Identification pyroptosis-related gene signature to predict prognosis and associated regulation axis in colon cancer

Abstract

Background: Pyroptosis is an important component of the tumor microenvironment and associated with the occurrence and progression of cancer. As the expression of pyroptosis-related genes and its impact on the prognosis of colon cancer (CC) remains unclear, we constructed and validated a pyroptosis-related genes signature to predict the prognosis of patients with CC. Methods: Microarray datasets and the follow-up clinical information of CC patients were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Candidate genes were screened out for further analysis. Various methods were combined to construct a robust pyroptosis-related genes signature for predicting the prognosis of patients with CC. Based on the gene signature and clinical features, a decision tree and nomogram were developed to improve risk stratification and quantify risk assessment for individual patients. Results: The pyroptosis-related genes signature successfully discriminated CC patients with high-risk in the training cohorts. The prognostic value of this signature was further confirmed in independent validation cohort. Multivariable Cox regression and stratified survival analysis revealed this signature was an independent prognostic factor for CC patients. The decision tree identified risk subgroups powerfully, and the nomogram incorporating the gene signature and clinical risk factors performed well in the calibration plots. Conclusion: Pyroptosis-related genes signature was an independent prognostic factor, and can be used to predict the prognosis of patients with CC.

Keywords: colon cancer; genes signature; immunotherapy; prognosis; pyroptosis.

FIGURE 1

Identify the pyroptosis-related genes. (A) The correlation heatmap containing the candidate pyroptosis-related genes. (Red: positive correlation; Blue: negative correlation. The strength of the relevance was represented by the depth of the color). (B) Univariate cox regression analysis of OS for each pyroptosis-related genes. (C) Patients with low-risk scores had a survival advantage over patients with high-risk scores. (D) Pyroptosis ssGSEA scores were significantly elevated in patients who died during follow up. (E) Kaplan–Meier curves for the OS of patients in the high- and low-risk groups. (F) ROC curves indicated the predictive efficiency of the risk score.

FIGURE 2

Construction of risk signature in the training cohort. (A) WGCNA was performed with whole-transcriptome profiling data and pyroptosis ssGSEA Z-score. (B) A total of 26 non-grey modules were identified after merging. (C) The red module depicting the highest correlation (r = 0.18, p = 3e-06) was considered the most correlated with pyroptosis. (D) Sixty-seven promising candidates were identified among hub genes extracted from the red module. (E,F) The LASSO Cox regression model was used to identify the most robust markers, with an optimal λ value of 0.0617. (G) Distribution of LASSO coefficients of the pyroptosis-related gene signature.

FIGURE 3

The gene signature predicts worse survival in the training set. (A) GSEA confirmed the status of pyroptosis in the two subgroups. (B) The follow up data indicated that PRS significantly decreased in patients alive. (C) Kaplan–Meier survival curve showed that patients with lower PRS enjoyed better outcomes. (D) Multivariate Cox regression analysis demonstrated that PRS was an independent risk factor for OS. (E) tROC analysis suggested that PRS was an accurate variable for predicting the survival.

FIGURE 4

Validation of the gene signature in different series. (A) TCGA confirmed the pyroptosis status in the validation cohort. (B) PRS was significantly decreased in patients alive in the validation cohort. (C) Patients with lower PRS exhibited better prognosis in the validation cohort. (D,E) The best k value was chosen for NMF consensus clustering in the TCGA (Figure 4D) and GEO (Figure 4E) cohorts. (F,G) Statistical difference in OS was observed in NMF-derived clusters based on the expression pattern of the gene signature (TCGA: Figure 4F; GEO: Figure 4G). (H,I) Multivariate Cox regression analysis indicated that PRS was an independent risk factor for OS in the training and validation cohorts (Figure 4H), as well as in the all cohorts (Figure 4I).

FIGURE 5

The gene signature serves as a valuable marker for poor survival in the pooled cohort. (A) Meta-analysis in the training and validation cohorts. (B) Meta-analysis for subgroup analysis. (C) PRS Z-scores were significantly elevated in deceased patients. (D) PRS discriminated high-risk patients in different clinicopathological including gender, age, and p-stage.

FIGURE 6

The pyroptotic-related gene signature is a promising marker of therapeutic resistance. (A) GSEA confirmed that the gene signature was associated with therapeutic resistance. (B) PRS is negatively associated with virous cancer therapeutic pathways. (C) PRS is related to immunosuppressive cells. (D) A landscape plot was conducted to depict the relationships between different molecules and the pyroptosis-related gene signature. (E) The ratio of worse outcomes after surgery is greatly elevated in higher PRS group. (F–H) Low-PRS is a prognostic marker of a more favorable outcome in different subgroups (6F: drugs; 6G: surgery; 6H: location of primary tumor).

FIGURE 7

Combination of the pyroptosis signature and clinicopathological features improves risk stratification and survival prediction. (A) A decision tree was constructed to improve risk stratification. (B) Performance of the decision tree. (C) A nomogram was constructed to quantify risk assessment for individual patients. (D) Calibration analysis indicated a high accuracy of survival prediction. (E,F) tROC analysis and decision curve analysis (DCA) demonstrated that the nomogram was the most stable and powerful predictor for OS among all the clinical variables.