DL

A comprehensive transcriptomic meta-Analysis leveraging deep learning to uncover molecular signatures and potential therapeutic targets in Triple-Negative Breast Cancers
A comprehensive transcriptomic meta-Analysis leveraging deep learning to uncover molecular signatures and potential therapeutic targets in Triple-Negative Breast Cancers

Triple-negative breast cancer (TNBC), characterized by its aggressive behavior and lack of hormone receptor expression, remains a therapeutic challenge. This study integrates multi-omics data and AI-driven approaches to dissect the molecular mechanisms driving TNBC progression. Through a meta-analysis of 49 transcriptomic studies (2013–2024), we identified 2,101 differentially expressed genes (DEGs), including 68 consistently dysregulated protein-coding genes, with CXCL10 (↑4.01-fold) and ADH1B (↓4.8-fold) as the most significantly altered. Pathway enrichment revealed upregulated genes associated with cell proliferation, immune evasion, and metabolic reprogramming, while downregulated genes implicated hormonal signaling suppression and extracellular matrix remodeling. Gene Ontology analysis highlighted mitotic regulation and immune dysregulation as central processes. AI-based clustering of protein-protein interaction networks identified five functional modules (Tumor Growth, Invasion & Metastasis, Metabolism, Immune & Inflammation, Hormonal & Stress Response), with hub genes like CDK1 and CXCL8 driving tumor proliferation and immune escape. Notably, machine learning algorithms enhanced data integration and cluster identification, revealing FOXM1 as a key regulator of mitotic pathways (p = 6.189E-07) and JUN as a mediator of stromal-epithelial interactions despite its downregulation.

Apr 9, 2025

Oleuropein's Effects on Breast Cancer Revealed by RNA-Sequencing and Machine Learning
Oleuropein's Effects on Breast Cancer Revealed by RNA-Sequencing and Machine Learning

Breast cancer (BC) remains a leading cause of cancer-related morbidity and mortality worldwide, highlighting the Critical need for innovative treatment strategies. Phytochemicals, bioactive compounds derived from plants, have emerged as promising candidates in cancer therapy due to their diverse anti-cancer properties. Oleuropein, a polyphenol found in olive oil, has shown potential in modulating key signaling pathways, inducing apoptosis, and inhibiting metastasis in various cancer models. In this study, we investigated the effects of oleuropein on genome expression profile of MDA-MB-231 BC cell line by RNA-sequencing method. The cell line treated with 200 μL of oleuropein for 48 hours, total RNA extracted from both treated and untreated cells and RNA sequencing performed to assess global gene expression changes. Differential Gene Expression (DEG) analysis was conducted to evaluate pharmacological effects of Oleuropein treatment through pathway analysis and deep learning models. A comprehensive RNA-sequencing analysis revealed a total of 137 differentially expressed genes in MDA-MB-231cells treated with oleuropein. Of these, 115 genes were downregulated, while 21 genes were upregulated during the study. These findings suggest that oleuropein exerts a significant impact on breast cancer cells by modulating multiple molecular mechanisms. The downregulation of numerous genes involved in cell proliferation, survival, and invasion pathways indicates the potential for oleuropein to inhibit tumor growth and metastasis in BC.

Apr 8, 2025

Drug Discovery
Drug Discovery

Projects bridges AI-driven target prioritization with precision experimental pipelines to uncover novel therapeutics. Leveraging structural bioinformatics and high-throughput virtual screening, the platform computationally evaluates millions of compounds against metagenes—AI-identified genes critical to disease mechanisms (e.g., cancer progression, antimicrobial resistance).

Jan 13, 2024