---
title: "CLDN8 and ABCA12: Shared Molecular Signature in Ulcerative Colitis–Psoriasis Comorbidity"
id: "frontiers-in-immunology-19-cldn8-and-abca12-define-a-shared-molecular-signature-in-ulcerative-colitis"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-19-cldn8-and-abca12-define-a-shared-molecular-signature-in-ulcerative-colitis"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1862149"
published_at: "2026-08-05T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CLDN8 and ABCA12: Shared Molecular Signature in Ulcerative Colitis–Psoriasis Comorbidity
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-19-cldn8-and-abca12-define-a-shared-molecular-signature-in-ulcerative-colitis
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1862149)
- **Published At:** 2026-08-05T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The provided source contains only website navigation and metadata; the full article text was not included in the source content supplied. - The article title indicates that **CLDN8** and **ABCA12** are reported to define a shared **molecular signature** in patients with comorbid **ulcerative colitis** and **psoriasis**. - No abstract, methods, results, figures, sample sizes, statistical analyses, or author conclusions were present in the supplied content. - Key details such as how CLDN8 and ABCA12 were identified, cohorts or sample types used, analytical approaches, and any quantified effect sizes or clinical correlations were not reported in the source. - Because the source text is missing, no validated clinical recommendations, mechanistic insights, or therapeutic implications can be extracted or confirmed from the supplied material. - Readers should consult the full Frontiers in Immunology article for complete methods, data, and clinical interpretation; these elements were not available in the provided content.
## Clinical Analysis & Structured Key Points
Frontiers | CLDN8 and ABCA12 define a shared molecular signature in ulcerative colitis-psoriasis comorbidity ORIGINAL RESEARCH article Front. Immunol. , 05 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1862149 Published in Frontiers in Immunology Inflammation 7 impact factor 11.3 citescore Part of a Research Topic Decoding inflammation across diseases: Insights from omics technologies Submission open 12k views 8 articles Editor & Reviewers Edited by C L Chunying Li Reviewed by K M Ka Man (Ivy) Law X N Xiaoping Niu Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article Figure 8 View in article Figure 9 View in article Figure 10 View in article Table 1 the summary of GEO datasets involving UC and PsO. View in article Table 2 RT-qPCR primers information of the indicated DEGs. View in article Table 3 Characteristics of the included GWAS datasets for UC and PsO. View in article Table 4 Bidirectional MR Analysis of UC and PsO after robustness filtering. View in article ORIGINAL RESEARCH article Front. Immunol. , 05 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1862149 CLDN8 and ABCA12 define a shared molecular signature in ulcerative colitis-psoriasis comorbidity H W Han Wang 1,2 † K J Kun Jin 2 † Y Z Yuan Zhao 3 † Y R Ying Ruan 4 H Z Huiqin Zhu 4 N Z Ni Zhu 2 * 1. School of Pharmacy, Xianning Medical College, Hubei University of Science and Technology, Xianning, China 2. Hubei Key Laboratory of Environmental Risks and Related Diseases Precision Control, School of Basic Medicine Sciences, Xianning Medical College, Hubei University of Science and Technology, Xianning, China 3. School of Biomedical Engineering, Xianning Medical College, Hubei University of Science and Technology, Xianning, China 4. National Demonstration Center for Experimental General Medicine Education, Xianning Medical College, Hubei University of Science and Technology, Xianning, China See more Article metrics View details Abstract Background: Ulcerative colitis (UC) and psoriasis (PsO) are two common immune-mediated inflammatory diseases with significant comorbidity, yet whether one directly causes the other or they share common genetic and immunopathological backgrounds remains unclear. This study integrated bioinformatics, animal experiments, and bidirectional Mendelian randomization to systematically identify potential common molecular targets mediating the comorbidity. Methods: Transcriptomic data for UC and PsO were obtained from the Gene Expression Omnibus (GEO) database. Differential expression genes (DEGs) and WGCNA were performed to identify common transcriptomic alterations. Then, machine learning algorithms (SVM, RF, XGBoost, GLM) were used to screen for shared signature genes. The nomogram diagnostic models were constructed, and immune infiltration landscape were analyzed. An acute UC-PsO comorbidity mouse model was established by simultaneous administration of dextran sulfate sodium and imiquimod, and the expression levels of key genes were examined. Finally, bidirectional Mendelian randomization (MR) was performed to assess the genetic causal relationship between UC and PsO. Results: 3,840 DEGs in UC and 4,208 in PsO, with 619 overlapping DEGs, were identified. WGCNA further screened 17 up−regulated and 16 down−regulated co−expressed genes common to both diseases. Cross−validation using four machine learning algorithms finally identified CLDN8 and ABCA12 as the core signature genes shared by UC and PsO. Diagnostic models based on these two genes performed well for both diseases (specific AUC values can be added). Immune infiltration analysis revealed similar inflammatory pathway characteristics between the two diseases. In the UC−PsO comorbidity animal model, the expression changes of CLDN8 and ABCA12 were consistent with those in clinical samples, confirming their functional relevance. Bidirectional MR analysis showed no significant causal effect of UC on PsO nor of PsO on UC, which does not support the direct causation hypothesis but instead supports the shared mechanism hypothesis. Conclusion: CLDN8 and ABCA12 shared molecular signature in UC-PsO comorbidity, however, no direct genetic causal relationship between UC and PsO. Clinically, each disease should be managed according to its own pathology, while targeting common inflammatory pathways involving CLDN8/ABCA12 to enable personalized treatment. 1 Introduction Inflammatory Bowel Disease (IBD) encompasses a spectrum of chronic, relapsing intestinal inflammatory disorders, with Ulcerative Colitis (UC) and Crohn’s Disease constituting its principal forms. As the predominant IBD subtype, UC is characterized by persistent inflammation confined to the colon and rectum, yet its etiopathogenesis remains incompletely elucidated; current evidence implicates a multifactorial interplay involving genetic susceptibility, environmental triggers, gut microbial dysbiosis, and dysregulated mucosal immune responses ( 1 ). The global burden of UC continues to escalate, as reflected by steadily rising incidence and prevalence rates—a trend particularly pronounced in industrialized nations but increasingly evident in newly industrialized and developing regions as well ( 2 ). Patients with UC not only have to endure gastrointestinal symptoms such as diarrhea and blood in stool over a long period but often also experience multi-system manifestations including joint, skin, and eye issues, severely threatening their quality of life and leading to significant medical costs ( 3 ). Despite the continuous enrichment of treatment options for UC over the past two decades, which comprises 5-aminosalicylic acid derivatives, corticosteroids, immunomodulators, and biological agents, 10-20% of patients still respond poorly to existing medications and ultimately require surgical treatment ( 4 ). These clinical challenges indicate that a deeper exploration of the pathogenesis of UC and its relationship with other chronic inflammatory diseases has become an urgent research need. As a systemic inflammatory condition, UC extends its influence beyond the gut, exhibiting a high burden of extraintestinal comorbidities. Among these, the link with psoriasis (PsO) has garnered particular attention. PsO is a chronic inflammatory dermatosis driven by dysregulated keratinocyte proliferation and infiltration of immune cells, pathophysiological features that strikingly parallel the epithelial barrier dysfunction seen in UC. UC and PsO, as immune-mediated chronic inflammatory diseases, constitute a significant public health burden worldwide. Epidemiological evidence indicates that UC occurs in 0.5% of individuals with PsO ( 5 ), while the reciprocal observation reveals that PsO affects approximately 2.8% of patients diagnosed with UC ( 5 ). Large-scale population studies further reveal a significant bidirectional association between the two diseases: the risk of UC in patients with PsO is 1.71 (95% CI: 1.55-1.89) ( 6 ), while the odds ratio of PsO in patients with UC is 1.75 (95% CI: 1.49-2.05) ( 6 ). Notably, this relationship exhibits greater magnitude in younger populations; specifically, individuals under 19 years of age with PsO demonstrate a hazard ratio for developing inflammatory bowel disease of 5.33 [95% confidence interval (CI): 3.74–7.59] ( 7 ). This phenomenon has been corroborated by regional investigations spanning diverse ethnic cohorts. For instance, a Korean population-based analysis revealed that a diagnosis of PsO confers an elevated subsequent risk of UC, with an adjusted odds ratio calculated at 1.77 (95% CI: 1.44–2.18) ( 8 ). Furthermore, evidence from a 20-year nationwide Danish cohort study indicates that the incidence of UC is heightened among PsO patients undergoing various therapeutic regimens ( 9 ). These comorbid relationships may even begin to manifest up to ten years before diagnosis ( 10 ), suggesting that the two diseases may share underlying pathophysiological mechanisms. The above epidemiological evidence highlights the public health significance of UC and PsO and provides a solid context for future research into the potential associations between them. A Danish nationwide cohort investigation has documented a bidirectional relationship between UC and PsO, demonstrating not only an elevated incidence of PsO among individuals with UC but also a substantially heightened susceptibility to UC in the PsO population (risk increased by 49% in patients with mild PsO and by 56-96% in patients with severe PsO) ( 11 ). Systematic reviews and meta-analyses further quantified this association, showing a 71% increased risk of UC in patients with PsO ( 6 ). A nationwide cohort study in Korea also confirmed a significant association between PsO and increased risk of UC (adjusted odds ratio 1.77) ( 8 ). Collectively, these findings underscore a robust link and a notable propensity for comorbid occurrence between UC and PsO. PsO is a disease characterized by chronic, immune-mediated skin inflammation, with a global prevalence of approximately 2%. It not only affects the skin but is also associated with multi-system diseases such as arthritis, cardiovascular issues, and metabolic disorders ( 12 ). Patients with PsO face long-term skin damage, itching, and psychological stress, significantly impairing their quality of life ( 13 ). Recent advances in molecularly targeted therapies and biologic agents have substantially improved the therapeutic landscape for PsO; nonetheless, some patients still experience limited efficacy and drug-related adverse reactions ( 14 , 15 ). Importantly, epidemiological and clinical observations show that PsO co-occurs with various autoimmune diseases, including IBD, particularly with a higher comorbidity rate of PsO and UC compared to the general population ( 16 ). Both conditions show certain overlaps in pathogenesis, genetic susceptibility, and expression of inflammatory factors, suggesting a potential common pathological basis ( 17 ). However, despite existing research focusing on the epidemiological association and partial molecular mechanisms between UC and PsO, the specific molecular basis underlying their comorbidity and its clinical significance still urgently require systematic elucidation ( 18 ). Current studies are mostly concentrated on the pathogenesis and treatment of UC or PsO as single diseases, with limited basic research on their comorbidity. Some genomic and transcriptomic analyses have shown intersections between the two in terms of immune response, inflammatory factors, and signaling pathways, such as abnormal IL-23/Th17 axis and upregulation of chemokines; however, the related results are mostly preliminary findings, lacking a systematic co-expression network and comprehensive multi-angle validation ( 19 ). Moreover, research that solely relies on traditional bioinformatics mining is often affected by dataset heterogeneity, insufficient sample size, and limitations of analysis methods, which consequently casts uncertainty upon the robustness and practical clinical applicability of candidate comorbidity genes ( 20 ). Therefore, there is an urgent need to integrate multiple datasets and employ multi-level analytical methods to thoroughly screen and validate the core genes and regulatory networks of comorbidity between UC and PsO, providing a solid foundation for revealing the comorbidity mechanism and identifying potential therapeutic targets ( 21 ). Against this backdrop, the present investigation centers on elucidating the molecular underpinnings of UC–PsO comorbidity. To overcome the constraints inherent in conventional research paradigms, an integrative analytical framework was adopted, comprising differential expression profiling, Weighted Gene Co-expression Network Analysis (WGCNA), and a consensus strategy incorporating multiple mainstream machine learning algorithms ( 22 , 23 ). Differential expression analysis can systematically screen for disease-related genes, while WGCNA helps to reconstruct gene regulatory networks and identify disease-related functional modules and key genes ( 24 ). Various machine learning algorithms further enhance the robustness and biological significance of the screening results from statistical and predictive perspectives, increasing the clinical application potential of candidate genes ( 25 ). The genetic causal nexus between the two disorders was further interrogated using Mendelian randomization. Through cross-validation with multiple methods, we can minimize biases associated with single analyses and improve the accuracy and reliability of identifying key comorbidity genes. This study systematically identified and validated diagnostic genes associated with the comorbidity of UC and PsO through integrated omics analysis and multi-algorithm screening, and explored their roles in immune regulation and inflammatory responses. The findings suggest that CLDN8 and ABCA12 may serve as potential common diagnostic biomarkers for UC and PsO comorbidity, with their involvement in immune cell differentiation and migration potentially linked to the inflammatory processes underlying both diseases. This investigation yields novel perspectives on the molecular pathogenesis underpinning UC–PsO comorbidity and establishes a conceptual framework to inform the future development of precision diagnostic modalities and targeted therapeutic interventions. The integrative approach used here may also inform future studies on comorbid mechanisms in other autoimmune diseases and support the advancement of personalized medicine. 2 Materials and methods 2.1 Data collection and preprocessing The flowcharts were shown in Figure 1 . Transcriptome datasets related to UC and PsO were obtained from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo ). Datasets were selected based on the availability of both disease and healthy control samples, adequate sample size for robust differential expression analysis, unambiguous platform annotation with data amenable to normalization, and complete clinical information—particularly for PsO, where lesional, non-lesional, and control samples were required to be clearly labeled. Datasets generated on incompatible microarray platforms or containing samples with documented pharmacological intervention prior to biopsy collection were excluded. After this rigorous screening, the UC dataset selected includes GSE36807, GSE47908, and GSE16879, which contain normal control samples and UC patient samples; the PsO dataset selected includes GSE13355, GSE14905, and GSE30999, which contain normal control samples and PsO patient samples ( Table 1 ). Raw expression data underwent background adjustment and normalization utilizing R software (v4.3.2). Inter-batch technical variation was mitigated by applying the normalizeBetweenArrays routine implemented within the “limma” package. Subsequent quality assessment was conducted via principal component analysis (PCA), and outlier samples were removed to ensure the reliability of subsequent analyses. Figure 1 Flowchart of this study. Table 1 Date Set Control Disease Tissue Group GSE36807 7 15 UC Colon Training datasets GSE47908 15 39 UC Colon Training datasets GSE16879 12 24 UC Colon Verification datasets GSE13355 122 58 PsO Skin Training datasets GSE14905 49 33 PsO Skin Training datasets GSE30999 85 85 PsO Skin Verification datasets the summary of GEO datasets involving UC and PsO. 2.2 Differentially expressed genes analysis Based on the preprocessed dataset, the R software limma package was used to conduct differential expression genes (DEGs) analysis for both the UC group versus the normal control group and the PsO group versus the normal control group. The criteria for screening differential genes were set as: adjusted P 0.5. The distribution of DEGs was visualized using a volcano plot, where red represents significantly upregulated genes and blue represents significantly downregulated genes, and the number and expression patterns of upregulated and downregulated DEGs in UC and PsO (such as co-upregulation, co-downregulation, inverse regulation, etc.) were statistically analyzed. 2.3 Weighted gene co-expression network analysis The R software “WGCNA” package was used to construct gene co-expression networks for UC and PsO. The optimal soft threshold (power value) was determined by analyzing scale independence and mean connectivity to ensure that the network meets the characteristics of a scale-free topology (fit index > 0.85). Gene clustering was executed utilizing the predetermined optimal soft thresholding power, thereby partitioning genes exhibiting correlated expression profiles into discrete co-expression modules. Module assignment and quantity were subsequently resolved via implementation of the dynamic tree cutting algorithm. Module-phenotype association analysis was used to filter key modules significantly associated with UC and PsO phenotypes ( P < 0.05), and the results were visualized through cluster trees and module-trait relationship heatmaps. 2.4 Shared gene screening and functional enrichment analysis We conducted an intersection analysis of the DEGs between UC and PsO, and further intersected these with key module genes selected through WGCNA, thus defining them as shared candidate genes for UC and PsO. The intersection results are visually presented using a Venn diagram. Functional enrichment analysis of the shared gene set was conducted using the “ clusterProfiler ” package within the R statistical computing environment, encompassing Gene Ontology (GO) annotations—spanning biological process, cellular component, and molecular function categories—as well as pathway enrichment analysis derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, using P<0.05 as the criterion for significant enrichment, and displayed the top enrichment items through a bubble chart. A protein–protein interaction (PPI) network comprising the identified shared hub genes was constructed utilizing the STRING database in conjunction with Cytoscape software. 2.5 Machine learning screening of diagnostic feature genes Leveraging the shared candidate gene set, four distinct machine learning frameworks—namely, Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Generalized Linear Model (GLM)—were deployed to identify diagnostic feature genes within the UC and PsO expression datasets. The intersection of the results from the four algorithms was taken as the core shared diagnostic genes for UC and PsO, with the intersection results displayed in a Venn diagram. 2.6 Diagnostic efficacy validation The diagnostic performance of the core overlapping diagnostic genes was evaluated through both internal and external validation strategies. Internal validation utilized the training dataset, while external validation employed independent GEO datasets, from which only baseline (pretreatment) samples were retained: for UC, baseline samples from GSE16879; for PsO, baseline samples from GSE30999. Expression profiles of the core genes across distinct groups (normal control, UC, and PsO) were visualized using box plots. Diagnostic performance was subsequently assessed via Receiver Operating Characteristic (ROC) curve analysis, with the corresponding area under the curve (AUC) computed. An AUC value exceeding 0.7 was adopted as the threshold for denoting satisfactory diagnostic capacity. 2.7 Immune infiltration analysis Immune cell composition within the UC and PsO expression datasets was characterized using the CIBERSORT algorithm to estimate the relative proportions of distinct immune cell subsets. The differences in immune cells between the control group and the disease group were compar
## Related Clinical Research

- [MicroRNAs in Immune-Related Diseases: Mechanisms, Functions and Therapeutic Perspectives](https://medichelpline.com/clinical-feed/frontiers-in-immunology-14-micrornas-in-immune-related-diseases-mechanism-functions-and-therapeutic.md)
- [Targeting angiogenesis with nanobodies: article unavailable — source details and expected scope](https://medichelpline.com/clinical-feed/frontiers-in-immunology-16-targeting-angiogenesis-advances-in-the-design-and-engineered-applications-of.md)
- [Shared genetic architecture and spatial cellular mapping of psoriasis and metabolic syndrome](https://medichelpline.com/clinical-feed/plos-one-9-shared-genetic-basis-and-spatial-cellular-atlas-of-psoriasis-and-metabolic.md)
- [GLP-1 Receptor Agonists in Rheumatic Disease: Mechanisms, Evidence, and Clinical Considerations](https://medichelpline.com/clinical-feed/pubmed-42706128.md) (DOI: 10.3760/cma.j.cn112137-20260312-00692)
- [Autoimmune Disease Rates in the 10 Years Before Myasthenia Gravis Diagnosis: Nationwide Korean Coh](https://medichelpline.com/clinical-feed/pubmed-42555884.md) (DOI: 10.1212/WNL.0000000000218377)

## Navigation
- [← Back to Infectious Disease Feed](https://medichelpline.com/clinical-feed/infectious-disease.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.