostic risk-immune-related mRNAs and immune infiltration.

Figure 12

Relative expression of miRNAs. (A)miR-136-5p; (B) miR-195-5p; (C) miR-31-5p; (D) miR-6887-3p; (E) Differential expression of miRNAs in TCGA.