Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis

Peng-fei Han; Wei-rong Cui; Fang-zheng He; Tao Wu; Chang-sheng Liao

doi:10.1371/journal.pone.0339397

. 2026 Jan 2;21(1):e0339397. doi: 10.1371/journal.pone.0339397

Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis

Peng-fei Han ^1,^‡, Wei-rong Cui ^2,^‡, Fang-zheng He ^1,², Tao Wu ^1,², Chang-sheng Liao ^1,^*

Editor: Srinivas Mummidi³

PMCID: PMC12758813 PMID: 41481752

Abstract

Objective

This study employs bioinformatics analysis with the objective of identifying commonly differentially expressed genes (DEGs) in ulcerative colitis (UC) and rheumatoid arthritis (RA), as well as exploring their underlying molecular mechanisms. By doing so, it aims to provide a theoretical basis for investigating the potential associations between these two diseases and developing novel therapeutic strategies.

Materials and methods

We downloaded multiple gene expression datasets for Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) from the Gene Expression Omnibus (GEO) database. For RA, GSE77298, GSE12021, and GSE55457 were selected as the training sets, with GSE89408 serving as the validation set. For UC, GSE36807, GSE87473, and GSE92415 were chosen as the training sets, and GSE13367 as the validation set.During data processing, we first merged the RA and UC data from each training set with standardized data, eliminated batch effects, and obtained combined datasets of differentially expressed genes (DEGs). Subsequently, we conducted a cross-analysis of the DEGs from RA and UC to identify commonly up-regulated and down-regulated genes. To gain a deeper understanding of these DEGs, we constructed a protein-protein interaction (PPI) network and identified hub genes.For further analysis of these hub genes, we utilized the GENEMANIA platform to obtain functional annotations and interaction information. Finally, we validated our analysis results using the GSE89408 and GSE13367 datasets.

Results

After a thorough analysis of the differentially expressed genes in the cells of patients with rheumatoid arthritis (RA) and ulcerative colitis (UC) we found that genes such as CCR7, CD19, CXCL13, CXCR4, and SELL were significantly up-regulated, suggesting their crucial roles in the pathology of both diseases. This discovery not only underscores the importance of these genes as biomarkers for the differential diagnosis of RA and UC, but also highlights key nodes worthy of further validation. In the future, it may be possible to slow or halt disease progression by modulating the expression of these genes.

Conclusion

The results of this study reveal potential common molecular mechanisms underlying rheumatoid arthritis (RA) and ulcerative colitis (UC). The key target genes CCR7, CD19, CXCL13, CXCR4, and SELL highlight common underlying factors associated with both diseases. Further investigation and exploration of these findings can pave the way for new candidate targets and directions in therapeutic research aimed at treating RA and UC. This study emphasizes the importance of utilizing bioinformatics approaches to uncover the mechanisms of complex diseases, providing a promising pathway for the development of more effective and targeted treatments.

1. Introduction

Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) are two typical and prevalent autoimmune diseases that have long been the focus of medical research and clinical treatment [1,2]. These diseases not only severely impact patients’ quality of life but also pose challenges for treatment due to their complex pathogenesis and diverse clinical manifestations [3]. Rheumatoid Arthritis is a chronic, systemic autoimmune disease that primarily affects the synovial membrane of joints, leading to joint inflammation, pain, swelling, and dysfunction [4,5]. Its pathogenesis involves abnormal activation of various immune cells and autoimmune reactions, resulting in persistent inflammation and destruction of the synovial membrane [6]. Inflammatory bowel disease (IBD) encompasses two subtypes: ulcerative colitis (UC) and Crohn’s disease (CD). Unlike CD, which can affect the entire gastrointestinal tract, the inflammation in UC is confined to the colonic mucosa. Epidemiological data indicate that UC is more prevalent than CD globally [7]. Therefore, our study primarily focuses on exploring the association between rheumatoid arthritis (RA) and UC. As a chronic, non-specific inflammatory bowel disorder, UC predominantly affects the mucous membrane of the large intestine, leading to symptoms such as diarrhea, abdominal pain, and mucopurulent bloody stools. The exact etiology of UC remains incompletely understood, but it is currently believed to result from a combination of multiple factors, including genetic predisposition, environmental triggers, immune dysregulation, and alterations in the gut microbiota [8,9].

Research has demonstrated that patients with ulcerative colitis (UC) face a significantly higher risk of developing rheumatoid arthritis (RA) compared to those with other intestinal diseases. During the course of UC, some patients may exhibit extraintestinal manifestations such as arthritis, which is commonly referred to as “enteropathic arthritis” or “arthritis associated with ulcerative colitis” [10]. Despite the distinct anatomical sites and clinical presentations of RA and UC, studies have confirmed that both diseases involve characteristic abnormal activation of the immune system and chronic inflammatory responses (e.g., Fcγ receptor signaling). Additionally, there is significant genetic overlap between RA and UC in the IL-23/Th17 pathway, suggesting that they may share common genetic and molecular mechanisms [11–13]. Therefore, joint research on RA and UC is of paramount importance, as it can enhance diagnostic accuracy, optimize treatment strategies, and provide more effective therapeutic approaches for patients.

Bioinformatics, as an interdisciplinary field, provides powerful support for elucidating the genetic and molecular mechanisms of complex diseases by integrating knowledge and technologies from biology, computer science, and statistics [14]. In recent years, with the rapid development of bioinformatics techniques, in-depth analysis of gene expression differences between RA and UC has become feasible, providing a powerful tool for revealing their pathogenesis and identifying new therapeutic targets. The aim of this study is to use bioinformatics methods to conduct a joint research on Rheumatoid Arthritis and Ulcerative Colitis, revealing their differences and similarities at the gene expression level, identifying new biomarkers and potential disease-related genes, and deepening our understanding of RA and UC. These findings will not only contribute to a deeper understanding of the pathogenesis of RA and UC but may also provide new ideas for early diagnosis, prognosis assessment, and formulation of treatment strategies for these diseases.

In summary, this study will utilize bioinformatics methods to comprehensively analyze the gene expression data of RA and UC, aiming to reveal their differences and similarities at the gene expression level and provide new perspectives and clues for in-depth research and treatment of these diseases.

2. Materials and methods

2.1. Data Acquisition: In this study, we primarily utilized microarray technology for gene expression analysis and obtained datasets related to Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) from the Gene Expression Omnibus (GEO) public database.,We selected datasets with a sample size greater than 20 to ensure sufficient statistical power for reliably identifying differentially expressed genes (DEGs). Only datasets with clearly defined disease groups and control groups were included to avoid phenotypic confounding.We searched and extracted datasets using the following keywords: “Rheumatoid Arthritis,” “Ulcerative Colitis,” “microarray,” “human samples,” and corresponding disease-specific gene expression patterns. The datasets included in our analysis were RA (GSE77298, GSE12021, and GSE55457 as the training set, with GSE89408 used as the validation dataset) and UC (GSE36807, GSE87473, and GSE92415 as the training set, with GSE13367 used as the validation set). The GSE77298 (RA) dataset contains 23 samples, including 16 disease samples and 7 control samples; GSE12021 (RA) contains 21 samples, with 12 disease samples and 9 control samples; GSE55457 (RA) contains 23 samples, with 13 RA samples and 10 control samples; GSE36807 (UC) contains 22 samples, including 15 disease samples and 7 control samples; GSE87473 (UC) contains 127 samples, with 106 disease samples and 21 control samples; GSE92415 (UC) comprises 183 samples collected before (n = 87) and after (n = 75) treatment. It was originally generated to evaluate the efficacy of golimumab (GLM) induction therapy in moderate-to-severe ulcerative colitis. For our analysis, we selected the 87 pre-treatment UC samples together with 21 healthy control samples. Detailed age group and gender information were not available for all samples due to limitations in the data collection process. We acknowledge that parameters such as age, gender, treatment conditions, and comorbidities may exert potential confounding effects on gene expression profiles and serve as important background variables in certain analyses. However, our primary objective is to minimize batch effects across datasets through rigorous normalization procedures, thereby identifying differentially expressed genes (DEGs) associated with rheumatoid arthritis and ulcerative colitis, and exploring how these genes contribute to the progression of these diseases. Therefore, we have decided to include the aforementioned datasets in our study.Detailed sample information and dataset characteristics for the RA and UC cohorts are summarized in S1 and S2 Tables, respectively.

2.2. Data Preprocessing and DEG Identification: We separately performed preprocessing on six gene expression datasets. Specifically, GSE77298, GSE12021, and GSE55457 were utilized for the analysis of rheumatoid arthritis, whereas GSE36807, GSE87473, and GSE92415 were employed for the analysis of ulcerative colitis. To ensure the quality of the data, we excluded genes (rows) or samples (columns) with a relatively high proportion of missing values. In cases where multiple probes corresponded to the same gene within a dataset, we calculated the arithmetic mean of their expression levels. This approach was taken to eliminate redundancy and streamline the subsequent analysis procedures. Subsequently, the processed expression matrices were normalized to rectify experimental batch effects and other systematic biases, thereby ensuring comparability across different datasets. Based on these preprocessed data, we utilized the limma package, which runs within the RStudio environment, to conduct differential expression analyses on the RA and UC groups in comparison to the normal control group. The limma package is well-suited for handling high-throughput expression data generated by microarray and RNA sequencing technologies. It can effectively estimate the fold change in gene expression and compute its statistical significance. We established stringent screening criteria: an adjusted P-value (adj.P.Val) < 0.05 and |log₂ fold change (FC)| > 1. By setting these criteria, we were able to control the false discovery rate and simultaneously identify differentially expressed genes that were not only statistically significant but also held potential biological implications.

2.3. Batch Effect Correction and Differential Analysis: To mitigate batch effects that may arise from different experimental platforms, this study employed batch correction algorithms. We merged the standardized expression datasets from RA (GSE77298, GSE12021, and GSE55457) and UC (GSE36807, GSE87473, and GSE92415) respectively, and utilized the ComBat function provided by the sva package in RStudio to adjust for batch effects. The datasets generated before and after this normalization for RA and UC are available in S1–S4 Files.Subsequently, we used the ggpubr package to visualize the batch-corrected data. After correction, we conducted differential expression gene analysis using the limma package to identify differentially expressed genes with statistical significance in the merged datasets.

2.4. Screening of Common DEGs in RA and UC: After batch effect correction and differential expression analysis, DEGs in the RA and UC datasets were identified. A cross-analysis of the DEG sets for the two diseases enabled us to successfully identify common DEGs, including both upregulated and downregulated genes.

2.5. Construction and Analysis of Protein-Protein Interaction (PPI) Network and Selection of Hub Genes: Using the STRING online platform, we constructed a human PPI network containing 88 common DEGs from Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC), focusing on association data for Homo sapiens. To ensure the credibility of protein interactions within the network, we established a minimum interaction score threshold of 0.400. Subsequently, the network was analyzed using the CytoHubba plugin in Cytoscape software (version 3.10.2) to identify hub genes. This analysis employed four different centrality algorithms—Degree, Closeness Centrality, Betweenness Centrality, and Maximal Clique Centrality(MCC)—to effectively detect genes playing a central role in the network. This multifaceted approach allowed us to identify hub genes that have a significant impact on the network’s structure and function, circumventing the limitations of relying on a single network feature for gene identification and ensuring that the identified hub genes were validated in terms of their importance in the network from multiple perspectives.

2.6. GENEMANIA Online Analysis: To further investigate the interactions and functional associations between the ten hub genes selected in this study, we used the advanced gene function prediction tool GENEMANIA. By entering the names of these ten hub genes on the GENEMANIA website and selecting the appropriate species “Homo sapiens,” an interaction network diagram was automatically constructed based on their known interactions and functional associations.

2.7. Independent Validation of DEGs Using Additional Datasets: During the validation phase, we utilized the public GSE89408 dataset for RA validation and the GSE13367 dataset for UC validation. Prior to conducting differential expression analysis, necessary preprocessing steps were performed, including normalization, to minimize technical variations and ensure data comparability. The differential expression analysis aimed to identify significantly differentially expressed genes (DEGs) between the control group and each disease-specific cohort (RA and UC). Subsequently, we specifically examined the overlap between the DEGs identified in these datasets and the 10 hub genes previously identified in our preliminary analysis. To systematically evaluate the diagnostic potential of these 10 hub genes, we further performed receiver operating characteristic (ROC) curve analysis in the validation sets to quantify their ability to distinguish disease states. Meanwhile, leave-one-out cross-validation was employed to assess the robustness of the classification model constructed based on these genes.

2.8. Characterization of immune cell infiltration: To assess the potential influence of tissue heterogeneity and immune cell infiltration on hub genes, we performed quantitative immune cell infiltration analysis on RA and UC data using the CIBERSORT algorithm with its LM22 signature matrix. Based on linear support vector regression, this method resolves the relative proportions of 22 immune cell subsets. To further distinguish whether hub gene expression is driven by immune cell abundance or stems from intrinsic tissue pathological responses, we calculated Pearson correlation coefficients between each hub gene’s expression level and all immune cell proportions, accompanied by significance testing. This analysis aims to biologically differentiate genes directly associated with immune infiltration from those potentially representing core genes involved in key disease pathways.

2.9. Subcellular Localization Analysis: To gain deeper insights into the functional localization and potential mechanisms of the hub genes within cells, we performed a systematic subcellular localization analysis of the five selected hub genes (SELL, CCR7, CD19, CXCL13, and CXCR4). The relevant data were obtained from the Human Protein Atlas database, with batch downloading and processing conducted using the HPAanalyze package in RStudio. This database encompasses protein localization information for over 15,000 human genes. Based on the primary subcellular localization of their encoded proteins, these genes were categorized into the following four classes: membrane proteins (primarily localized to the plasma membrane), secreted proteins (mainly secreted into the extracellular space), cytoplasmic proteins (primarily localized in the cytoplasm), and nuclear proteins (primarily localized in the nucleus).

2.10. Constructing the CeRNA Regulatory Network: Potential interactions between core genes and lncRNAs (long non-coding RNAs) or miRNAs (microRNAs) were retrieved from four authoritative databases: miRanda, miRDB, miRTarBase, and TargetScan. These databases are highly reputable in the field of miRNA target prediction, each employing distinct algorithms and data sources to predict interactions between miRNAs and their target genes. The selection of these four databases as references was based on their ability to provide multi-dimensional data support, thereby enhancing the reliability and comprehensiveness of the prediction results.After obtaining predictions from these four databases, rigorous cross-validation was performed. Cross-validation is a critical bioinformatics analysis method used to evaluate the accuracy and consistency of predictions by comparing results derived from different data sources or algorithms. In this study, only lncRNA–miRNA pairs that were consistently recorded and mutually supported across all four databases—miRanda, miRDB, miRTarBase, and TargetScan—were retained, thereby strengthening the reliability and consistency of the predicted outcomes.Subsequently, an in-depth analysis was conducted to examine the interactions between these validated miRNAs and the hub genes. Based on the filtered interaction pairs, a ceRNA regulatory network was constructed using Cytoscape 3.10.2.

2.11. Statistical Methods, Software, and Tools: All statistical analyses were performed in the R environment. The two-sample t-test was used to analyze differences in gene expression between groups. The Benjamini-Hochberg method was employed for multiple testing correction to control the false discovery rate (FDR). Data processing and statistical analysis were conducted using R language (version 4.3.1).The intermediate analysis files and results generated during the above statistical procedures are available in S1 Data.

3. Results

3.1. Identification of Differentially Expressed Genes (DEGs): After analysis, a series of differentially expressed genes (DEGs) were identified across different datasets. Specifically, the GSE36807 dataset revealed 471 DEGs in the uc group, the GSE87473 dataset showed 928 DEGs in the uc group, and the GSE92415 dataset indicated 1020 DEGs in the uc group. For the RA group, the GSE77298 dataset displayed 432 DEGs, the GSE12021 dataset exhibited 235 DEGs, and the GSE55457 dataset demonstrated 314 DEGs.

3.2. Batch Correction and Differential Expression Analysis: After standardization procedures, we initially integrated the annotated expression datasets for the RA group (GSE77298, GSE12021, and GSE55457) and the UC group (GSE36807, GSE87473, and GSE92415). Subsequently, we eliminated batch effects from the combined datasets. The subsequent differential expression analysis revealed that there were 416 DEGs in the RA group and 778 DEGs in the UC group.The complete lists of differentially expressed genes identified in the RA and UC analyses are provided in S5 and S6 Files, respectively (Figs 1–6).

Fig 1 — 1a represents the combined datasets of RA before batch correction. 1b displays the merged RA dataset post-batch correction.

Fig 2 — Red indicates high expression levels, while blue indicates low expression levels.

Fig 5 — Red signifies high expression levels, while blue signifies low expression levels.

3.3. Selection of Commonly Expressed Genes in RA and UC: We conducted differential expression analysis on the combined and batch-effect-corrected datasets for rheumatoid arthritis (RA) and ulcerative colitis (UC) groups, obtaining their respective differentially expressed genes (DEGs). By intersecting the two sets of DEGs, we precisely identified 77 upregulated genes and 11 downregulated genes. The upregulated genes include CXCL13, CXCL10, PSMB9, CXCL9, AIM2, IGHM, SNX10, BIRC3, IGLC1, SEMA4A, IGLJ3, KMO, CXCL11, CD72, GBP1, GZMH, TNFSF11, GZMK, TRBC1, PNOC, EVI2B, CCL18, PLA2G2D, CFB, CYTIP, CD79A, ITGB2, LAMP3, CXCR4, MS4A1, LAX1, WNT5A, ISG20, RASGRP1, IL7R, IGHG1, LCP1, FKBP11, RHOH, FAIM3, IGHD, CD19, MMP1, PIM2, LOXL1, RAC2, SLAMF7, SPP1, SPAG4, GZMB, KIAA0125, CHI3L2, TNIP3, CR2, PLA2G7, CORO1A, CXCL6, IGLV6–57, CD38, CCL19, SEMA4D, RGS1, CCR7, MMP3, TNFAIP6, ZBED2, AQP9, FCGR1B, P2RX5, SERPINA1, LILRA3, HS3ST3A1, BCL2A1, ICOS, MMP9, SELL, LTF. The 11 downregulated genes are AKR1B10, TRHDE, CYP4F12, PDE6A, MAOA, PCK1, NPY1R, EIF1AY, RPS4Y1, MB, and ADH1C (Fig 7).

Fig 7 — 7a displays the 77 commonly upregulated differentially expressed genes (DEGs) identified in both RA and UC. 7b shows the 11 commonly downregulated DEGs identified in both RA and UC.

3.4. Construction of Protein-Protein Interaction (PPI) Network and Selection of Hub Gene: In this study, a protein-protein interaction (PPI) network was constructed based on the common differentially expressed genes (DEGs) identified in rheumatoid arthritis (RA) and ulcerative colitis (UC). As shown in the following figure, the network comprises 88 shared DEGs, with disconnected nodes in the network hidden. It is represented by 88 nodes and 345 edges, where the nodes signify proteins encoded by DEGs and the edges represent interactions between these proteins. Utilizing Cytoscape software and the Degree, Closeness Centrality, Betweenness Centrality, and Maximal Clique Centrality (MCC) algorithms provided by the cytohubba plugin, along with summing their scores, the top ten key hub genes were identified: CXCL13, CCR7, CCL19, CD19, GZMB, CXCL9, CD38, CXCR4, IL7R, and SELL. The complete lists of hub genes identified at confidence thresholds of 0.300, 0.400, and 0.500, as well as their intersection, are provided in S7–S10 Files, respectively.The positions of these hub genes within the network and the intensity of the node colors reflect their connectivity and importance within the network,Furthermore, ROC analysis demonstrated that these hub genes exhibit strong discriminatory power in distinguishing disease states. Leave-one-out cross-validation revealed highly consistent discriminatory performance of RA-associated hub genes across different datasets. The AUC values from the full dataset analysis and leave-one-out validation showed strong correlation (points closely distributed along the diagonal), indicating that the discriminatory capability of these genes is not dependent on specific datasets. For UC-associated hub genes, the leave-one-out validation results showed good reproducibility. Although the point distribution was relatively scattered, most genes maintained effective discriminatory ability (AUC > 0.8) when excluding any single dataset. Notably, the robustness of UC hub genes exhibited some heterogeneity: some genes remained significant in all three validation rounds, while others showed fluctuating significance across different data subsets. This variation may reflect the impact of UC disease heterogeneity on gene expression, yet the overall discriminatory performance of the core gene set was validated.The detailed results of the ROC analysis for hub genes in both rheumatoid arthritis and ulcerative colitis are provided in S11 and S12 Files, respectively (Figs 8–12).

Fig 12 — 12b Variability of gene expression changes (log2FC) across different datasets.

Fig 9 — 9a highlights the module of 10 identified central genes based on the degree metric. 9b showcases the closeness centrality of the genes. 9c illustrates the betweenness centrality of the genes. 9d presents a refined view of the Maximal Clique Centrality (MCC) of the genes.

Fig 10 — 10a:Validation of the Diagnostic Efficacy of Hub Genes in rheumatoid arthritis (RA).10b:Validation of the Diagnostic Efficacy of Hub Genes in ulcerative colitis (UC).

Fig 11 — 11b Variability of gene expression changes (log2FC) across different datasets.

3.5. GENEMANIA Online Analysis: Analysis of the 10 hub differentially expressed genes (DEGs) (CXCL13, CCR7, CCL19, CD19, GZMB, CXCL9, CD38, CXCR4, IL7R, and SELL) on the GeneMANIA website revealed their central roles in various immune and inflammation-related biological processes. These genes are either directly involved or indirectly participate through interactions with other genes in immune cell activation, recruitment, and migration. The core processes identified by the analysis include leukocyte migration, leukocyte chemotaxis, cell chemotaxis, neutrophil migration, cellular response to biological stimuli, and cellular response to bacterial-derived molecules. This analysis underscores the pivotal roles of these hub genes in coordinating and driving immune cell migration, inflammatory responses, and responses to pathogen/damage signals. It suggests that the networks regulated by these genes represent potential mechanistic links between the pathophysiology of RA and UC, directly mediating tissue damage caused by immune cell infiltration under chronic inflammatory conditions. The extensive involvement of these genes in critical biological processes highlights their potential as therapeutic targets for modulating underlying disease processes and alleviating symptoms (Fig 13).

3.6. Independent Validation of DEGs Using Additional Datasets: When performing validation analysis using the specified validation datasets, a threshold of p < 0.01 was selected to determine significant differences. This analysis aimed to validate the differential expression patterns of specific genes of interest compared to normal control groups in the context of RA and UC. The results of the validation analysis revealed significant differences in the expression levels of the CCR7, CD19, CXCL13, CXCR4, and SELL genes between the disease groups (RA and UC) and the normal control group. More specifically, compared to normal controls, the CCR7, CD19, CXCL13, CXCR4, and SELL genes exhibited significantly elevated expression levels in the RA and UC disease groups, indicating an upregulated state. The higher expression levels of these genes in RA and UC highlight their potential involvement in disease progression or inflammatory processes. The validation analysis emphasizes the importance of these genes as potential biomarkers or therapeutic targets for RA and UC. The upregulation of these genes suggests their role in disease exacerbation or as key players in potential pathogenic mechanisms, positioning them as possible targets for therapeutic interventions aimed at reducing their expression or activity in the context of the disease (Figs 14–18).

3.7. Characterization of immune cell infiltration: Given the confounding effects of immune cell infiltration,we systematically conducted immune infiltration association analyses for the five identified hub genes (SELL, CCR7, CD19, CXCL13, CXCR4) in both RA and UC cohorts. The results revealed distinct immune microenvironment patterns between the two diseases and clarified the potential roles of each hub gene within these contexts.In UC, CIBERSORT analysis demonstrated significant inter-sample heterogeneity in immune infiltration. Based on this, the five hub genes could be clearly categorized into two groups according to their correlations with immune cell abundance, reflecting their different functional emphases in the disease process.Genes closely associated with B-cell immune responses (CD19, CXCL13, SELL, CCR7):The expression of CD19 and CXCL13 showed highly significant positive correlations with infiltration levels of B-cell lineages. For instance, CD19 correlated strongly with naive B cells (r = 0.599, p < 3.49e-26), plasma cells (r = −0.380, p < 3.30e-10), and follicular helper T cells (r = 0.513, p < 1.67e-18). CXCL13 was also significantly associated with memory B cells (r = 0.315, p < 2.83e-7) and follicular helper T cells (r = 0.480, p < 4.16e-16). These data strongly suggest that the hub status of CD19 and CXCL13 largely reflects the active aggregation of germinal center reactions and adaptive humoral immunity in UC mucosa.SELL and CCR7, key mediators of lymphocyte homing and migration, showed significant positive correlations with various immune cells (e.g., naive B cells, activated CD4 memory T cells, follicular helper T cells). However, they also exhibited strong negative correlations with M2 macrophages. This indicates that SELL and CCR7 not only participate in recruiting lymphocytes to intestinal inflammatory sites but also correlate with fluctuations in the immunosuppressive microenvironment, potentially playing a dual role in coordinating both the “advance” and “retreat” of immune responses.Gene relatively independent of immune infiltration (CXCR4):In stark contrast to the other four genes, CXCR4 expression showed no significant or only weak correlations with most immune cell subsets. Its correlations with all T-cell subsets, NK cells, and monocytes were non-significant (p > 0.05). The only notable correlation was a negative association with M2 macrophages (r = −0.566, p < 5.15e-23) and connections with other non-immune pathways. This crucial evidence suggests that CXCR4 may primarily participate in core pathological responses of intrinsic intestinal cells (such as epithelial or stromal cells), including cell migration and survival, with effects relatively independent of variations in immune cell numbers across samples.In RA, we observed a markedly different pattern: the immune infiltration profile of RA synovial tissue showed high consistency across samples, lacking the significant heterogeneity observed in UC. Accordingly, correlation analysis yielded a definitive conclusion: the expression levels of all five hub genes (SELL, CCR7, CD19, CXCL13, CXCR4) showed no statistically significant associations with the proportions of any of the 22 immune cell subsets analyzed (all p-values substantially > 0.05). Specifically, even genes highly correlated with immune cells in UC showed no significant correlations in RA. This result excludes confounding effects of inter-sample variations in immune cell composition on gene expression measurements.This key finding indicates that in RA, the expression changes of our identified hub genes are not driven by the abundance of infiltrating immune cells. Instead, they are more likely to represent core pathogenic molecular programs that are stably activated within intrinsic synovial tissue cells (such as fibroblast-like synoviocytes) during the disease state. Since the expression of these genes is unaffected by fluctuating immune infiltration proportions, this significantly enhances their potential as reliable disease biomarkers or therapeutic targets.The complete immune cell expression profiles for the hub genes in rheumatoid arthritis and ulcerative colitis are provided in S13 and S14 Files, respectively (Figs 19 and 20).

Fig 19 — 19a Boxplot of the Infiltration Proportions of 22 Immune Cell Types in Rheumatoid Arthritis 19b Heatmap of Correlation between Hub Genes and Immune Cell Infiltration.

Fig 20 — 20a Boxplot of the Infiltration Proportions of 22 Immune Cell Types in Ulcerative Colitis 20b Heatmap of Correlation between Hub Genes and Immune Cell Infiltration.

3.8. Subcellular Localization Characteristics of Hub Genes: Systematic subcellular localization analysis of the five hub genes revealed a distinct compartmentalization pattern: four genes (SELL, CCR7, CD19, and CXCR4, representing 80% of the total) displayed predominant plasma membrane localization. This marked membrane localization preference indicates their encoded proteins primarily function as surface receptors or adhesion molecules. CXCL13 was identified as the sole secreted protein (20%), mainly localized to the extracellular space—a finding fully consistent with its established role as a chemokine and indicative of its involvement in immune microenvironment modulation through paracrine signaling.Reliability assessment confirmed high confidence in the localization data (Table 1): 80% of the genes (SELL, CCR7, CXCL13, and CXCR4) were supported by “Enhanced” level evidence, while the remaining gene (CD19) received “Supported” level validation. These high-quality data establish a solid foundation for subsequent mechanistic investigations.

Table 1. Subcellular Localization Analysis of Hub Genes.

Gene	Main_Location	Reliability	Location_Category
SELL	Plasma membrane	Enhanced	Membrane
CCR7	Plasma membrane	Enhanced	Membrane
CD19	Plasma membrane	Supported	Membrane
CXCL13	Secreted	Enhanced	Secreted
CXCR4	Plasma membrane	Enhanced	Membrane

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3.9. Constructing the CeRNA Regulatory Network: We constructed a ceRNA regulatory network comprising 6 lncRNAs, 4 miRNAs, and 2 core genes, forming a total of 12 regulatory relationships.The complete ceRNA regulatory network is visualized in S1 Fig. Notably, the regulatory axis centered around hsa-miR-622–CXCR4/CCR7 suggests that immune cell migration may represent a shared pathological basis for these two autoimmune diseases. Future studies could experimentally validate the expression levels of key nodes in this network in tissues of RA and UC patients, as well as their impact on inflammatory responses, thereby providing a theoretical foundation for the development of targeted ceRNA therapeutic strategies.

4. Discussion

Statistically, the number of patients with rheumatoid arthritis (RA) and ulcerative colitis (UC) worldwide is showing an upward trend [15,16]. Although UC and RA are two distinct diseases, they share certain connections. Both are related to abnormalities in the immune system, and thus may occur simultaneously in some cases. Studies have shown that patients with UC may have an increased risk of developing RA [17]. This may be because, in some instances, the same immune abnormalities can lead to the onset of both diseases. However, not all UC patients will develop RA, which depends on individual differences and whether timely and effective treatment is received. Recent research progress has revealed overlapping characteristics in the pathogenesis of RA and UC [18]. To investigate these potential links, we employed bioinformatics techniques to analyze the expression patterns of specific genes under both conditions. The results showed higher expression levels of CCR7, CD19, CXCL13, CXCR4, and SELL genes in patients with RA and UC. Our research underscores the pivotal significance of these genes in unraveling the intricate interplay between the shared and distinct pathological processes underlying these autoimmune diseases.

CCR7, as a crucial G-protein-coupled receptor (GPCR), plays a vital dual role in immune responses. It serves as a navigational receptor for leukocyte migration, sensitively perceiving concentration gradients of chemokines and other chemoattractants, and accurately guiding the migration path of individual cells and even cell collectives. More importantly, CCR7 also functions as a builder and regulator of chemotactic gradients, cleverly modulating the spatial and temporal distribution of chemokines by internalizing its specific ligand CCL19, thereby enhancing or prolonging the chemotactic gradient effect. This unique mechanism endows dendritic cells (DCs) with the ability to maintain long-distance navigation and drives the formation of complex and variable collective migration patterns, which can flexibly adapt to the size and shape of different environments while providing essential navigational information for accompanying cells [19]. In Sjögren’s syndrome, the expression of CCL19/CCR7 is significantly elevated in salivary gland tissue [20]. Research on multiple sclerosis indicates that cerebrospinal fluid levels of CCL19 correlate with the numbers of T cells and CCR7 + dendritic cells. Furthermore, in experimental autoimmune encephalomyelitis models, CCR7 signaling is involved in immune cell recruitment and the regulation of Th1/Th17 responses [21]. Studies on psoriasis have also reported an association between increased levels of CCR7 + T lymphocytes and disease persistence [22].Studies have shown that in the synovial microenvironment of RA, the maturity of DCs increases, manifested by high expression of CCR7 and other maturation markers, accompanied by a metabolic shift towards glycolysis [23]. Furthermore, research by Emma Probst Brandum et al. further revealed that CCR7 antagonists may be beneficial in preventing the recruitment of immune cells to inflamed tissues, thereby preventing autoimmune reactions from worsening in the early stages of diseases such as RA, suggesting that in inflammatory diseases like RA, CCR7 may participate in and promote disease progression, and its expression may be relatively high or at least expressed in certain cell types (such as DCs) [24]. For UC, studies have also shown that CCR7 may play an important role. Frank Autschbach et al. tested new reagents on cryosections of normal human intestines, tonsils, and livers, and analyzed inflamed intestinal tissues from patients with Crohn’s disease and UC, finding that CCR7 expression was upregulated in inflamed intestinal tissues of UC patients, further suggesting that CCR7 may be involved in the inflammatory process of UC [25]. Jie HE et al. used bioinformatics methods to identify 11 core genes, including CCR7 (such as ICAM1, SELL, CD44, etc.), and pointed out that these genes may play critical regulatory roles in UC, closely related to pathological processes such as lymphoproliferative diseases, inflammation, and necrosis. Subsequent Western Blot experimental results also confirmed the high expression of ICAM1 and SELL in UC, further emphasizing the importance of CCR7 in the pathogenesis of UC [26].The research on CCR7 in the field of immune diseases still holds boundless possibilities. Although numerous studies have currently uncovered the expression changes and potential action mechanisms of CCR7 in various immune diseases, its specific functions in different disease stages and different cell subsets remain to be further explored in depth.

CD19 is a CD molecule (i.e., leukocyte differentiation antigen) expressed on B cells, belonging to the Ig superfamily. It participates in the interaction between B cells and other immune cells such as T cells and macrophages, regulating humoral and cellular immune responses. It also modulates the transport of Ca²⁺ within B cells. CD19 influences the activation and proliferation of B cells, thereby regulating the intensity and duration of immune responses [27]. Research by Anang DC et al. revealed a notable phenomenon: compared to lymphoid tissues from healthy individuals, the number of CD19 ⁺ B cells, CD4 ⁺ CXCR5 ⁺ follicular helper T cells, and CD8 ⁺ CXCR5 ⁺ follicular T cells is significantly increased in the lymphoid tissues of patients with rheumatoid arthritis (RA) and individuals at risk for RA [28]. This discovery further emphasizes the potential importance of CD19 in the pathogenesis of RA. Relevant research by Heung Bum Lee, MD indicates that the percentage of CD19 is elevated in patients with ulcerative colitis (UC) compared to a control group [29]. In addition to RA and UC, CD19 has also garnered attention in other autoimmune diseases. In patients with systemic lupus erythematosus (SLE), a significantly higher frequency of CD19 ⁺ CD20 ⁻ B cells is observed compared to healthy controls, and this frequency positively correlates with disease activity [30].In multiple sclerosis (MS), CD19 ⁺ B cells participate in inflammatory responses within the central nervous system, promoting the development of neuroinflammation through interactions with T cells and other immune cells [31].These studies collectively indicate that CD19 plays a pivotal role in various autoimmune diseases.However, to more comprehensively and accurately reveal the expression characteristics and potential action mechanisms of CD19 in a wider range of immune diseases such as ulcerative colitis (UC) and rheumatoid arthritis (RA), it is still necessary to conduct larger-scale and more rigorously designed scientific studies for in-depth exploration and validation.

CXCL13 is described as the most effective chemokine for B cells [32].Through interaction with its receptor CXCR5, it effectively promotes the migration of B lymphocytes. The binding of CXCL13 to CXCR5 also contributes to the maturation of B cells into plasma cells, which then produce antibodies [33]. CXCL13 regulates the activation and migration of various immune cells at inflammatory sites, mediating the expression of inflammatory mediators and thereby modulating inflammatory responses,It plays a crucial role in autoimmune diseases such as Sjögren’s syndrome, lupus nephritis, and multiple sclerosis [34,35]. Achudhan D et al. found that levels of CXCL13 and TNF-α were higher in Rheumatoid Arthritis (RA) samples compared to healthy controls [36]. Another meta-analysis involving 332 RA patients and 147 healthy controls also pointed out that circulating CXCL13 levels in RA patients were significantly higher than those in healthy individuals, further confirming the crucial role of CXCL13 in the pathogenesis of RA [37].Udai P Singh et al. compared the systemic concentrations of key chemokines and cytokines in 42 patients with inflammatory bowel disease (IBD) of different disease activities with the levels in 10 healthy donors. They found that, compared to normal healthy donors, a series of chemokine levels were significantly increased in IBD patients, including macrophage migration inhibitory factor (MIF), CCL25, CCL23, CXCL5, CXCL13, CXCL10, CXCL11, MCP1, and CCL21 (P < 0.05) [38]. Research by Lu Hui et al..also demonstrated that CXCL13 is significantly overexpressed in both ulcerative colitis (UC) and rheumatoid arthritis (RA), closely related to disease activity and the severity of inflammatory responses. In UC, CXCL13 is mainly secreted by T peripheral helper cell cells and macrophages; in RA, CXCL13 is mainly secreted by various cells in synovial tissue, including monocytes/macrophages, T follicular helper cells, and Follicular dendritic cells [39].

CXCR4 is an amino acid rhodopsin-like G protein-coupled receptor (GPCR) that specifically binds to the ligand CXCL12. CXCL12 exhibits a strong chemotactic effect on lymphocytes, and as the exclusive receptor for CXCL12, CXCR4 can activate multiple signaling pathways upon activation by CXCL12, thereby regulating cell migration, survival, and proliferation [40] The interction between such receptors and ligands holds profound significance in both biological and medical fields. Numerous studies have demonstrated that the CXCR4/CXCL12 axis exhibits abnormal activation in a variety of autoimmune diseases, including psoriasis, systemic lupus erythematosus (SLE), multiple sclerosis (MS), rheumatoid arthritis (RA), type 1 diabetes (T1D), and inflammatory bowel disease (IBD), particularly ulcerative colitis (UC). For instance, elevated mRNA expression of CXCR4/CXCL12 has been observed in both psoriatic lesions and SLE patients [41,42]. Focusing on rheumatoid arthritis, which is the focus of our current analysis, the pathogenic role of CXCR4 is particularly prominent. In a prospective study by I B Hansen et al. on rheumatoid arthritis (RA) patients receiving methotrexate (MTX) treatment, plasma CXCL12 (p-CXCL12) levels in RA patients were significantly and persistently elevated compared to the control group [43]. Additionally, other studies have shown that the expression of CCR1, CCR2, CCR4, CCR5, and CXCR4 on the surface of B cells in synovial fluid (SF) of arthritis patients is significantly increased [44]. In studies of ulcerative colitis (UC), CXCR4 expression also showed a significant increase. Research by S Hosomi et al. found that the number of immature plasma cells in the peripheral blood of patients with active ulcerative colitis was significantly increased, and these cells expressed positive for multiple chemokine receptors, including CXCR4. Compared with the healthy control group, CXCR4 expression levels in UC patients were significantly elevated. Further research suggested that high CXCR4 expression may be closely related to the migration of immature plasma cells to the inflammatory sites in UC, revealing the important role of CXCR4 in the pathogenesis of ulcerative colitis [45]. In summary, the CXCR4/CXCL12 axis represents a shared immune dysregulation pathway connecting multiple autoimmune diseases.

SELL (selectin L) is a membrane glycoprotein widely expressed on the surface of human and other animal cells, primarily expressed on the surface of endothelial cells and upregulated during inflammation and immune responses, mediating the migration and infiltration of leukocytes. It plays a crucial role in the initial “rolling” adhesion of leukocytes on the vascular endothelial surface, enabling immune cells to scan chemotactic signals and select appropriate tissue entry sites [46,47], It exhibits abnormal phenotypes in a variety of autoimmune diseases, is closely associated with disease activity and immune cell migration, and influences the onset and progression of these diseases.Research by Y. Kurohori MD et al. on peripheral blood mononuclear cells (PBMCs) from rheumatoid arthritis (RA) patients revealed that the positive rate of L-selectin in RA patients was significantly higher than that in the normal population. Furthermore, in active RA cases, the expression of L-selectin-positive CD4 + cells and the L-selectin/CD4 ratio was also significantly higher than in inactive cases or the normal control group. More interestingly, the number of L-selectin-positive cells showed a positive correlation with laboratory indicators such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), further strengthening the close link between L-selectin expression and RA inflammation activity [48]. Similarly, elevated SELL mRNA expression has also been observed in systemic sclerosis [49], and upregulation of SELL has been detected in ankylosing spondylitis as well [50]. Although significant progress has been made in research on SELL (L-selectin) in autoimmune diseases, many aspects of its underlying mechanisms remain unclear. In the future, large-scale, multicenter clinical studies are needed to further explore the specific molecular mechanisms regulating SELL expression.

Previous studies have clearly indicated that microRNAs (miRNAs) and their related genes occupy a central position in the regulatory network of inflammatory bowel disease (IBD). Specifically, research by Mohsen Nemati Bajestan et al. revealed that in the IBD environment, both the long non-coding RNA MALAT1 and the pro-inflammatory cytokine IL-6 expression are significantly upregulated, and their interaction is finely regulated by microRNAs, including hsa-miR-9-5p (abbreviated as miR-9-5p). This finding strongly supports the presence and indispensable regulatory role of miR-9-5p in the pathophysiology of IBD [51]. Furthermore, our research explored the potential connection between rheumatoid arthritis (RA) and ulcerative colitis (UC) at the miRNA and gene levels, particularly focusing on the two key genes CCR7 and CXCR4. However, this preliminary finding requires extensive subsequent research for further validation and expansion to more comprehensively reveal its underlying mechanisms and clinical significance.

In general, our exploration of the molecular mechanisms of rheumatoid arthritis (RA) and ulcerative colitis (UC) has revealed significant similarities between these two diseases. This provides new insights into their pathological mechanisms and valuable perspectives for the development of new treatment strategies and the identification of potential therapeutic targets for RA and UC. However, current research is still primarily based on bioinformatics analysis, without in vitro functional experiments or animal model validation. Therefore, further studies are required to clarify the specific roles of these genes in disease progression and to evaluate their feasibility as therapeutic targets or diagnostic biomarkers.

5. Conclusion

This study employed bioinformatics approaches to conduct an in-depth analysis of gene expression differences between two autoimmune diseases RA and UC. Based on differential expression analysis, we identified genes differentially expressed in each disease compared to normal controls and further isolated a shared core set of disease-related genes. The results reveal that although RA and UC differ in clinical manifestations and affected organs, they exhibit significant overlap in certain immune-related signaling pathways and gene expression patterns, suggesting common underlying mechanisms in immune regulation and inflammatory responses. Notably, several key genes involved in immune cell activation and inflammatory mediator release showed consistent dysregulation in both diseases, highlighting them as priority candidates for subsequent functional experiments and translational research. In summary, this study elucidates both shared and distinct gene expression features between RA and UC from a bioinformatics perspective, providing new insights into their common and specific pathological mechanisms. The identified common key genes establish a theoretical foundation for exploring cross-disease immunomodulatory targets, holding significant scientific and potential translational value.

6. Limitations

While this study systematically identified shared hub genes and pathways between rheumatoid arthritis (RA) and ulcerative colitis (UC) through bioinformatic analysis, several limitations should be acknowledged. First, our analysis relied on bulk tissue sequencing data, the results of which are inevitably influenced by tissue cellular heterogeneity. Inter-individual variations in the proportions of immune cells, stromal cells, and other components within synovial tissue and intestinal mucosa may represent significant confounding factors in gene expression variation. Although we employed bioinformatic methods for estimation and statistical adjustment, residual confounding effects on the accuracy of the results cannot be entirely excluded.Second, the integration of datasets was challenged by incomplete clinical metadata. The absence of detailed information such as age, sex, disease stage, medication history, and comorbidities in some datasets limited our ability to more comprehensively control for these potential confounding variables in the models, which may affect the specificity of the differential expression analysis.Furthermore, a primary limitation of this study is that all findings are derived from bioinformatic inferences and have not yet been validated through functional experiments. The causality of the identified hub genes and their predicted molecular mechanisms, along with their precise biological functions in the pathogenesis of RA and UC, require further confirmation using in vitro cell models, organoids, or animal studies.Finally, the retrospective nature of analyses based on public databases introduces an inherent risk of selection bias. Additionally, technical variations across different study platforms, despite normalization, may retain residual batch effects.

Supporting information

S1 Table. Details of rheumatoid arthritis-related datasets used in this study.

(DOCX)

pone.0339397.s001.docx^{(12.2KB, docx)}

S2 Table. Details of the ulcerative colitis datasets used in this study.

(DOCX)

pone.0339397.s002.docx^{(12KB, docx)}

S1 File. Rheumatoid Arthritis dataset before normalization.

(CSV)

pone.0339397.s003.csv^{(8.1MB, csv)}

S2 File. Rheumatoid Arthritis dataset after.

(CSV)

pone.0339397.s004.csv^{(13.6MB, csv)}

S3 File. Ulcerative Colitis dataset before normalization.normalization.

(CSV)

pone.0339397.s005.csv^{(38.4MB, csv)}

S4 File. Ulcerative Colitis dataset after normalization.

(CSV)

pone.0339397.s006.csv^{(72.1MB, csv)}

S5 File. Differentially expressed genes for rheumatoid arthritis.

(CSV)

pone.0339397.s007.csv^{(45.7KB, csv)}

S6 File. Differentially expressed genes for ulcerative colitis.

(CSV)

pone.0339397.s008.csv^{(86.3KB, csv)}

S7 File. Hub genes from PPI network (confidence threshold = 0.300).

(CSV)

pone.0339397.s009.csv^{(979B, csv)}

S8 File. Hub genes from PPI network (confidence threshold = 0.400).

(CSV)

pone.0339397.s010.csv^{(873B, csv)}

S9 File. Hub genes from PPI network (confidence threshold = 0.500).

(CSV)

pone.0339397.s011.csv^{(692B, csv)}

S10 File. Intersection of hub genes from thresholds 0.3, 0.4, and 0.5.

(CSV)

pone.0339397.s012.csv^{(95B, csv)}

S11 File. ROC analysis results for hub genes in rheumatoid arthritis.

(CSV)

pone.0339397.s013.csv^{(2.9KB, csv)}

S12 File. ROC analysis results for hub genes in ulcerative colitis.

(CSV)

pone.0339397.s014.csv^{(2.8KB, csv)}

S13 File. Immune cell expression profiles of hub genes in rheumatoid arthritis.

(CSV)

pone.0339397.s015.csv^{(5KB, csv)}

S14 File. Immune cell expression profiles of hub genes in ulcerative colitis.

(CSV)

pone.0339397.s016.csv^{(5.1KB, csv)}

S1 Fig. ceRNA Regulatory Network.

(TIF)

pone.0339397.s017.tif^{(121.6KB, tif)}

S1 Data

(ZIP)

pone.0339397.s018.zip^{(60.4MB, zip)}

Data Availability

The relevant code has been uploaded to 10.6084/m9.figshare.30639671.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0339397.r001

Decision Letter 0

Senthilnathan Palaniyandi

3 Jun 2025

Dear Dr. Han,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: No

**********

Reviewer #1: This manuscript presents a bioinformatics-based comparative analysis of gene expression profiles in rheumatoid arthritis (RA) and ulcerative colitis (UC) using publicly available microarray datasets. The goal of identifying shared and disease-specific molecular signatures is clinically relevant and potentially impactful. The authors have utilized standard tools and multiple datasets, which strengthens the analytical rigor. However, the manuscript requires some revisions to improve clarity and the biological interpretation of results.

1. Overstatement of Therapeutic Implications: The manuscript repeatedly refers to the identified genes as therapeutic targets or biomarkers. While these genes may warrant further investigation, the current study is based solely on in silico transcriptomic analysis without functional validation. Please moderate the language to reflect that these genes are potential candidates for future research, not confirmed therapeutic targets as the manuscript addresses very common genes (e.g. CD19 on B cells) that needs further proof to be considered a specific biomarker or therapeutic target for these diseases.

2. Lack of RA vs. UC Differential Comparison: Despite the stated objective of uncovering molecular mechanisms that distinguish RA from UC, the manuscript focuses primarily on genes commonly upregulated in both diseases. Please modify the objective of the manuscript to reflect better the data evaluated.

3. Biological Interpretation of Results: In the section “GENEMANIA Online Analysis” authors list the ten hub genes and suggest that these genes are involves in metabolic and thermogenic processes. Further down in the discussion they go in detail explaining the role of each gene where they correctly explain the functions of these genes – nonrelated to metabolic and thermogenic processes but strong inflammatory responses and cellular migration. The authors should revise this interpretation or provide robust references to support the claim.

4. Review grammar, typographical errors and abbreviations: Please review the manuscript for grammar errors, long sentences or typographical errors to improve readability. In the results section match UC abbreviation with the rest of the manuscript. Add the full word phrase for Tph, WB and other abbreviations lacking explanation.

5. Attach better figure quality.

Reviewer #2: The study reports the Common Differential Gene Expression between Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) using datasets through bioinformatics analysis. However, there are key concerns which need to be addressed.

• The rationale for choosing these disease conditions RA and UC is not clear. Why did they did not choose other autoimmune conditions which are closely related to UC or RA.

• The reason for choosing these specific datasets is not clearly mentioned.

• Did they look at the other parameters like gender, age, treatment conditions and other existing co-morbidities which could influence the study outcomes.

• The results report the differentially expressed genes (DEG’s) such as CD19, CCR7 which are very common in other autoimmune conditions. The relevance of the DEG’s with other autoimmune disorders should be discussed in detail.

• The identified DEG’s should be validated experimentally to address the common molecular mechanisms.

• The quality of the figures should be improved, and the study can be strengthened by providing the detailed insights into relationship between these conditions.

**********

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Reviewer #1: No

Reviewer #2: No

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Attachment

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PLoS One. 2026 Jan 2;21(1):e0339397. doi: 10.1371/journal.pone.0339397.r002

Author response to Decision Letter 1

25 Jul 2025

Dear Reviewers,

We wholeheartedly extend our gratitude to all of you for the precious feedback on our manuscript titled "Analysis of Common Differential Gene Expression between Rheumatoid Arthritis and Ulcerative Colitis". We are well aware of the immense effort and time you have devoted during the review process, and we hereby offer our sincere thanks. Your insightful remarks have not only enhanced our in-depth understanding of this research but also provided us with invaluable suggestions. We have carefully contemplated all the suggestions and will incorporate them one by one in the revised manuscript. Below are our point-by-point responses to each of your major comments.

Reviewer #1:

1.Dear Reviewer, I sincerely appreciate your invaluable review comments and your attention to enhancing the quality of this manuscript. Your observation regarding the "overstatement of therapeutic significance" is exceptionally insightful and of paramount importance. Indeed, it is scientifically rigorous to refer to the identified genes as "therapeutic targets" or "biomarkers" without functional validation, particularly for widely expressed genes like CD19. Following your suggestion, we have revised the relevant sections of the manuscript to present the potential research value of these genes in a more cautious manner.Terminology Revision: We have replaced the term "therapeutic targets" with the more prudent phrase "potential candidate genes for future research," emphasizing that these genes require further functional validation and clinical evidence before they can be confirmed as genuine therapeutic targets or biomarkers. Additionally, we have refined the language in certain paragraphs to prevent overinterpretation or exaggeration of the current study's conclusions.

2.Dear Reviewer,Thank you for reviewing my submission and providing your invaluable feedback. The issue you raised regarding the "lack of comparative analysis between rheumatoid arthritis and ulcerative colitis" is of critical importance. Although the primary objective of this study was to identify potential shared molecular mechanisms between RA and UC , as you correctly pointed out, the current research primarily focused on genes that were concurrently upregulated in both diseases, without systematically comparing their differential characteristics in terms of gene expression patterns, functional pathways, or pathological mechanisms. To better reflect the scientific value of this work, we have revised the research objectives to more precisely define the scope and focus. Specifically, the updated objectives now state:This study employs bioinformatics analysis with the objective of identifying commonly differentially expressed genes (DEGs) in ulcerative colitis (UC) and rheumatoid arthritis (RA), as well as exploring their underlying molecular mechanisms. By doing so, it aims to provide a theoretical basis for investigating the potential associations between these two diseases and developing novel therapeutic strategies.

3.Dear Reviewer,Thank you for reviewing my submission and providing your valuable feedback. In the section on "GENEMANIA online analysis," we listed ten hub genes and initially suggested that they might be involved in metabolic and thermogenic processes. However, later in the discussion section, we provided a more detailed description of these genes' functions, highlighting their primary associations with inflammatory responses and cell migration, rather than metabolic or thermogenic processes. We sincerely apologize for this inconsistency in our statements. To ensure the scientific rigor and logical coherence of the paper, we have revised the relevant sections. Specifically, we have re-examined and adjusted the descriptions in the "GENEMANIA online analysis" section to more accurately reflect the functional associations of these genes within the gene network. Furthermore, in the discussion section, we have further clarified the biological functions of these genes. For instance, we have removed the statement "involved in metabolic and thermogenic processes" and supplemented it with a more precise description: their central roles in various immune and inflammation-related biological processes, as well as their participation in immune cell activation, recruitment, and migration.

4.Dear Reviewer, I'm deeply grateful for your meticulous review of my submission and the precious feedback you've offered. We've conducted a systematic revision targeting language and structural issues: First off, we've gone through the manuscript sentence by sentence to rectify all spelling and grammatical errors. Next, we've disassembled and reconstructed lengthy and complex sentences, and employed logical connectors to enhance the coherence between paragraphs. Especially in the Methodology and Results sections, we've sharpened the content by trimming repetitive descriptions and consolidating similar information to boost its conciseness.

At the same time, we've re-examined and reorganized the scientific logical chain of the study. While keeping the core data intact, we've eliminated redundant expressions and supplemented key research details to enrich the completeness of the information.

In the Results section, we've annotated the full names for all abbreviations when they first appear in each paragraph. For the same abbreviations that show up later in the paragraph, we've uniformly used lowercase letters. Regarding unexplained abbreviations in the original manuscript, such as "Tph" and "WB," we've added their corresponding full phrases to ensure that readers can clearly comprehend the content.

5.Dear Reviewer,We have conducted a thorough inspection and optimization of the relevant figures and tables. We have adjusted their sizes and resolutions in accordance with the journal's requirements to ensure they meet the submission standards while maintaining both clarity and aesthetic appeal.

Reviewer #2: •

1.Dear Reviewer,Thank you for raising these significant questions. Regarding the selection of rheumatoid arthritis (RA) and ulcerative colitis (UC) as research subjects, we have made revisions in the manuscript based on the following scientific considerations:

(1) From an epidemiological perspective, both RA and UC have relatively high incidence rates globally, with a rising prevalence in recent years. Moreover, these two diseases exhibit a considerable overlap rate in clinical practice, particularly among UC patients, who have a significantly higher incidence of extraintestinal manifestations (such as arthritis) compared to patients with other intestinal diseases.

(2) In terms of pathological mechanisms, both RA and UC are chronic inflammatory diseases. RA primarily involves chronic inflammation of the joint synovium, while UC focuses on the inflammatory response in the intestinal mucosa. Despite their different lesion sites, both diseases are characterized by abnormal activation of the immune system and chronic inflammatory responses (e.g., Fcγ receptor signaling). Additionally, there is notable genetic overlap between RA and UC in the IL-23/Th17 pathway, providing an ideal comparative model for studying immune-related mechanisms.

(3) Research value and innovation: Selecting RA and UC as research subjects not only helps to uncover the common mechanisms underlying autoimmune diseases but also provides a theoretical basis for developing new therapeutic strategies. We have supplemented the research background section to more clearly explain the reasons for choosing RA and UC and emphasize their representativeness and scientific value in autoimmune disease research.

We highly value your revision suggestions and have made corresponding adjustments to the manuscript according to your feedback.

2.Dear Reviewer, The gene expression datasets we selected are all sourced from the public bioinformatics database—GEO (Gene Expression Omnibus). GEO is a high-throughput gene expression data repository maintained by the National Center for Biotechnology Information (NCBI) in the United States. It encompasses a wide range of research fields and features high-quality data with a broad clinical application background. The datasets we chose include samples from patients with rheumatoid arthritis (RA) and ulcerative colitis (UC), as well as healthy control groups, with each group comprising more than 20 samples. This sample size ensures the statistical significance of our experimental results and helps avoid phenotypic confounding due to insufficient sample sizes. Additionally, we prioritized datasets that align with our specific research objectives to ensure data comparability and the reliability of our analyses.

3.Dear Reviewer, Thank you for your questions. We hereby provide the following responses to the issues you raised: We explicitly acknowledge that parameters such as age, gender, treatment conditions, and comorbidities may potentially confound gene expression profiles and serve as important background variables in certain analyses. However, due to limitations in the data collection process, we were unable to obtain detailed age group and gender information for all samples. Given that our primary research objective is to minimize batch effects across datasets through rigorous normalization procedures, identify differentially expressed genes associated with rheumatoid arthritis and ulcerative colitis, and explore the roles of these genes in disease progression, we decided to include the aforementioned datasets in our study and performed standardization processes during subsequent data analysis. We believe that this approach can, to a certain extent, balance the influence of confounding factors while focusing on the core objectives of our research. This aspect has been clarified in the "Limitations" section of our article.

4.Dear Reviewer,Thank you for this valuable suggestion. Delving into the performance of these differentially expressed genes (DEGs) in other autoimmune diseases will indeed facilitate a better understanding of the potential significance of our findings. In response to your advice, we have added supplementary discussions in the relevant section. We believe that these additional analyses have substantially enhanced the depth and breadth of our study, enabling readers to gain a more comprehensive understanding of the disease-specific and shared characteristics of these DEGs.

5.Dear Reviewer,We sincerely appreciate your valuable feedback. We have come to deeply recognize the pivotal role that experimental validation plays in research. In this study, we conducted preliminary validation using an additional dataset. Given the large number of genes initially screened, validating each one individually would lack focus and efficiency. Therefore, we plan to further screen for 1 - 3 ideal genes through the construction of animal models in the later stage for subsequent validation. However, constructing and validating animal models takes time. Thus, we intend to report the above experimental content and validation results in a forthcoming paper.

The current research findings serve as the preliminary results for subsequent experiments. Nevertheless, it is undeniable that they lack the support of replicable experimental results. Hence, we have added a detailed explanation of this limitation and our future prospects in the "Limitations" section of the manuscript.Once again, we thank you for your precious and insightful suggestions, which have been of immense help in improving our research and enhancing its scientific quality.

6.Dear Reviewer,Regarding the issue of the images, we have utilized the official PLOS ONE processing platform PACE (https://pacev2.apexcovantage.com/ Home Page - Apex.PACE ) to adjust the dimensions and resolution of the charts and graphs. This ensures that they meet the submission requirements while maintaining both clarity and aesthetic appeal.As for the detailed explanation you requested concerning the relationship between rheumatoid arthritis (RA) and ulcerative colitis (UC), we have made supplementary enhancements in the revised manuscript, primarily based on the following considerations: We explored the connections between the extra-articular manifestations of RA and UC. In terms of immune pathways, both diseases are characterized by abnormal activation of the immune system and chronic inflammatory responses. Additionally, there is a significant genetic overlap in the IL-23/Th17 pathway between the two. Moreover, they share common genetic and molecular mechanisms. We delved into the genetic overlaps between RA and UC, such as the differential expression of genes like CCR7, CD19, CXCL13, CXCR4, and SELL, and their roles in the diseases.However, it is important to note that current research still requires stronger experimental validation to confirm the practical clinical value of the identified genes and pathways.

We again thank the reviewer for their valuable comments and look forward to your further feedback on our revised manuscript.

Best regards,

Wei-rong Cui

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PLoS One. doi: 10.1371/journal.pone.0339397.r003

Decision Letter 1

Srinivas Mummidi

5 Oct 2025

Dear Dr. %Peng-fei han%,

==============================

Please submit your revised manuscript by Nov 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Srinivas Mummidi, D.V.M., Ph.D.

Academic Editor

PLOS ONE

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Additional Editor Comments (if provided):

1. A major concern of this paper is using mixed platforms (especially in the validation studies. This could result in false positives. The authors should indicate how they have controlled for this in the discussion

2. GSE92415 is from a golimumab trial and contains pre/post treatment samples; the authors should explicitly state which samples are used in their analysis in such datasets as drugs will have strong effects on gene expression and could bias their results

3. The pipeline description is ambiguous and mixes RNA seq and microarray methods (DESeq2 vs limma) and statistical tests (limma’s moderated t vs a standard two sample t), reducing reproducibility and potentially invalidating some results. Please clarify in Methods how this was done. Ideally Limma should be done for microarray studies and DE-Seq2 for RNA-Seq studies

4. Some statistical analysis descriptions are confusing. Ideally all microarray analyses should have been with a microarray appropriate pipeline (RMA/quantile normalization → probe to gene mapping → limma), and reserve DESeq2 exclusively for RNA seq (GSE89408). Clearly separate these in the Methods. For validation analyses, avoid plain two sample t tests across thousands of genes; either use limma again (with FDR control), or pre specify a small gene set tested with a priori hypotheses.

5. Batch correction is showed in PCAs. Also show the PCAs by case/control to show correction as COMBAT can potentially remove true biological signal. Perform cell composition adjustment and stratified analyses -- otherwise the results are confounded

6. Cytoscape’s cytoHubbacytoHubba MCC refers to Maximal Clique Centrality and not “modularity class.” Could the authors please clarify.

7. The hub list is very generic. The robustness of the hub ranking could be tested using network evidence type edge confidence thresholds, and network randomization, and report stability. Interpret hubs in the context of cellular compartment. 3. Address tissue heterogeneity and immune cell confounding.

8. For each gene, report log2FC with 95% CI, BH adjusted p in validation, and ROC/AUC for disease vs control. Perform study wise replication (each dataset left out in turn).

9. Provide a fully reproducible ceRNA pipeline (databases, versions, cutoffs, intersection logic) and test for co expression consistency across cohorts; otherwise move the ceRNA figure to Supplementary as hypothesis generating

10. The Discussion section sometimes overstates clinical translation (e.g., “potential therapeutic targets”) on the basis of cross sectional gene expression and network centrality. This not causality -- revise appropriately.

11. Please deposit: (i) the exact R scripts/notebooks, (ii) the sample inclusion lists per GEO series (with platform IDs and tissue source), (iii) the ComBat corrected expression matrices (with and without covariate design), and (iv) full DEG tables

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

**********

Reviewer #1: Thank you for addressing the comments. I believe the edits have improved the quality of the manuscript.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

**********

PLoS One. 2026 Jan 2;21(1):e0339397. doi: 10.1371/journal.pone.0339397.r004

Author response to Decision Letter 2

23 Nov 2025

Dear Reviewers,

We sincerely appreciate the valuable comments and suggestions regarding our manuscript entitled "Differential Gene Expression Analysis of Common Signatures Between Rheumatoid Arthritis and Ulcerative Colitis." We are truly grateful for the considerable time and effort you have dedicated to the review process. Your insightful critiques have not only deepened our understanding of this study but have also provided us with invaluable recommendations. We have carefully considered each comment and will address them point by point in the revised version. Below are our detailed responses to your primary concerns.

1.We sincerely thank the reviewer for this valuable comment. The reviewer rightly pointed out that integrating datasets from different platforms in validation studies introduces potential batch effects as a critical issue, which may introduce technical biases and lead to false positive findings. We fully agree with this perspective and have prioritized this consideration in our analytical process. Prior to the integrative analysis of validation datasets from different platforms, we specifically applied the ComBat algorithm from the R package "sva" to correct for batch effects in the gene expression matrix. This method is widely recognized as a gold standard for addressing batch effects in genomic data, effectively identifying and removing non-biological variations introduced by technical differences across experimental platforms. Following batch effect correction, we performed differential expression analysis using the R package "limma". Limma accounts for various sources of variation within linear models, thereby yielding more accurate statistical inferences. For the validation phase, our approach primarily involved conducting differential expression analysis within each independent validation dataset to assess whether our core findings could be consistently replicated across different platforms and cohorts. This cross-platform reproducibility substantially reduces the likelihood of false positives and strongly supports the robustness of our conclusions.

2.We sincerely thank you for raising this important and insightful point. You correctly noted that the GSE92415 dataset, derived from a golimumab clinical trial, includes paired samples collected both before and after treatment, and that the pharmacological intervention could significantly alter gene expression profiles. We fully agree that carefully accounting for treatment status is crucial in the analytical design.Upon careful re-examination, we confirm that our initial analysis did not adequately distinguish between pre- and post-treatment samples within the GSE92415 dataset, instead analyzing all samples collectively, which may have introduced confounding bias due to drug effects. We have promptly reanalyzed the data, utilizing only the pre-treatment (baseline) samples for all relevant analyses.We are pleased to report that the final gene list obtained from this reanalysis remains consistent with our original findings derived from the full dataset. This outcome not only indicates the relative stability of the gene signatures we initially identified but also validates the effectiveness of the corrective measures taken during the reanalysis, thereby enhancing the reliability and accuracy of our results.Once again, we deeply appreciate your attentive review and guidance. Your expert comments have been instrumental in further refining our study and improving the overall quality of our work.

3.We sincerely thank you for reviewing our manuscript and providing this valuable feedback. You rightly pointed out the lack of clarity in the description of our data analysis workflow and the confusion regarding the methods used—a critical issue with which we fully concur. We acknowledge that this was a significant oversight in our manuscript, which compromised the clarity and reproducibility of our methods, and we sincerely apologize for this error.Upon carefully re-examining our analysis code and records, we confirm that the limma method was in fact used for differential gene expression analysis of the microarray data in this study. The limma package is suitable for analyzing gene expression data generated by either microarray or RNA-seq technologies. The mention of "DESeq2" in the Methods section was an unfortunate typographical error; while DESeq2 is typically applied to RNA-seq data, it was not used in our study. We take full responsibility for this confusion between the two methodological names.We have now completely removed all incorrect references to "DESeq2" and have thoroughly revised the Methods section to accurately and clearly describe the analytical workflow.

4.We sincerely thank you for this important and insightful comment. You have rightly pointed out the lack of clarity in our methodological description, particularly regarding the need for tailored analytical workflows for different data types. We fully agree that precise description of analytical methods is crucial for research reproducibility.Upon careful re-examination, we confirm that the limma package was consistently used for all differential expression analyses during the validation phase, including the processing of both microarray and RNA-seq datasets. The limma package is well-suited for analyzing gene expression data generated by either microarray or RNA-seq technologies, which ensured methodological consistency throughout our study.

5.We sincerely thank you for this important and expert comment. You correctly highlighted the risk that batch effect correction may potentially remove genuine biological signals, as well as the confounding influence of cellular composition on result interpretation. We fully agree that these methodological considerations are crucial for ensuring the reliability of our findings.Following your suggestion, we performed comprehensive PCA visualization analysis. We have now included PCA plots grouped by "Case/Control" status alongside those grouped by "Experimental Batch". These results clearly demonstrate that ComBat correction effectively removed technical variations across batches while successfully preserving and even highlighting the biological differences between the case group (triangles) and the control group (circles).Furthermore, we employed the CIBERSORT algorithm with its LM22 signature matrix to accurately estimate the proportions of 22 immune cell subtypes within each sample.

6.Thank you for pointing out this key terminology error. You are absolutely correct. In our study, we employed the Maximal Clique Centrality (MCC) method from the CytoHubba plugin to identify hub nodes within the network. We sincerely apologize for the typographical error in our original description, where it was mistakenly referred to as "modularity class," which is indeed inaccurate. This error has been comprehensively corrected throughout the revised manuscript.

7.Thank you for your attention to our analytical methodology. We selected a correlation threshold of 0.4 for network construction, which represents a widely accepted and moderately stringent cutoff in the field. This choice was based on the need to balance network scale with connection specificity: an excessively low threshold (e.g., 0.2) would introduce numerous weak or false-positive connections, resulting in an overly dense network, while an overly high threshold (e.g., 0.6) could exclude biologically meaningful signals, leading to an excessively sparse network. The threshold of 0.4 was chosen to retain meaningful biological associations while minimizing biases associated with overly lenient or stringent cutoffs. This threshold is further supported by its application in several published studies, such as:(Zhang R, Lan X, Zhu W, Wang L, Liu P, Li P. Regulation of autophagy by the PI3K-AKT pathway in Astragalus membranaceus–Cornus officinalis to ameliorate diabetic nephropathy. Front Pharmacol. 2025;16:1505637. Liu H, Wu M, Qi G, Ma F, Cao Y. Identification and Validation of Potential Diagnostic Biomarkers for Pulmonary Arterial Hypertension Based on Gene Expression Profiling. Pulm Circ. 2025;15(4):e70207.) To evaluate the robustness of the identified hub genes, we systematically varied the correlation threshold from 0.3 to 0.5 and re-identified hub genes under each condition. The results demonstrated that the top-ranked core hub genes remained largely stable across this range of thresholds. This confirms that these core hubs are not artifacts of a specific threshold value but represent intrinsic key nodes within the network structure.

Understanding the subcellular localization of hub genes is essential for interpreting their biological functions. As suggested, we performed a detailed compartment-based analysis using the Human Protein Atlas (HPA) database, which yielded important insights: the five hub genes exhibit a clear localization dichotomy, with 80% (SELL, CCR7, CD19, and CXCR4) being membrane-localized proteins primarily positioned on the plasma membrane, while the remaining 20% (CXCL13) is a secreted protein localized to the extracellular space. Based on these localization patterns, we propose a plausible functional divergence: the membrane proteins are likely involved in immune recognition and recruitment through direct cell-surface interactions, whereas the secreted protein CXCL13 may modulate the microenvironment via paracrine signaling. This spatial perspective provides a compelling explanation for their distinct immune-correlation patterns.

Tissue heterogeneity and the confounding effects of immune cell infiltration are critical issues that must be seriously considered. To directly quantify and address this problem, we systematically performed immune infiltration association analysis on the identified hub genes using the CIBERSORT algorithm. This analysis provided key evidence to distinguish whether changes in gene expression originate from differences in immune cell composition or from pathological programs of tissue-resident cells.

Our analysis yielded clear and discriminative conclusions: In UC, we observed significant heterogeneity in immune infiltration. Through correlation analysis, we successfully categorized the hub genes into two groups: one group (e.g., CD19, CXCL13) showed strong correlations with the abundance of adaptive immune cells (such as B cells and follicular helper T cells), indicating that their hub status is partially driven by immune cell infiltration; whereas the other group (e.g., CXCR4) remained relatively independent of immune cell variations, suggesting that it is more likely to reflect intrinsic pathological processes of intestinal resident cells. In RA, we discovered a distinctly different pattern. Immune infiltration in synovial tissue showed high consistency across samples, and more importantly, none of the hub genes (SELL, CCR7, CD19, CXCL13, CXCR4) showed significant correlations with the proportions of any immune cell type. This key evidence strongly indicates that in RA, the expression changes of our identified hub genes are not confounded by variations in immune cell composition among samples, but rather represent core pathogenic molecular programs that are stably activated within resident synovial cells during disease progression.We have systematically integrated these key analyses and conclusions into the manuscript and included the immune cell correlation results in the supplementary files. We once again thank you for your insightful comments, which have helped us better highlight the novelty and rigor of our study. We hope the current revisions meet your requirements.

8.We sincerely thank you for these rigorous and insightful comments. Your request for detailed statistical reporting and robustness validation is crucial for ensuring the reliability of our findings. In accordance with your suggestions, we have conducted comprehensive supplementary analyses, as detailed below: We have calculated and plotted ROC curves for each validated gene (Figure 10), and reported the log2FC (with 95% CI) and Benjamini-Hochberg (BH) adjusted p-values. These results have been compiled in a supplementary table included in the supplementary files. Furthermore, we rigorously performed a "leave-one-dataset-out" study-level validation. The results demonstrate that our identified hub genes remained statistically significant across all validation rounds, with stable AUC values, confirming the exceptional robustness of our findings.

9.Sincerely thank you for this rigorous and highly constructive feedback. You correctly pointed out that, in the absence of cross-cohort co-expression consistency validation, ceRNA analysis based on bioinformatic predictions is more appropriately framed as hypothesis-generating rather than as definitive evidence. We fully agree with this perspective. To ensure the robustness of our paper’s conclusions and adhere to the highest standards of scientific rigor, we have moved the ceRNA network figure from the main text to the supplementary materials. In both the Results and Methods sections of the manuscript, we have revised all relevant descriptions to clearly emphasize the exploratory and hypothesis-generating nature of this analysis, and have removed any language that could be interpreted as implying causal relationships or overstating the findings.

10.We sincerely thank you for this important comment. We fully agree that directly inferring "potential therapeutic targets" based on cross-sectional gene expression data and network centrality analysis constitutes an overinterpretation of the clinical implications and incorrectly implies causality. We have thoroughly revised the Discussion section to address this issue.

11.We thank the reviewer for this suggestion, which helps enhance the reproducibility of our work. In accordance with this request, we have included all requested materials in the supplementary files.

We again thank the reviewer for their valuable comments and look forward to your further feedback on our revised manuscript.

Best regards,

Wei-rong Cui

Attachment

Submitted filename: Response to Reviewers .docx

pone.0339397.s021.docx^{(17.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0339397.r005

Decision Letter 2

Srinivas Mummidi

7 Dec 2025

Analysis of Common Differential Gene Expression between Rheumatoid Arthritis and Ulcerative Colitis

PONE-D-24-53954R2

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PLoS One. doi: 10.1371/journal.pone.0339397.r006

Acceptance letter

Srinivas Mummidi

PONE-D-24-53954R2

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Details of rheumatoid arthritis-related datasets used in this study.

(DOCX)

pone.0339397.s001.docx^{(12.2KB, docx)}

S2 Table. Details of the ulcerative colitis datasets used in this study.

(DOCX)

pone.0339397.s002.docx^{(12KB, docx)}

S1 File. Rheumatoid Arthritis dataset before normalization.

(CSV)

pone.0339397.s003.csv^{(8.1MB, csv)}

S2 File. Rheumatoid Arthritis dataset after.

(CSV)

pone.0339397.s004.csv^{(13.6MB, csv)}

S3 File. Ulcerative Colitis dataset before normalization.normalization.

(CSV)

pone.0339397.s005.csv^{(38.4MB, csv)}

S4 File. Ulcerative Colitis dataset after normalization.

(CSV)

pone.0339397.s006.csv^{(72.1MB, csv)}

S5 File. Differentially expressed genes for rheumatoid arthritis.

(CSV)

pone.0339397.s007.csv^{(45.7KB, csv)}

S6 File. Differentially expressed genes for ulcerative colitis.

(CSV)

pone.0339397.s008.csv^{(86.3KB, csv)}

S7 File. Hub genes from PPI network (confidence threshold = 0.300).

(CSV)

pone.0339397.s009.csv^{(979B, csv)}

S8 File. Hub genes from PPI network (confidence threshold = 0.400).

(CSV)

pone.0339397.s010.csv^{(873B, csv)}

S9 File. Hub genes from PPI network (confidence threshold = 0.500).

(CSV)

pone.0339397.s011.csv^{(692B, csv)}

S10 File. Intersection of hub genes from thresholds 0.3, 0.4, and 0.5.

(CSV)

pone.0339397.s012.csv^{(95B, csv)}

S11 File. ROC analysis results for hub genes in rheumatoid arthritis.

(CSV)

pone.0339397.s013.csv^{(2.9KB, csv)}

S12 File. ROC analysis results for hub genes in ulcerative colitis.

(CSV)

pone.0339397.s014.csv^{(2.8KB, csv)}

S13 File. Immune cell expression profiles of hub genes in rheumatoid arthritis.

(CSV)

pone.0339397.s015.csv^{(5KB, csv)}

S14 File. Immune cell expression profiles of hub genes in ulcerative colitis.

(CSV)

pone.0339397.s016.csv^{(5.1KB, csv)}

S1 Fig. ceRNA Regulatory Network.

(TIF)

pone.0339397.s017.tif^{(121.6KB, tif)}

S1 Data

(ZIP)

pone.0339397.s018.zip^{(60.4MB, zip)}

Attachment

Submitted filename: PONE-D-24-53954.docx

pone.0339397.s019.docx^{(15.2KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0339397.s020.docx^{(17.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers .docx

pone.0339397.s021.docx^{(17.9KB, docx)}

Data Availability Statement

The relevant code has been uploaded to 10.6084/m9.figshare.30639671.

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Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis

Peng-fei Han

Wei-rong Cui

Fang-zheng He

Tao Wu

Chang-sheng Liao

Roles

Abstract

Objective

Materials and methods

Results

Conclusion

1. Introduction

2. Materials and methods

3. Results

Fig 1. Principal component analysis (PCA) of gene expression data before and after batch correction.

Fig 6. Presents a volcano plot, with green indicating low expression and red indicating high expression.

Fig 2. Displays a heatmap, where green represents the normal control group, light blue represents the disease group, and pink, purple, and green distinctions indicate different datasets within the groups.

Fig 3. Presents a volcano plot, with green symbolizing low expression and red symbolizing high expression.

Fig 4. Depicts the combined datasets of UC prior to batch correction. 2b displays the merged UC dataset post-batch correction.

Fig 5. Displays a heatmap, in which green represents the normal control group, light blue represents the disease group, and pink, purple, and green distinctions indicate the different datasets within these groups.

Fig 7. Venn diagram of differentially expressed genes (DEGs) between rheumatoid arthritis (RA) and ulcerative colitis (UC).

Fig 8. Displays the protein-protein interaction (PPI) network generated by the 88 common differentially expressed genes (DEGs) identified between rheumatoid arthritis (RA) and ulcerative colitis (UC).

Fig 12. Robustness validation of hub genes in Ulcerative Colitis 12a Correlation of Area Under the Curve (AUC) values obtained from the full dataset analysis and the leave-one-out cross-validation.

Fig 9. Protein-protein interaction (PPI) networks of hub genes identified in rheumatoid arthritis (RA) and ulcerative colitis (UC).

Fig 10. Receiver operating characteristic (ROC) curves of hub genes in rheumatoid arthritis (RA) and ulcerative colitis (UC).

Fig 11. Robustness validation of hub genes in Rheumatoid Arthritis 11a Correlation of Area Under the Curve (AUC) values obtained from the full dataset analysis and the leave-one-out cross-validation.

Fig 13. Visualization of the GENEMANIA analysis for commonly expressed differentially expressed genes (DEGs) and their involvement in key biological processes.

Fig 14. CCR7 Expression in RA and UC – Demonstrating Significant Upregulation of CCR7 in Both RA and UC Groups, Suggesting Enhanced Activity and Possible Involvement in Disease Processes in the Context of the Disease.

Fig 18. SELL Expression in RA and UC – Revealing Significant Upregulation of SELL in Both RA and UC Groups, Indicating Increased Activity and Possible Participation in Disease Pathways in the Context of the Disease.

Fig 15. CD19 Expression in RA and UC – Showing Significant Upregulation of CD19 in Both RA and UC Groups, Indicating Increased Activity and Potential Participation in Disease Processes in the Context of the Disease.

Fig 16. CXCL13 Expression in RA and UC – Illustrating Significant Upregulation of CXCL13 in Both RA and UC Groups, Reflecting Enhanced Activity and Possible Involvement in Disease Mechanisms in the Context of the Disease.

Fig 17. CXCR4 Expression in RA and UC – Displaying Significant Upregulation of CXCR4 in Both RA and UC Groups, Suggesting Elevated Activity and Potential Role in Disease Processes in the Context of the Disease.

Fig 19. Immune cell profiling and gene-immune cell correlation in rheumatoid arthritis (RA).

Fig 20. Immune cell profiling and gene-immune cell correlation in ulcerative colitis (UC).

Table 1. Subcellular Localization Analysis of Hub Genes.

4. Discussion

5. Conclusion

6. Limitations

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Senthilnathan Palaniyandi

Roles

Author response to Decision Letter 1

Decision Letter 1

Srinivas Mummidi

Roles

Author response to Decision Letter 2

Decision Letter 2

Srinivas Mummidi

Roles

Acceptance letter

Srinivas Mummidi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

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