A Breast Cancer Prediction Model Based on a Panel from Circulating Exosomal miRNAs

Abstract

Breast cancer (BC) has been a serious threat to women’s health. Exosomes contain a variety of biomolecules, which is an excellent choice as disease diagnostic markers, but whether it could be applied as a noninvasive biomarker for BC diagnosis demands to be additional studied. In this study, we aimed at creating a predictive model and reveal the value of plasma exosomal miRNA (exo-miRNA) in early diagnosis of BC. Firstly, exosomes isolated from plasma were identified by Nanoparticle Tracking Analysis (NTA), Transmission Electron Microscope (TEM), and Western Blot. miRNA expression in plasma samples from 56 BC patients and 40 normal controls was analyzed by high-throughput sequencing. miRNAs with strong correlation characteristics were selected by Lasso logistic regression. Then, we built the training set and test set, evaluated the Lasso regression accuracy, and evaluated the performance of different models in the training set and test set. Finally, GO analysis, KEGG, and Reactome pathway enrichment analysis were used to understand the biological significance of 16 characteristic miRNAs. The successful separation of exosomes in serum was identified by NTA, TEM, and Western Blot. The training set data matrix containing 1962 miRNAs was obtained by sequencing for model construction, and 16 strongly correlated miRNAs were selected by Lasso logistic regression. The accuracy of Lasso regression in training set and test set were 97.22% and 95.83%, respectively. We built different models and evaluated the performance of each model in the training set and test set. The results showed that the AUC values of Lasso, SVM, GBDT, and Random Forest model in the training set were 1, and the AUC values in the test set were 0.979, 0.936, 0.971, and 0.979, respectively. Bioinformatics analysis showed that 16 signature miRNAs were significantly enriched in cancer-related pathways such as herpes simplex virus 1 infection, TGF-β signaling, and Toll-like receptor family. The results of this study suggest that the 16 characteristic miRNAs screened from plasma exosomes can be used as a group of biomarkers, and the prediction model constructed based on this set of markers is expected to be used in the early diagnosis of BC.

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

“…The knowledge gained is also applicable to broader processes of fluid and pathogen accumulation, dispersal, or elimination (such as contamination of produce by human or plant pathogens during irrigation) [215]. The intricate leaf shape in these applications causes the liquid sheet to break up in a non-symmetrical manner in air, leading to different distributions of drop sizes and speeds compared to symmetrical sheet fragmentation [216]. Various types of splashes occur when a crown strikes a thin film, deep pool, or nearsurface margin, including unstable sheet development and breakup [217].…”

The role of non-coding RNAs in extracellular vesicles in breast cancer and their diagnostic implications

“…Key miRNAs with differing expression profiles include miR-9 , miR-16 , miR-21 , miR-429 , miR-320a , miR-1246 and miR-4433b-5p which have been collected from serum, plasma, or urine EVs [ 151 – 153 ]. Furthermore, to improve their role as a biomarker, panels of miRNAs have been developed and shown to lead to a more accurate diagnosis, with a combination of miR-21 , miR-16 , miR-9 , and miR-429 enabling discrimination between different BC subtypes and healthy controls (sensitivity 96.8% and specificity 80.0%) [ 151 ] and a predictive model of 16 EV miRNAs outperforming routine diagnostic methods [ 154 ] (Fig. 3 ).…”