Evidence map›Paper›PMID 42920017›Full record

ArticleMammalian genome : official journal of the International Mammalian Genome Society2026

Comprehensive regulatory network construction highlights lncRNA-miRNA-mRNA-TF interactions and key molecular drivers in rheumatoid arthritis.

Rithika Selvaranganathan, Gowtham Murugesan, Pavithra Nagendran, Jeyakumar Natarajan

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Article in Mammalian genome : official journal of the International Mammalian Genome Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Rithika SelvaranganathanData Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Gowtham MurugesanData Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Pavithra NagendranData Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Jeyakumar NatarajanData Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India. n.jeyakumar@buc.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and progressive joint damage. Although numerous molecular contributors have been reported, the integrated regulatory architecture linking coding and non-coding transcripts remains incompletely defined. This study aimed to construct a multilayer lncRNA-miRNA-mRNA-transcription factor (TF) network to prioritize candidate regulators associated with RA. Publicly available RNA-sequencing datasets from RA and control samples were retrieved from the NCBI Sequence Read Archive. Differential expression analysis was performed using DESeq2. High-confidence miRNA-mRNA interactions were obtained from miRDip, miRNA-lncRNA associations from miRNet, and TF annotations from AnimalTFDB. Functional enrichment analyses were conducted to identify overrepresented biological themes. Network topology and module structure were evaluated in Cytoscape using CytoHubba and MCODE. Integration of interaction datasets identified 179 mRNAs targeted by 93 miRNAs, of which 90 miRNAs were connected to 404 annotated lncRNAs. Twenty-five TFs were incorporated into the regulatory framework. Degree-based analysis highlighted QKI, hsa-miR-497-5p, and KCNQ1OT1 as the most connected nodes within their respective categories. Module detection revealed five densely interconnected sub-networks, indicating coordinated regulatory organization. Several prioritized elements have previously been implicated in immune or inflammatory regulation, supporting the biological relevance of the network. This integrative analysis delineates a comprehensive regulatory landscape of RA and provides a ranked set of candidate molecules for future experimental validation. Among these, CCDC6 was identified as a downregulated gene positioned within a highly connected module and linked to multiple hub miRNAs and lncRNAs, suggesting potential functional importance in RA pathobiology.

Indexed as

Arthritis, RheumatoidGene Regulatory NetworksMicroRNAsRNA, Long NoncodingRNA, MessengerTranscription FactorsAnimalsGene Expression RegulationHumansMicroRNAsRNA, Long NoncodingRNA, MessengerTranscription Factors

Identifiers

PMID42920017

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.