Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Inferring gene regulatory networks (GRNs) from clinical transcriptomic data is challenging due to the lack of explicit temporal information.
• Cross-sectional studies provide snapshots but miss dynamic biological processes and disease progression.
• Understanding temporal dynamics is crucial for deciphering complex diseases and identifying therapeutic targets.

Purpose of the Study:

• To present the PROB_R protocol for inferring latent temporal disease progression.
• To reconstruct gene regulatory networks from cross-sectional transcriptomic data.
• To identify key regulatory genes involved in disease progression.

Main Methods:

• The PROB_R protocol utilizes cross-sectional transcriptomic data to infer pseudo-temporal disease progression.
• It applies network inference algorithms to reconstruct gene regulatory networks.
• The method was validated on a breast cancer dataset.

Main Results:

• PROB_R successfully recovered pseudo-temporal gene expression dynamics aligned with disease progression.
• The protocol reconstructed gene regulatory networks from static data.
• Key regulatory genes driving disease progression were identified.

Conclusions:

• PROB_R offers a robust method for inferring temporal dynamics and GRNs from cross-sectional transcriptomic data.
• This approach enhances our understanding of disease progression and gene regulation.
• The identified key regulators can serve as potential therapeutic targets.