Network-Based Analysis of Fibromyalgia-Associated Genes Identifies Candidate Molecular Targets and Enriched Biological Pathways
- 1All India Institute of Medical Sciences, Kalyani, India
Abstract
BackgroundFibromyalgia (FM) is a disorder characterized by chronic pain and somatic symptoms that are not fully understood. Central sensitization, a process by which the central nervous system amplifies pain signals, is a key aspect of the pathophysiology of fibromyalgia. Despite its prevalence, effective treatment options are limited and the underlying pathophysiological mechanisms are not fully understood. Network pharmacology, which analyzes the interactions between diseases, targets, and biological pathways, can be used to identify potential molecular targets and pathways associated with fibromyalgia. In this study, we aimed to identify potential hub genes and the underlying biological pathways involved in fibromyalgia.
MethodsFour online databases, GeneCards, DisGeNET, OMIM, and PharmGKB, were initially screened for genes associated with fibromyalgia. GeneCards and DisGeNET were subsequently used to identify common genes, which were used to generate a protein association network using the STRING database. Cytoscape software was used to analyze the network, and the CytoHubba plugin was used to identify hub genes based on degree and betweenness centrality. Functional enrichment analysis was performed using ShinyGO to identify significantly enriched KEGG pathways.
ResultsTNF and IL6 were identified as the top two hub genes with the highest degree of centrality. Functional enrichment analysis identified the Toll-like receptor (TLR) signaling pathway as a significantly enriched pathway among the prioritized hub genes, with a fold enrichment of 6.78 and FDR of 0.0346.
ConclusionThis study identified TNF, IL6 and other highly connected genes, along with TLR signaling as an enriched pathway in the fibromyalgia-associated gene network. These findings provide potential molecular targets and pathways for further investigation into the pathophysiology of fibromyalgia. Further experimental and clinical studies are required to independently validate these findings and determine their therapeutic relevance.
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BibTeX
@misc{sarkar2026networkbased,
title = {{Network-Based Analysis of Fibromyalgia-Associated Genes Identifies Candidate Molecular Targets and Enriched Biological Pathways}},
author = {S. Sarkar and A. Srinivasan},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.26363374},
url = {https://www.medrxiv.org/content/10.64898/2026.09.18.26363374v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Network-Based Analysis of Fibromyalgia-Associated Genes Identifies Candidate Molecular Targets and Enriched Biological Pathways
AU - S. Sarkar
AU - A. Srinivasan
PY - 2026
DA - 2026/09/18
PB - medRxiv
DO - 10.64898/2026.09.18.26363374
UR - https://www.medrxiv.org/content/10.64898/2026.09.18.26363374v1
AB - BackgroundFibromyalgia (FM) is a disorder characterized by chronic pain and somatic symptoms that are not fully understood. Central sensitization, a process by which the central nervous system amplifies pain signals, is a key aspect of the pathophysiology of fibromyalgia. Despite its prevalence, effective treatment options are limited and the underlying pathophysiological mechanisms are not fully understood. Network pharmacology, which analyzes the interactions between diseases, targets, and biological pathways, can be used to identify potential molecular targets and pathways associated with fibromyalgia. In this study, we aimed to identify potential hub genes and the underlying biological pathways involved in fibromyalgia. MethodsFour online databases, GeneCards, DisGeNET, OMIM, and PharmGKB, were initially screened for genes associated with fibromyalgia. GeneCards and DisGeNET were subsequently used to identify common genes, which were used to generate a protein association network using the STRING database. Cytoscape software was used to analyze the network, and the CytoHubba plugin was used to identify hub genes based on degree and betweenness centrality. Functional enrichment analysis was performed using ShinyGO to identify significantly enriched KEGG pathways. ResultsTNF and IL6 were identified as the top two hub genes with the highest degree of centrality. Functional enrichment analysis identified the Toll-like receptor (TLR) signaling pathway as a significantly enriched pathway among the prioritized hub genes, with a fold enrichment of 6.78 and FDR of 0.0346. ConclusionThis study identified TNF, IL6 and other highly connected genes, along with TLR signaling as an enriched pathway in the fibromyalgia-associated gene network. These findings provide potential molecular targets and pathways for further investigation into the pathophysiology of fibromyalgia. Further experimental and clinical studies are required to independently validate these findings and determine their therapeutic relevance.
ER -