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Updated: Sep 24, 2026
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening
Published on: October 26, 2017
Integrative profiling of multi-modal plasma cfRNA signatures enables detection and prognostic risk stratification in
Jun Wang1, Liu Yang1, Kai Fang2
1Shenzhen Key Laboratory of Microbial Genetic Engineering, Vascular Disease Research Center, College of Life Sciences and Oceanography, Guangdong Provincial Key Laboratory of Regional Immunity and Disease, Carson International Cancer Center, School of Medicine, Shenzhen University, Shenzhen 518060, China.
Abstract:
Breast cancer requires non-invasive biomarkers for accurate detection and risk stratification. We comprehensively profiled plasma cell-free RNA (cfRNA) from 41 patients with malignant and 42 with benign breast lesions using SLiPiR-seq. Multiple cfRNA subtypes displayed distinct expression patterns, and machine-learning models were developed with repeated stratified four-fold cross-validation. The integrated cfRNA model achieved a mean AUC of 0.795, while the cf-miRNA model performed best (AUC: 0.814) and was externally validated in an independent cohort (AUC: 0.867). A three-gene tissue expression signature comprising DLST, DOCK4, and EGFL7 further stratified patients by overall survival in TCGA-BRCA. High-risk tumors showed increased PIK3CA mutations, PI3K-AKT pathway activation, and altered immune features. Single-cell analysis revealed distinct localization of the three genes across epithelial cells, tumor-associated macrophages, cancer-associated fibroblasts, and T-cell populations. 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.
