Mendelian Randomization Transcriptomics and Network Pharmacology for Identification of Osteoarthritis Drug Targets

This article delineates an integrated methodology that combines Mendelian randomization (MR), transcriptomic analysis, and network pharmacology to identify and prioritize potential therapeutic targets for osteoarthritis (OA). It is designed to guide researchers in implementing this multimodal pipeline to investigate drug repurpose and the development of novel therapeutic interventions for OA. The methodological framework comprises five sequential stages: first, MR analysis pipeline, employing two-sample MR to identify putative causal plasma proteins associated with OA, followed by Steiger filtering and phenome-wide association scanning to assess causal directionality and potential off-target effects; second, transcriptomic sequencing workflow, integrating RNA-seq data to identify and refine candidate protein targets; third, integration strategy, merging MR and transcriptomic results to prioritize candidate proteins; fourth, network pharmacology and molecular docking procedures, involving the construction of protein-protein interaction networks, functional enrichment analysis, and molecular docking to explore ligand-target interactions; and fifth, intended application, focusing on the prioritization of candidate compounds and natural products with potential therapeutic relevance. By organizing the workflow into distinct analytical phases, this framework provides a reproducible approach for transitioning from genetic and transcriptomic discovery to computational drug-target evaluation, without presenting specific experimental outcomes. The methodology facilitates systematic and hypothesis-driven investigation into OA therapeutics using publicly accessible datasets and computational tools.