TL;DR
Support is provided for exosomal mRNA profiles as a promising non-invasive approach for the early breast cancer detection, supporting machine learning classifiers as a promising non-invasive approach.
Breast carcinoma remains the most frequently diagnosed malignancy among women globally, carrying a substantial burden of incidence and fatality. Timely diagnosis and intervention are of utmost significance for improving patient prognosis. Accumulating evidence suggests that microRNAs (miRNAs) derived from plasma exosom...
Collectively, multi-modal plasma cfRNA profiling may enable non-invasive breast cancer detection and provide candidate markers for prognostic risk stratification and future precision oncology applications.
This review systematically evaluate recent progress in machine-learning-based screening of diagnostic biomarkers for SCLC across transcriptomic, exosomal RNA, proteomic, metabolomic, metabolomic, and DNA methylation datasets and critically appraise the diagnostic accuracy, validation rigor, and reproducibility of publi...
Colorectal cancer (CRC) is one of the most common malignant tumors. Exosomes are key modulators of intercellular communication and important participants in CRC tumorigenesis. However, the influence and underlying mechanism of cancer-secreted exosomes in CRC are not fully understood. In the present study, exosome-enric...
These findings establish eRNAs as a complementary noncoding layer to conventional messenger RNA classifiers, with clinical potential for breast cancer risk stratification, therapeutic guidance, and early detection.
Early diagnosis of breast cancer (BC) remains challenging. The limited sensitivity and specificity of existing serum tumor markers for reliable clinical application highlight the need to develop a more accurate and efficient screening workflow. This study analyzed serum samples from 255 breast cancer patients and 300 h...
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