ArticleMolecular oncology2026
Transcriptional profiling of circulating extracellular vesicles from prebiopsy prostate cancer patients.
Article in Molecular oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Machine learning-guided multimodal profiling defines perturbed immune states at the time of cancer diagnosis.Briefings in bioinformatics · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
Early detection of cancer is essential for effective treatment. However, current prostate cancer screening methods lack sufficient sensitivity and specificity, leading to overdiagnosis and unnecessary treatment. There is also an unmet need to distinguish clinically significant from insignificant prostate cancer. To identify complementary biomarkers for improved screening and diagnosis, we performed transcriptional profiling of cancer-associated transcripts in circulating extracellular vesicles (EVs) isolated from peripheral blood of patients with suspected prostate cancer prior to biopsy and healthy donors. Expression data for 2549 mRNAs were obtained from 28 men. CAPN5 expression was significantly lower, whereas BIRC2, CASP3, CD63, FMO5, IRF6, PFDN1, PRDX6, PSMD2, RIT1, S100A2, THBS1, and XRCC2 were significantly elevated in EVs from patients with significant prostate cancer (n = 14) compared with cancer-free individuals and patients with insignificant disease (n = 14). Candidate biomarkers were subsequently evaluated by in silico validation using the The Cancer Genome Atlas (TCGA) prostate adenocarcinoma dataset and the GEO dataset GSE70768 containing benign and malignant prostate tissues. This analysis identified CASP3, XRCC2, and RIT1 transcripts in circulating EVs as promising biomarkers for the early detection of significant prostate cancer.
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Registered trials
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