Mechanistic investigation of glycolysis and pyroptosis in colon adenocarcinoma tissues, and prognostic analysis of patient clinical outcomes

TL;DR

This study identifies a set of genes related to glycolysis and pyroptosis that can predict survival outcomes in colon cancer patients.

Contribution

A novel prognostic model using glycolysis and pyroptosis-related genes for predicting colon adenocarcinoma patient outcomes.

Findings

A prognostic model using 53 differentially expressed genes showed significantly reduced survival in high-risk groups (P < 0.001).

The model's risk score effectively predicted 1, 3, and 5-year survival with an AUC greater than 0.7.

High-risk groups exhibited elevated immune cell infiltration and gene expression compared to low-risk groups.

Abstract

The exact mechanisms driving colorectal cancer (CRC) are yet to be fully elucidated. This study aims to confirm the reliability of a prognostic model for colon adenocarcinoma (COAD) by analyzing the varied expression levels of Glycolysis & Pyroptosis-Related Differentially Expressed Genes (G&PRDEGs) in COAD using bioinformatics tools.

We retrieved gene expression data and clinical details for COAD patients from the Cancer Genome Atlas (TCGA) database. These data were analyzed to categorize the samples into pyroptosis-positive and pyroptosis-negative groups based on their expression of G&PRDEGs. A prognostic model for COAD was then developed using LASSO Cox regression analysis, focusing on these differentially expressed genes (DEGs). Kaplan-Meier curves were plotted to assess the differences in survival between the two groups. Furthermore, we conducted multivariate Cox regression analyses to evaluate the influence of clinical parameters and model-derived risk scores. Analyses of pathway enrichment were performed using R software, alongside single-sample gene-set enrichment analysis (ssGSEA) to explore the role of immune cells and functions associated with G&PRDEGs.

A predictive model was developed using 53 G&PRDEGs that were expressed differentially. An examination of survival rates revealed that the high-risk groups exhibited a noticeably diminished overall survival (OS) in comparison to the low-risk groups in the TCGA database (P < 0.001). An examination of the receiver operating characteristic (ROC) curve indicated that the risk score effectively predicted outcomes at 1, 3, and 5 years, with a space beneath the curve greater than 0.7. The risk score significantly affected the survival of COAD sufferers in the TCGA database, on the basis of the multivariate Cox regression analysis. The high-risk groups had a hazard ratio (HR) of 3.988 (95% CI 2.865 ~ 5.551, P < 0.001) in contrast to the low-risk groups. Examinations of enrichment identified an increase in the expression of DEGs in the high-risk groups, in contrast to the low-risk cohort, on the basis of the TCGA database. SsGSEA revealed elevated levels of immune cell soakage in the high-risk groups in contrast to the low-risk groups.

The COAD prognosis model, developed using G&PRDEGs, exhibits predictive capability for the prognosis of COAD sufferers and offers utility in prognostic analysis for COAD sufferers.

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