Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis

Background Colorectal cancer (CRC) is the most prevalent gastrointestinal cancer worldwide. Our goal was to construct a model based on pyroptosis- and aging-related genes (PARGs) to predict CRC outcomes of colorectal cancer. Methods The Colon Adenocarcinoma/Rectal Adenocarcinoma Esophageal Carcinoma (COADREAD) dataset from the cancer genome atlas (TCGA) was obtained using R. Colorectal cancer-related datasets, namely, GSE74602, GSE87211, and GSE161158 were acquired from the Gene Expression Omnibus (GEO) database. PARGs were collected from various sources such as the GeneCards database, Molecular Signatures Database (MSigDB), and relevant literature. Differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed using R. Prognostic models were constructed utilizing LASSO (least absolute shrinkage and selection) regression analyses. Column line plots and calibration curve plots were generated using the R package. Immunohistochemical analyses were performed using the HPA (Human Protein Atlas) database. Results To obtain sets of genes related to both pyroptosis and aging (PARGs), we identified overlapping genes from two distinct datasets: one consisting of genes associated with pyroptosis (PRGs), and the other consisting of genes associated with aging (ARGs). We then created a risk signature that encompassed both pyroptosis and aging factors, which was further validated using diagnostic tools such as a Calibration Curve and decision curve analysis (DCA). The risk score derived from this signature significantly affects the overall survival of patients (CRC) patients. The stability and accuracy of this association were further confirmed using stratified survival analysis and DCA. Additionally, GSEA was performed to obtain results for both high-risk and low-risk groups. Conclusions CRC severity may be predicted using the PARGs signature, which is a reliable prognostic analysis model.