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ArticlePloS one2025

Machine learning-based transcriptomic analysis identifies NAMPT and SAT1 as potential biomarkers and therapeutic targets in ferroptosis-associated rheumatoid arthritis.

Devi Soorya Narayana Sasikumar, Vino Sundararajan

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In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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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

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Devi Soorya Narayana SasikumarIntegrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Vino SundararajanIntegrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.ORCID https://orcid.org/0000-0002-0015-8460

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRheumatoid arthritis (RA) is an autoimmune disease with chronic presentation, involving symmetric joints and systemic involvement. Ferroptosis is iron-dependent programmed cell death through lipid peroxide accumulation, implicated in inflammatory diseases, including RA. However, its underlying mechanisms and gene-level contributions to RA pathogenesis remain largely unexplored. Therefore, this study emphasizes identifying ferroptosis-related genes associated with RA, evaluating their diagnostic, prognostic, and therapeutic potential, and exploring their role in immune modulation.

methodsThe transcriptomic dataset (GSE89408) from the peripheral blood gene expression was downloaded from the Gene Expression Omnibus (GEO) database. We extracted the differentially expressed genes (DEGs) using R software and the most relevant modules relevant to RA were identified through weighted gene coexpression network analysis (WGCNA). We also identified the differentially expressed ferroptosis genes. The gene ontology and pathways involving the common genes were identified and the protein-protein interaction network was constructed. The hub genes were identified using three machine learning algorithms, least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), after which the diagnostic efficiency of the hub genes and the correlation with immune infiltrating cells were predicted.

resultsA total of 9176 DEGs and a module of 314 genes were obtained which has a significant correlation with RA and 17 genes were selected after the intersection. Using the three machine-learning algorithms, we retrieved 8 hub genes (CISD2, LACTB, PRNP, SAT1, NAMPT, MITD1, SOD2, and FASN) between RA and ferroptosis which showed good diagnostic performance using the ROC curve and nomogram plots. Functional annotation analysis was utilized to inspect the biological functions of the hub genes and the genes showed a substantial association with the immune infiltrating cells.

conclusionNAMPT, CISD2, LACTB, PRNP, SAT1, SOD2, MITD1, and FASN may modulate ferroptosis and RA by influencing immunity, and NAMPT and SAT1 contribute significantly to the diagnosis and treatment of the disease. Future studies focusing on validating these genes in larger cohorts and exploring their therapeutic potential will provide deeper insights.

Indexed as

Antigens, SurfaceArthritis, RheumatoidCytokinesFerroptosisMachine LearningNicotinamide PhosphoribosyltransferaseBiomarkersDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MapsTranscriptomeAntigens, SurfaceBiomarkersCytokinesNicotinamide Phosphoribosyltransferasenicotinamide phosphoribosyltransferase, human

Identifiers

PMID41021591
PMCPMC12478934

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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.