---
title: "Shared ferroptosis and cuproptosis gene candidates in rheumatoid arthritis: integrative network an"
id: "plos-one-15-integrative-network-analysis-identifies-candidate-genes-shared-between"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-15-integrative-network-analysis-identifies-candidate-genes-shared-between"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358176"
published_at: "2026-09-11T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Shared ferroptosis and cuproptosis gene candidates in rheumatoid arthritis: integrative network an
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-15-integrative-network-analysis-identifies-candidate-genes-shared-between
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358176)
- **Published At:** 2026-09-11T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by synovial inflammation, immune dysregulation, oxidative stress, and disturbed metal ion homeostasis. - The study used an integrative bioinformatic pipeline combining **WGCNA**, differential expression, functional enrichment, PPI network construction, immune infiltration deconvolution, and single-cell analysis to identify metal-dependent cell-death regulators in RA. - Bulk RNA-seq from peripheral CD14+ monocytes (GSE294225) included 15 healthy controls and 9 active RA patients (DAS28 > 2.7); 1,410 differentially expressed genes (DEGs) were identified (adjusted P 1). - An RA-associated co-expression module was enriched for oxidative stress, mitochondrial dysfunction, and cell death pathways, linking metal-dependent regulated cell death to RA pathobiology. - Overlap of ferroptosis- and cuproptosis-related gene sets with RA-associated modules pinpointed three upregulated hub genes: **FTH1** (ferritin heavy chain 1), **SOD2** (superoxide dismutase 2), and **CDKN2A** (cyclin-dependent kinase inhibitor 2A). - These hub genes had high module membership, significant differential expression in RA monocytes, and correlations with immune infiltration patterns: increased pro-inflammatory monocytes/macrophages and reduced regulatory T cells. - Functional enrichment implicated oxidative stress response, iron and copper homeostasis, mitochondrial respiration, and cellular senescence as relevant pathways. - Single-cell RNA-seq (GSE296117) localized hub gene expression predominantly to RA synovial macrophages and fibroblasts, key effector cell types in joint pathology. - The authors propose that intersecting **ferroptosis** and **cuproptosis** pathways may contribute to RA pathogenesis and that FTH1, SOD2, and CDKN2A are candidate biomarkers and therapeutic targets; all underlying data and analysis scripts are publicly available. - Data sources reported: bulk RNA-seq accession GSE294225, single-cell RNA-seq accession GSE296117, and code at the authors' GitHub repository; funding and publication details are provided in the source.
## Clinical Analysis & Structured Key Points
Integrative network analysis identifies candidate genes shared between ferroptosis and cuproptosis pathways in rheumatoid arthritis pathogenesis | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by synovial inflammation, progressive joint destruction, and systemic immune dysregulation. Recent findings suggest that disturbed metal homeostasis and regulated cell death pathways, including ferroptosis (iron-dependent lipid peroxidation) and cuproptosis (copper-dependent mitochondrial proteotoxic stress), contribute to RA pathogenesis. In this study, we used an integrative bioinformatic approach combining weighted gene co-expression network analysis (WGCNA), differential expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, and immune cell infiltration deconvolution to identify key metal-dependent cell death regulators in RA. Using bulk RNA-seq data from peripheral CD14 + monocytes (GSE294225) from 15 healthy controls and 9 patients with active RA (DAS28 > 2.7), we identified 1,410 significantly differentially expressed genes (DEGs) (adjusted P 1). WGCNA revealed an RA-associated module enriched in oxidative stress, mitochondrial dysfunction, and cell death pathways. Overlap analysis of ferroptosis- and cuproptosis-related gene sets distinguished three upregulated hub genes, including FTH1 ( ferritin heavy chain 1 ), SOD2 ( superoxide dismutase 2 ), and CDKN2A ( cyclin-dependent kinase inhibitor 2A ) as key candidate regulators. These genes showed high module membership, significant differential expression in RA monocytes, and notable associations with immune infiltration patterns, including increased pro-inflammatory monocytes/macrophages and reduced regulatory T cells. Functional enrichment also highlighted oxidative stress response, iron and copper homeostasis, mitochondrial respiration, and cellular senescence. The single-cell analysis further showed that these hub genes are predominantly expressed in the RA synovial macrophages and fibroblasts, two major mediators of joint pathology. Together, these findings indicate that there may be an association between ferroptosis-related pathways and cuproptosis-related pathways in RA and suggest that FTH1 , SOD2 , and CDKN2A are candidate biomarkers and candidate therapeutic targets. Citation: Ahmad A, Khalil S, Law D, De los Ríos-Escalante PR, Abdel-Maksoud MA, Almutairi S, et al. (2026) Integrative network analysis identifies candidate genes shared between ferroptosis and cuproptosis pathways in rheumatoid arthritis pathogenesis. PLoS One 21(9): e0358176. https://doi.org/10.1371/journal.pone.0358176 Editor: Shantanu Gupta, UFRN: Universidade Federal do Rio Grande do Norte, BRAZIL Received: March 30, 2026; Accepted: August 21, 2026; Published: September 11, 2026 Copyright: © 2026 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data supporting the findings of this study are publicly available. The bulk RNA-sequencing and single-cell RNA-sequencing datasets are available from the NCBI Gene Expression Omnibus under accession numbers GSE294225 and GSE296117, respectively. All author-generated R scripts, curated gene lists, processed results, and source data underlying the figures and tables are openly available at https://github.com/Abbasahmad9080/RA_Biomarker_Analysis . No restrictions apply to data or code access. Funding: The work was funded by the Higher Education Department, Government of Khyber Pakhtunkhwa, Pakistan, under the Higher Education Research Endowment Fund (HEREF) Project-3111. This research was funded by the ongoing research funding program, (ORF-2026-2191), King Saud University, Riyadh, Saudi Arabia. The authors are also thankful to the Catholic University of Temuco, Chile, for its support through the MECESUP UCT 0804 project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. 1. Introduction Rheumatoid arthritis (RA) is a chronic, systemic inflammatory autoimmune disorder that primarily affects synovial joints, leading to progressive cartilage and bone destruction, functional disability, and an increased risk of mortality [ 1 ]. RA also imposes a substantial socioeconomic burden. In 2019, its global prevalence was estimated at 18 million cases, and the global age-standardized incidence rate was approximately 13.48 per 100,000 population in 2021 [ 2 ]. The disease shows a clear female predominance, with nearly 70% of the cases occurring in women, and it is more commonly reported in industrialized countries, possibly due to demographic factors, environmental exposures, and underdiagnosis in low- and middle-income regions [ 2 ]. The burden of RNA is projected to rise further by 2040, particularly among young adults (20–54 years) in lower-income settings, underscoring the need for a deeper understanding of its underlying mechanisms [ 3 ]. The pathogenesis of RA is highly complex and involves genetic susceptibility, environmental triggers such as smoking and infections, and dysregulation of both innate and adaptive immune responses, ultimately resulting in persistent synovial inflammation and extra-articular manifestations, including cardiovascular and pulmonary complications [ 4 ]. A central role is played by fibroblast-like synoviocytes (FLS), which acquire an aggressive, tumor-like phenotype, promote pannus formation, and secrete pro-inflammatory cytokines such as TNF-α and IL-6, thereby contributing to matrix degradation and joint damage [ 5 ]. In addition, T cells, B cells, macrophages, and neutrophils infiltrate the synovium and sustain a pro-inflammatory microenvironment by producing autoantibodies, including rheumatoid factor and anti-citrullinated protein antibodies, and by activating osteoclasts that drive bone erosion [ 6 ]. More recent studies have further highlighted the contributions of epigenetic alterations and metabolic reprogramming and have clarified how environmental influences interact with inherited risk factors, such as HLA-DRB1 alleles, to initiate and perpetuate disease progression [ 7 ]. Recent advances in multi-omics technologies and bioinformatics have shown that non-apoptotic regulated cell death (RCD) pathways may also play important roles in RA pathogenesis [ 8 ]. Apoptosis, a caspase-dependent process, is a key mechanism for maintaining immune homeostasis; however, resistance to apoptosis in FLS contributes to synovial hyperplasia [ 9 ]. Increasing evidence points to alternative RCD pathways, particularly ferroptosis and cuproptosis. Ferroptosis is an iron-dependent form of cell death characterized by lipid peroxidation, glutathione depletion, and inactivation of glutathione peroxidase 4 (GPX4), whereas cuproptosis is a copper-mediated form of cell death associated with mitochondrial proteotoxic stress, aggregation of lipoylated proteins, and loss of iron-sulfur cluster protein [ 10 , 11 ]. These pathways are especially relevant to RA, a disease strongly linked to oxidative stress, in which elevated reactive oxygen species (ROS), chronic synovial inflammation, and disturbed metal ion homeostasis, including iron overload and copper imbalance, may collectively promote cell injury, immune activation, and joint destruction [ 12 ]. Ferroptosis has been implicated in regulating FLS proliferation, chondrocyte death, and inflammatory responses in RA, and inflamed joints have been reported to exhibit altered iron distribution and increased lipid peroxidation [ 13 ]. Likewise, emerging evidence links copper-dependent toxicity and cuproptosis to RA progression, suggesting that mitochondrial dysfunction and proteotoxic stress in immune cells and synoviocytes may contribute to disease development, particularly in the context of the abnormal copper levels observed in patients with RA [ 11 ]. Taken together, the potential overlap between ferroptosis and cuproptosis through shared features such as mitochondrial dysfunction and disrupted metal homeostasis represents a promising and still underexplored framework for understanding the oxidative and metabolic basis of RA [ 14 ]. Integrative bioinformatic approaches are well-suited to dissect such complex biological interactions by combining transcriptomic and other high-dimensional datasets. Weighted gene co-expression network analysis (WGCNA) is a powerful systems biology method that constructs scale-free gene co-expression networks, enabling the identification of gene modules associated with clinical traits and of hub genes with potential biomarker or therapeutic relevance in RA [ 15 ]. Differential expression analysis using tools such as DESeq2 further helps identify genes with significant transcriptional alteration between RA and healthy samples [ 16 ]. In parallel, immune profiling using single-sample gene set enrichment analysis (ssGSEA) can estimate immune cell infiltration and functional status of immune cell populations from bulk RNA data, providing insight into immune signatures associated with disease activity in RA synovium or peripheral blood [ 17 ]. These strategies have already been successfully applied in RA research to characterize immune infiltration patterns, including increased macrophages and neutrophils, and to link these patterns to pathologic pathways [ 18 ]. However, the combined contribution of ferroptosis- and cuproptosis-related genes to RA, particularly in relation to immune dysregulation, remains insufficiently understood. In the present study, we used an integrative bioinformatics framework to investigate the role of metal-dependent regulated cell death in RA. Using the GSE294225 dataset, we first identified differentially expressed genes (DEGs), then applied WGCNA to detect disease-associated modules and hub genes. Ferroptosis- and cupeorptosis-related regulators were subsequently screened, and immune cell infiltration was assessed using ssGSEA. Finally, we explored the relationships between candidate genes and immune signatures to identify candidate biomarkers and candidate therapeutic targets associated with RA pathogenesis. 2. Materials and methods Fig 1 shows stepwise overview of the bioinformatics workflow employed in this study: (1) retrieval of the GSE294225 rheumatoid arthritis dataset and preprocessing, including quality control and normalization; (2) identification of differentially expressed genes (DEGs); (3) weighted gene co-expression network analysis (WGCNA) to identify key gene modules; (4) integration of ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs) to identify overlapping genes; (5) functional enrichment analyses, including GO, KEGG, and Reactome pathways; (6) immune infiltration analysis; (7) identification of potential therapeutic targets using DGIdb; (8) single-cell RNA sequencing (scRNA-seq) analysis; (9) external validation using independent datasets; and (10) visualization and statistical analysis of the results. 2.1. Dataset and preprocessing The Bulk RNA-sequencing data were obtained from the Gene Expression Omnibus (GEO) under the accession GSE294225, which contained 24 samples: 15 healthy controls (HC) and 9 patients with active rheumatoid arthritis (RA; DAS28 > 2.7). These samples are representative of CD14 + peripheral blood monocytes of the whole blood [ 19 , 20 ]. The initial count- matrix of 18,205 genes was run in R (v4.5.x) through the DESeq2 package (v1.50.2) [ 21 ]. Normalization has been performed using a variance-stabilizing transformation (VST), which tends to stabilize the variance relative to the mean expression range and is more appropriate in RNA-seq count data before proceeding to downstream network and visualization analysis [ 21 ]. Lowly expressed genes were filtered to those with mean normalized counts 1 were used to define the differentially expressed genes. This threshold has been broadly accepted in transcriptomic studies and allows for prioritizing genes showing biologically significant changes in expression over those with only small differences. In the differential expression analysis, both upregulation and downregulation genes were identified, but further analyses were performed on the upregulated genes to identify activated ferroptosis- and cuproptosis-related pathways in RA. The results were analyzed with volcano, and MA plots were created with ggplot2 and EnhancedVolcano [ 21 , 22 ]. 2.3. Weighted Gene Co-Expression Network Analysis (WGCNA) Weighted gene co-expression network analysis (WGCNA) was performed with the WGCNA package (v1.74) of R [ 15 ] to identify gene modules related to rheumatoid arthritis (RA), in which the pickSoft Threshold function was used to identify the ideal soft-thresholding power (b = 9) to achieve scale-free topology (R2 > 0.85). Based on the expression data, variance-stabilizing transformation (VST)-normalized data were used to obtain a signed weighted adjacency matrix, which was then converted to a topological overlap matrix (TOM) for hierarchical clustering. Dynamic hybrid tree cutting with parameters minModuleSize = 30, deepSplit = 2, and mergeCutHeight = 0.25 identified gene modules. The eigengenes of each module (Mes), i.e., the first principal components of the module’s expression profiles, were then subjected to Pearson correlation analysis with the binary RA trait (RA = 1, HC = 0). The module with the largest absolute correlation coefficient with the RA phenotype was regarded as the key module. Lastly, candidate hub genes were selected based on predefined criteria, including high module membership (MM > 0.8) and high gene significance (GS > 0.5; p < 0.05). Genes not meeting these thresholds were excluded from subsequent analyses. 2.4. Identification of Ferroptosis- and Cuproptosis-related genes The ferroptosis-related genes were downloaded from FerrDb V3 database containing ferroptosis-associated genes as divided into Driver genes, Suppressor genes, and Ferromarker genes. Some genes were found in more than one category, so a total of 1,519 non-redundant ferroptosis-related genes were ultimately obtained by selecting unique gene symbols. The full list of ferroptosis genes is shown in Supplementary Table S1 in S1 File (Excel file) [ 23 , 24 ]. The genes associated with cuproptosis were selected from published literature of genes involved in copper-induced cell death and cuproptosis regulation, with the important regulators of cuproptosis, FDX1, DLAT, and CDKN2A. To ensure maximal representation of the genes currently known to be involved in cuproptosis, additional genes were identified from studies published in the last three years (2022–2025). In total, 177 genes associated with cuproptosis were analyzed. The full list of genes, literature citations, and inclusion criteria are listed in Supplementary Table S2 in S2 File (Excel file) [ 25 – 27 ]. Venn diagram analysis showed that ferroptosis and cuproptosis shared common genes (~41 genes) [ 11 , 28 ]. Intersectively selected candidate genes were those that were significantly upregulated in the DEG list, in the top WGCNA module, and that overlapped the FRG/CRG gene lists. To determine the number of intersections, base R functions or the VennDiagram package were used [ 29 ]. 2.5. Functional enrichment analysis The analyses of functional enrichment were performed in R with the help of clusterProfiler (v4.16.0) [ 30 ]. The org. Hs. e.g., the db package was used to convert gene symbols to Entrez IDs [ 31 ]. The over-representation analysis (ORA) was used to perform gene ontology (GO) enrichment for biological processes, molecular functions, and cellular components, and the KEGG pathway analysis was performed at a significance level of p < 0.05 and adjusted p < 0.05 (Benjamini-Hochberg correction). Enrichplot and ggplot generated dot plots, bar plots, and network diagrams of the enrichment as described previously [ 30 , 32 ]. Reactome pathway enrichment was conducted using ReactomePA [ 33 ]. KEGG pathway enrichment was performed using clusterProfiler. A significance threshold of adjusted p < 0.05 was applied. 2.6. Immune infiltration analysis A comprehensive set of immune cell gene signatures was selected based on established immunology literature and validated databases (IMMGEN, TCGA, ImmuneSig) [ 34 ]. The estimate of immune infiltration was performed using marker gene signatures for 18 distinct immune cells ( Table 4 ) [ 35 , 36 ]. The infiltration score per immune cell type and sample was calculated as the average normalized expression of marker genes. The rank correlation coefficients for the expression of giants and immune cell infiltration were determined using Spearman’s correlation. T-tests compared the distributions of scores in RA and HC. Diversity indices, including Shannon (H = −∑ pi ln(pi)), were used to measure immune diversity. 2.7. Potential drug target identification Potential therapeutic drug targets associated with the identified hub genes were examined using the Drug-Gene Interaction Database (DGIdb). This online database stores experimentally validated and predicted interactions of drugs and genes. These databases were queried using the hub genes identified by the integrative analysis to identify compounds with known or potential regulatory interactions. Data on drug name, regulatory approval, therapeutic indication, and interaction score were obtained for each gene-drug pair. The resulting interactions were filtered to prioritize approved drugs and compounds with higher interaction scores, enabling the identification of potential candidate therapeutic agents that can be repurposed to treat rheumatoid arthritis by interacting with the hub genes. 2.8. Single-cell RNA-seq analysis The scRNA-seq data from rheumatoid arthritis synovial fluid were obtained in the GEO dataset GSE296117 and processed in the Seurat package in R to visualize the dimensionality reduction and cell distribution using DimPlot. The expression of hub genes (CDKN2A, SOD2, and FTH1) across cell groups was investigated using FeaturePlot, and DotPlot was used to show the mean expression and the proportion of cells expressing each gene in each cluster. VlnPlot was also applied to measure heterogeneity in gene expression across cell populations and to assess the distribution of heterogeneity of the hub gene expression at the single-cell level. 2.9. External validation Candidate gene expression was validated in four independent GEO datasets: GSE55235 (GPL96, synovial tissue, n = 30), GSE55457 (GPL96, synovial tissue, n = 33), GSE7729
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