Machine learning–driven transcriptomic and single-cell profiling of programed cell death patterns in colon cancer

Objective Colon cancer ranks among the most prevalent malignancies globally. Despite advances in therapy, patients’ prognosis remains poor, particularly in advanced stages. Programed cell death (PCD), including over 20 patterns, plays a pivotal role in colon cancer progression. However, a systematic analysis of the PCD regulatory network in colon cancer is lacking. Methods We comprehensively analyzed various PCD patterns in colon cancer using bulk transcriptomic and single-cell transcriptomic data from GEO and TCGA databases. Multiple machine learning algorithms were used to identify Key PCD patterns. A novel combined cell death index (CCDI) was constructed using 117 algorithm combinations. Functional enrichment, immune infiltration, nomogram construction, and pseudotime trajectory analyses were also performed. Results Different PCD patterns significantly impacted colon cancer prognosis. Disulfidptosis and anoikis were consistently identified as critical PCD patterns. The CCDI, based on these genes, outperformed existing models in prognostic prediction. Additionally, disulfidptosis and anoikis scores enriched in endothelial cells (ECs), which exhibited close interactions with other cell types. Six genes (CD36, CLU, FLNA, NOTCH3, TAGLN, TIMP1) were identified as key regulators during ECs phenotypic transition. Conclusions This study demonstrates the key roles of disulfidptosis and anoikis, and establishes a novel CCDI model with prognostic value in colon cancer. Additionally, it insights into ECs phenotypic transition and their regulatory genes, provides new therapy targets for colon cancer.