eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks

Area of Science:

• Computational biology
• Molecular biology
• Bioinformatics

Background:

• Measuring global mRNA expression is feasible, but profiling cell signaling pathway activity remains challenging.
• Existing methods struggle to link gene expression changes to upstream signaling events.
• Understanding signaling pathway dynamics is crucial for deciphering cellular responses.

Purpose of the Study:

• To introduce X2K Web, an enhanced implementation of the eXpression2Kinases algorithm.
• To provide a user-friendly platform for predicting upstream cell signaling pathways from gene expression signatures.
• To facilitate the analysis of kinase and transcription factor involvement in biological processes.

Main Methods:

• X2K Web computationally predicts upstream signaling pathways by analyzing differentially expressed genes.
• It identifies enriched transcription factors regulating gene expression.
• It constructs subnetworks using protein-protein interactions and performs kinase enrichment analysis.

Main Results:

• X2K Web integrates extensive transcription factor, kinase, and protein-protein interaction libraries.
• It offers direct access to thousands of gene expression signatures from kinase inhibitor studies in breast cancer cell lines.
• Results are visualized as interactive network images and downloadable bar graphs.

Conclusions:

• X2K Web provides an enhanced and accessible tool for predicting cell signaling pathway activity.
• The platform aids in understanding the regulatory mechanisms underlying gene expression changes.
• Optimized default parameters ensure robust and reliable predictions for biological research.