Discovery of target genes for fibromyalgia through bioinformatics analysis

Fibromyalgia (FM) is a chronic condition marked by widespread pain, fatigue, and other debilitating symptoms, affecting 2-4% of the population, predominantly women. Diagnosing FM is challenging due to its complex symptoms and lack of specific biomarkers. To characterize the gene expression profile in FM and identify target genes and potential biomarkers for FM. The RNA-sequencing data (RNA-seq) from FM patients and healthy controls were downloaded from the GEO database and analyzed in R to detect differentially expressed genes (DEGs). A weighted gene co-expression network analysis (WGCNA) was conducted on all genes to identify FM-associated modules. The intersection of DEGs with key module genes was used to build four machine learning models, with the top features from the support vector machine model tested for drug sensitivity to identify therapeutic targets. Expression of the top five genes was validated using real-time quantitative polymerase chain reaction and Western blotting. We identified 1599 DEGs between FM and healthy control. WGCNA revealed that 267 genes in the pink module were correlated with FM. The overlapped 76 key DEGs allow us to build machine-learning models that predict FM with high accuracy (area under the curve = 0.877). The top five genes that are contributing to the model were tested for potential drug targets. Drug sensitivity analysis showed a strong correlation between HAVCR1 and 10 tyrosine kinase inhibitors among the top gene-drug relationships. This study identified key FM-associated gene targets, demonstrating that their expression profiles can be used to predict FM risk. Our findings provide insights into the molecular mechanisms of FM and highlight potential therapeutic targets for improved FM treatment.