Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer
Yueqiong Lao1,2,3, Jie Yang1,2,4, Minli Hu3,1
1Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
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.