Wen Bi,1 Mengyue Yang,2 Renqun Mao1 

1Department of Hand-Foot Microsurgery, Shenzhen Nanshan People’s Hospital, and the 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China; 2Department of Cardiology, Shenzhen Nanshan People’s Hospital, and the 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China

Correspondence: Renqun Mao, Department of Hand-Foot Microsurgery, Shenzhen Nanshan People’s Hospital, and the 6th Affiliated Hospital of Shenzhen University Health Science Center, No. 89, Taoyuan Road, Nanshan District, Shenzhen, 518052, People’s Republic of China, Tel +86 755 26553111, Email drrenqunmao@126.com

Background: Fibromyalgia (FM) is a commonly encountered disease featuring chronic generalized pain, sleep disorder, and physical fatigue. Ankylosing spondylitis (AS) causes chronic lumbodorsalgia involving the sacroiliac joint, often clinically complicated with FM. Nevertheless, the pathophysiology of FM secondary to AS is still lacking.
Methods: Gene expression data of the whole blood in FM and AS patients were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were evaluated employing the “limma” package. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were implemented to explore common pathways. Weighted gene correlation network analysis (WGCNA) was adopted to screen key gene modules. Three machine learning algorithms were performed to refine the intersected genes. Single sample gene set enrichment analysis (ssGSEA) was applied to explore the relationships between hub genes and immune cells. The dependability of hub gene expressions in clinical blood specimens was verified by RT-PCR. Molecular docking was conducted to predict small molecular compounds targeting hub genes.
Results: DEG analysis screened 419 shared up-regulated and 179 shared down-regulated genes in FM and AS. A total of 143 common genes in positive modules of AS and FM were identified via WGCNA. Six key genes (CETN3, CACNA1E, OGT, QRFPR, SCOC, DIAPH1) were obtained by intersecting the WGCNA-derived shared genes and up-regulated DEGs. CETN3 and CACNA1E were refined as hub genes via three machine-learning algorithms and they showed excellent diagnostic value for FM and AS. However, ssGSEA exhibited different immune cell infiltration patterns in FM and AS. Gabapentin enacarbil was recognized as a potential therapeutic drug for AS-FM patients.
Conclusion: This study reveals the shared hub genes in AS and FM. Meanwhile, these results were confirmed in clinical samples. CETN3 and CACNA1E may become potential diagnostic biomarkers and therapeutic targets for patients with AS complicated by FM.

Keywords: fibromyalgia, ankylosing spondylitis, bioinformatics, CETN3, CACNA1E