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Machine learning-driven transcriptomic and single-cell profiling of programed cell death patterns in colon cancer
Jian-Ou Du1, Qing-Ke Huang2, Xue-Cheng Sun2
1Department of Gastroenterology, Yongjia County Traditional Chinese Medicine Hospital, Yongjia, China.
Science Progress
|March 19, 2026
Summary
This study reveals disulfidptosis and anoikis are key in colon cancer progression. A new colon cancer death index (CCDI) shows prognostic value, identifying potential therapeutic targets.
Area of Science:
- Oncology
- Molecular Biology
- Genomics
Background:
- Colon cancer is a leading global malignancy with poor prognosis in advanced stages.
- Programmed cell death (PCD) is crucial in colon cancer, but its regulatory network is not fully understood.
Purpose of the Study:
- To systematically analyze the PCD regulatory network in colon cancer.
- To develop a novel prognostic index for colon cancer based on PCD patterns.
Main Methods:
- Analysis of bulk and single-cell transcriptomic data from GEO and TCGA.
- Application of machine learning algorithms to identify key PCD patterns and construct a combined cell death index (CCDI).
- Functional enrichment, immune infiltration, nomogram, and pseudotime trajectory analyses.
Main Results:
- Disulfidptosis and anoikis were identified as critical PCD patterns impacting colon cancer prognosis.
- The novel CCDI model demonstrated superior prognostic prediction compared to existing models.
- Disulfidptosis and anoikis were enriched in endothelial cells (ECs), with six key genes identified as regulators of ECs phenotypic transition.
Conclusions:
- Disulfidptosis and anoikis play significant roles in colon cancer, and the CCDI offers valuable prognostic insights.
- The study provides new therapeutic targets by elucidating ECs phenotypic transition and regulatory genes in colon cancer.
