Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

This protocol outlines a multi-step computational pipeline for identifying potential therapeutic targets from RNA-sequencing data, including installation of relevant software, setup verification, and differential expression analysis using edgeR. We then show how to utilize the signaling pathway impact analysis (SPIA) algorithm to predict statistically significant pathways. To ensure confidence in the results, we focus on significant pathways (p < 0.05) to reduce false positive results. Unlike traditional gene sets, these pathways reflect protein-protein interaction networks, offering mechanistic insights into cellular processes like the cell cycle, immune response, and metabolism. These pathways are then analyzed using the Pathway2Targets algorithm, which interfaces with the OpenTargets.org database via an application programming interface (API). This algorithm incorporates a novel weighting approach that scores known drug targets within the identified pathways, while providing progress in real-time. Runtime depends on pathway complexity and target density. The output consists of two ranked files. The first file contains a list of predicted drug targets and their weighted scores, while the second consists of various details for the associated therapeutics. Together, this pipeline facilitates the prioritization of druggable targets and treatments in the context of disease-specific gene expression profiles.