Tumor-educated platelets as a source of potential biomarkers for colorectal cancer.
3064 Background: Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related death worldwide. Current diagnostic methods rely on invasive procedures and serum markers with limited sensitivity, highlighting the need for novel, minimally invasive biomarkers. Tumor-educated platelets (TEPs) have emerged as a promising source, as malignant cells reprogram platelets through molecular alterations. This study aimed to identify differentially expressed genes in TEPs from patients with CRC that could serve as potential diagnostic biomarkers. Methods: We downloaded gene expression data from platelets from the Gene Expression Omnibus (GEO; GSE183635), tissue gene expression data from TCGA-COAD and TCGA-READ, and vesicle data from Vesiclepedia. The data were preprocessed to remove low-quality reads, and high-quality reads were aligned to the human reference genome GRCh38.p13. We performed differential expression analysis through DESeq2 (|log2FoldChange| > 1, adjusted p-value < 0.05). Gene ontology (GO) enrichment analysis (p-value < 0.05) was conducted, and shared genes between TEPs, tumor tissues, and vesicles were identified. ROC curves (AUC > 0.75) assessed diagnostic potential. Results: A total of 3,211 differentially expressed genes were identified in TEPs, with 55 upregulated and 3,156 downregulated. Six genes ( TLN1, IGF2, IFITM3, DKK1, MYL9, TNNC2 ) were consistently upregulated in TEPs, tumor tissues, and observed in microvesicles. These genes exhibited AUC values ranging from 0.76 to 0.83, indicating high sensitivity for distinguishing CRC patients from healthy controls. Conclusions: Our study identified six DEGs in TEPs with elevated expression in CRC tissue, highlighting their potential as minimally invasive biomarkers for CRC diagnosis. These findings pave the way for developing more precise diagnostic tools to improve early detection and patient outcomes. Potential biomarker genes in TEPs for CRC, ranked by Log2FC. Gene Log2FoldChange P-adjusted AUC DKK1 2.011554 1,50×10 −3 0.78 IGF2 1.5006689 3,56×10 −9 0.81 IFITM3 1.102034 3,01×10 −9 0.81 TLN1 1.098185 1,24×10 −5 0.83 MYL9 1.060944 1,91×10 −15 0.77 TNNC2 1.023068 7,22×10 −8 0.76 AUC: Area Under the Curve.
Authors
- Danielle Queiroz Calcagno (ORCID: https://orcid.org/0000-0002-4429-2573)
- Guilherme Cardoso Almada
- Valéria Cristiane Santos da Silva
- Fabiano Cordeiro Moreira (ORCID: https://orcid.org/0000-0002-2799-3546)
- Ana Sofia Rodrigues (ORCID: https://orcid.org/0000-0001-7060-879X)
- Rodnei Macambira
- Juscelino Carvalho de Azevedo (ORCID: https://orcid.org/0000-0001-8770-9490)
- Fernanda Jardim da Silva
Institutions
- Universidade Federal do Pará (BR)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1200/jco.2025.43.16_suppl.3064
- Primary Topic
- Inflammatory Biomarkers in Disease Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico