Identification of prognostic biomarkers in colorectal cancer through multi-omics profiling of programmed cell death pathways.

BACKGROUND

Programmed cell death (PCD) pathways, including autophagy, ferroptosis, cuproptosis, neutrophil extracellular trap formation (NETosis), and paraptosis, are central to tumor biology and hold potential as therapeutic targets in colorectal cancer (CRC). The aim of this study is to use multi-omics data to analyze the role of PCD in CRC.

METHODS

We conducted a multi-omics analysis using data from The Cancer Genome Atlas (TCGA), single-cell sequencing, and spatial transcriptomics to investigate the expression profiles, prognostic significance, and functional effects of PCD-associated genes in CRC. Key prognostic genes were identified through gene set variation analysis (GSVA), single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression modeling.

RESULTS

Our analysis identified five PCD pathways with significant prognostic relevance in CRC, particularly autophagy and cuproptosis. Pan-cancer analyses highlighted unique and shared expression patterns of PCD-related genes across diverse cancer types, revealing their differential roles in cancer progression. Dynamic network biomarker (DNB) modeling pinpointed stage-specific critical transitions in PCD pathway activity, suggesting temporal variations in pathway influence on tumor progression. Functional assays demonstrated that overexpression of nuclear receptor coactivator 4 (), an autophagy-related gene, suppressed CRC cell migration and invasion while promoting apoptosis, validating its anti-tumor role in CRC.

CONCLUSIONS

This study underscores the prognostic significance of PCD pathways in CRC, highlighting their potential as biomarkers and therapeutic targets. By identifying core genes within these pathways and elucidating their temporal effects on tumor progression, we provide a comprehensive foundation for future research into PCD-targeted therapies in CRC, aiming to enhance personalized treatment strategies.