Work

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response

Year: 2026

Type: article

Abstract: Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC) remains unclear. Here, we employed 10 machine learning algorithms to develop a stable, accurate TEP-related gene signature (TEPGS) to explore its links to tumor-associated macrophages (TAMs) and spatial platelet abundance. TEPGS correlated strongly with poor prognosis and outperformed 71 published gene signatures in predicting CRC overall survival. Multi-omics analysis displayed that high TEPGs were marked by increased TP53 mutations, copy number alterations, diminished immune features, enrichment of pro-tumor SPP1 + /FCN1 + TAMs, and elevated spatial platelet abundance. Patients with high TEPGS exhibited resistance to immunotherapy but responded to a BRAF V600E inhibitor, while TEPGS showed tentative value for predicting cetuximab response and preliminary utility for bevacizumab. Functional assays confirmed ARPC1B as an oncogene. Our findings establish TEPGS as a valuable biomarker for prognostic stratification and tailored therapy selection in CRC.

Source: iScience

DOI: 10.1016/j.isci.2026.116229