Authors:

Mengting Xu

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About Paper:

The major challenge in breast cancer research today is tumor heterogeneity. Breast tumors have been divided into four primary categories includes triple negative, luminal A/B, and HER2. This research aims to non- invasively differentiate between the triple negative and luminal A/B subtypes of breast cancer by using post- translationally modified proteins in plasma extracellular vesicles (EVs). EVs are lipid-shelled vesicles found in almost all body fluids that carry biological components. Protein post-translational modifications (PTMs) have been identified as critical for protein degradation, and these modifications include phosphorylation, acetylation, and glycosylation. By examining data visualizations based on different modifications, including control, luminal A/B, and triple negative groups in formats such as heatmaps, histograms, PCA plots, etc., breast cancer subtypes can be identified through patterns. Running machine learning algorithms on existing datasets allows us to identify potential candidate biomarkers by discerning the top targets for breast cancer subtype identification. These results demonstrate the feasibility of a PTM EV-based pipeline for breast cancer, suggesting that a plasma EV pipeline could offer a new approach for detecting breast cancer diagnosis.

Source:

Purdue University / 2023

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Co-authors:

Mengting Xu