Search Results (3,648)

Long non-coding RNAs (lncRNAs) regulate granulosa cell function, but their stage-specific dynamics across the chicken follicle hierarchy remain unclear under matched endocrine conditions. We performed Ribo-Zero RNA sequencing on granulosa cells from small yellow follicles (SYF), F5, F2, and F1 follicles collected at the preovulatory luteinizing hormone (LH) surge. A multi-predictor pipeline (CPC2, CNCI, CPAT, PfamScan) identified 26,923 stringently filtered lncRNAs together with 15,838 mRNAs. Consecutive stage comparisons detected 2094, 1085, and 4318 differentially expressed genes and 671, 267, and 2762 differentially expressed lncRNAs in F5 vs. SYF, F2 vs. F5, and F1 vs. F2, respectively, with the most extensive remodeling at the F2-to-F1 transition. The F1 vs. SYF contrast captured the cumulative transcriptional difference across the hierarchy. Enrichment and temporal clustering showed that early hierarchical stages were characterized by proliferative, metabolic, and steroidogenic programs, whereas F1 granulosa cells were enriched for extracellular-matrix remodeling, MAPK signaling, and calcium ion binding. Weighted gene co-expression network analysis identified 10 stage-associated modules and highlighted candidate lncRNAs linked to lipid metabolism, angiogenesis, extracellular-matrix remodeling, and DNA repair, including G5825–MYLIP, G66587–VEGFA, and G60212–CKS1B. qPCR validation confirmed concordant expression trends for eight representative pairs. These results define a stage-resolved lncRNA–mRNA landscape across chicken follicle development and provide candidates for mechanistic studies of follicle maturation and periovulatory remodeling. Full article

We compared the cytokine profiles between two lupus low disease activity state (LLDAS) subgroups—clinically active (CA) and serologically active clinically quiescent (SACQ)—and identified predictors of disease flare. Fifty patients with systemic lupus erythematosus (25 CA, 25 SACQ) who maintained LLDAS for ≥6 months were enrolled and followed for 6 months. Cytokine modules were identified using weighted gene co-expression network analysis, and correlations with clinical traits were assessed. Predictors of flare were assessed using Cox regression. Three cytokine modules were identified. The brown (MCP-1 and IL-8) and turquoise (IFN-α, IFN-γ, IL-17A, IL-10, IL-12p70, IL-18, IL-23A, and IL-33) modules correlated with mucocutaneous and physician global assessment, respectively. These modules showed a positive, but not significant, correlation with CA. The comparison analysis revealed that IL-6 and IL-8 were higher in CA than in SACQ. Nine patients (18%) flared, six of whom belonged to the CA group. Flares were associated with a lower sustained LLDAS rate (77.8% vs. 34.1%) and higher levels of IL-1β, IL-6, and IL-33. In multivariable analysis, non-sustained LLDAS (HR 8.73) and IL-6 ≥ 45.1 pg/mL (HR 10.4) independently predicted a flare. Our study demonstrated that cytokine elevation persists despite LLDAS. Non-sustained LLDAS and elevated IL-6 predict a flare, suggesting that IL-6 may enhance the flare prediction biomarker. Full article

Background/Objectives: Alzheimer’s disease (AD) remains a progressive neurodegenerative disorder with limited therapeutic options. This study aimed to develop an integrative multi-omics computational pipeline to identify diagnostic biomarkers and prioritize druggable therapeutic targets for AD. Methods: We integrated transcriptomic data from 1047 samples (547 AD, 500 controls) using weighted gene co-expression network analysis (WGCNA) and three machine learning algorithms (LASSO, Random Forest, SVM) with strict separation of training, feature selection, and evaluation. Single-cell RNA sequencing of 48,481 nuclei from entorhinal cortex, two-sample Mendelian randomization (MR) with Bayesian colocalization, and structure-based molecular docking with triplicate 500 ns molecular dynamics (MD) simulations were also employed. Results: Machine learning identified 10 consensus biomarker genes involved in synaptic vesicle cycling, ion transport, and calcium homeostasis (internal test AUC = 0.891, 95% CI: 0.836–0.946; external validation on GSE48350: AUC = 0.847, 95% CI: 0.798–0.896). Covariate-adjusted differential expression and MR with Bayesian colocalization converged on eight immune-related therapeutic targets including APOE, TREM2, and TYROBP ($p<0.05$; Bonferroni-corrected threshold $p<0.00625$). Single-cell analysis revealed oligodendrocyte expansion in AD (28.5% versus 24.8%), with target genes predominantly expressed in microglia and astrocytes. Virtual screening of 2634 FDA-approved drugs prioritized 10 exploratory repurposing candidates; indomethacin–TREM2 and celecoxib–CSF1R are primary exploratory candidates given structurally validated binding pockets. Triplicate MD simulations (15 $\mathsf{\mu }$s aggregate) showed force-field-consistent structural stability (RMSD ≤ 3.2 Å). A quantitative multi-omics convergence framework identified four Tier 1 targets (APOE, TREM2, TYROBP, CX3CR1) supported by ≥5 analytical layers ($P_{\mathrm{perm}}=0.0003$; note: three of five layers share the same transcriptomic input). Conclusions: These findings provide a multi-evidence computational framework linking diagnostic biomarkers and druggable neuroinflammatory targets for AD. All predictions require experimental validation in biochemical and cellular models before clinical conclusions can be drawn. Full article

Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with poor prognosis and limited therapeutic options. Although epigenetic dysregulation is a hallmark of HCC, rational combinatorial targeting strategies remain incompletely defined. Here, we identify cooperative oncogenic functions of the chromatin modifiers enhancer of zeste homolog 2 (EZH2) and lysine-specific demethylase 1 (LSD1) in HCC. Analysis of the TCGA-LIHC cohort revealed that co-elevated EZH2 and LSD1 expressions are significantly associated with reduced overall survival. Gene set enrichment analysis demonstrated enrichment of Sonic Hedgehog (SHH) signaling and stress-responsive transcriptional programs in tumors with high EZH2/LSD1 expression. Functionally, dual pharmacological inhibition of EZH2 (GSK126) and LSD1 (SP2509) suppressed HCC cell proliferation, induced G1-phase arrest, and enhanced apoptosis, as evidenced by increased caspase-3/7 activity and decreased pro-caspase levels. Dual inhibition also impaired migration, invasion, tumor sphere formation, and stemness-associated gene expression. Mechanistically, co-targeting disrupted SHH signaling through the suppression of GLI1 expression. Chromatin immunoprecipitation revealed reduced EZH2, LSD1, and STAT3 occupancy at the GLI1 promoter following dual inhibition, leading to the repression of GLI1 and its downstream targets. Collectively, these findings demonstrate that EZH2 and LSD1 cooperatively sustain GLI1-dependent SHH signaling in HCC, and that dual epigenetic inhibition represents a mechanistically defined therapeutic strategy. Full article

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies. Full article

Nosema ceranae is a common and highly contagious fungal pathogen that primarily infects the gut of adult honeybees, causing nosemosis. As a chronic disease of the digestive system, it poses a global threat to honeybee health and colony sustainability. This study aimed to investigate the inhibitory effects of different concentrations of Scutellaria baicalensis aqueous extract on N. ceranae in the intestines of infected Apis cerana through feeding experiments. In addition, the therapeutic efficacy of its major active component, baicalin, was evaluated, and its potential molecular mechanisms of action were explored. The results showed that, compared with the control group, administration of S. baicalensis aqueous extract at concentrations of 1 mg/mL, 5 mg/mL, and 10 mg/mL significantly reduced midgut spore loads (p < 0.05). Further experiments showed that a 0.5 mg/mL baicalin sucrose solution, prepared with 0.5% (v/v) DMSO as co-solvent, exhibited optimal solubility and significantly inhibited the proliferation of spores in the honeybee midgut. Transcriptomic analysis of A. cerana revealed varying numbers of significantly differentially expressed genes among the baicalin-treated (HG) group, the co-solvent control (DMSO) group, and the blank control (C) group. Four candidate DEGs associated with the effects of baicalin were further identified, namely LOC108003965, LOC108000905, LOC107996681, and CYP4G11. Gene Ontology enrichment analysis showed that, in the comparison between the HG group and the C group, these DEGs were significantly enriched in six functional categories: iron ion binding, phosphoric ester hydrolase activity, heme binding, tetrapyrrole binding, hydrolase activity (acting on ester bonds), and oxidoreductase activity (acting on paired donors, with incorporation or reduction of molecular oxygen). Collectively, these results demonstrate that S. baicalensis aqueous extract effectively inhibits the proliferation of N. ceranae within the host, and its active component, baicalin, exhibits a similar inhibitory effect. The present study proposes a novel strategy in which baicalin may enhance host endogenous chitinase-related activity to target and disrupt the spore wall, offering a new perspective for the prevention and control of honeybee nosemosis. Full article

Background/Objectives: Zebrafish fin regeneration serves as a classic model for investigating vertebrate tissue regeneration, yet the core regulatory networks and their crosstalk with the immune microenvironment remain incompletely characterized. This study aimed to identify hub genes, and elucidate the underlying molecular mechanisms and immune microenvironment dynamics during zebrafish fin regeneration. Methods: We integrated multiple bulk RNA-seq datasets of zebrafish fin regeneration from the GEO database, followed by data standardization with batch effect removal. Hub genes were screened via differential expression analysis, weighted gene co-expression network analysis (WGCNA), and predictive models constructed with 13 classic machine learning algorithms. Functional enrichment, time-ordered gene co-expression network (TO-GCN) method, immune infiltration analyses and RT-qPCR validation were further performed. Results: We identified upregulated differentially expressed genes, regeneration-correlated gene modules and their overlapping genes, including 82 candidate genes and 10 hub genes enriched in cytoskeleton remodeling, extracellular matrix organization, and focal adhesion. Temporal analysis uncovered hierarchical gene regulation and functional switching during regeneration. Hub gene expression was significantly correlated with the infiltration of B cells, M1/M2 macrophages and CD8+ T cells, revealing a stage-specific immune microenvironment. RT-qPCR validation showed high consistency with the multi-omics data. Conclusions: This study provides potential gene targets for understanding zebrafish fin regeneration, and offers a valuable reference for investigating the crosstalk between regulatory networks and the immune microenvironment in vertebrate tissue regeneration. Full article

Background/Objectives: Although clinically distinct, bipolar disorder (BP), schizophrenia (SZ), major depressive disorder (MDD), and social anxiety disorder (SAD) share fundamental biology. We mapped these transdiagnostic systemic mechanisms. Methods: Weighted Gene Co-Expression Network Analysis (WGCNA) of peripheral blood RNA-Seq datasets evaluated module preservation, hub gene disruption, and microRNA (miRNA) networks. Results: Seven modules showed robust cross-disease preservation. Overall, 56 of 105 candidate hub genes exhibited altered expression, with 22 passing the false discovery rate (FDR) correction. Hubs like IL1B, TLR2, and MMP9 dominated networks linked to altered inflammatory signaling and structural remodeling. Downregulated ribosomal hubs characterized systemic metabolic stress. Discussion: These signatures capture extensive systemic dysregulation. Inflammation and metabolic shifts correlate strongly with pathways regulating chronic neuroinflammation, epigenetic control, and dendritic pruning. Computational models suggest these cascades evade miRNA controls, potentially compromising structural neural plasticity. Conclusions: This shared transcriptomic architecture challenges rigid diagnostic boundaries. Identifying systemic immune dysregulation and translational alterations as core pathogenic denominators provides a rationale for transdiagnostic therapies targeting upstream systemic networks to mitigate neural vulnerabilities. Full article

The m⁶A RNA methylation pathway plays a critical role in host antiviral defense. Host cells employ m⁶A readers such as YTHDF2 to regulate viral RNA fate through diverse mechanisms, including degradation, translational control, and immune recognition. However, we found that YTHDF2 is essential for SARS-CoV-2 replication, suggesting that a virus may exploit this host machinery to its advantage. Through integrative RNA-proteome analysis, we identified the SARS-CoV-2 nucleocapsid (N) transcript as the most heavily m⁶A-modified viral transcript and a direct interactor of YTHDF2. The N protein forms a complex with YTHDF2 in the cytoplasm and redirects this host RNA decay machinery toward host antiviral transcripts. N suppresses ISG15, IFIT1, MX1 and pro-inflammatory cytokines in a largely YTHDF2-dependent manner, an effect that is lost in YTHDF2-knockout cells. These findings reveal a viral immune evasion strategy wherein a viral protein actively hijacks an m⁶A reader to silence antiviral gene expression, establishing the N-YTHDF2 axis as a therapeutic target against SARS-CoV-2 and other coronaviruses. Full article

The primary processing shapes the taste characteristics of coffee beans, while the regulation pathways remain unclear. Coffee beans processed by five methods—dry processing (DP), wet processing (WP), red honey (RH), black honey (BH) and anaerobic fermentation (AF)—were evaluated using electronic tongue analysis, sensory evaluation, and untargeted metabolomics. Sensory evaluation scores for mouthfeel, balance, and overall were higher in BH and AF. Conversely, the WP and DP exhibited heightened bitterness and astringency responses on the electronic tongue sensors, particularly for the former. The multigroup metabolomic comparison identified 808 DMs, and WGCNA revealed eight sensory-related modules containing 467 hub metabolites, mainly amino acids and derivatives, organic acids, alkaloids, and phenolic acids. KEGG analysis demonstrated that pathways such as caffeine metabolism and glycerophospholipid metabolism were the main pathways responsible for the metabolic differences. Further correlation analysis revealed potential flavor components closely associated with key taste characteristics. 1,3,4,5-tetrahydroxycyclohexanecarboxylic acid and Tyr demonstrated positive associations with bitterness, while TPC, TFC, Gly, and Met exhibited negative correlations with bitterness and astringency. Glu demonstrated a positive correlation with umami. These findings elucidate the material basis by which the primary processing modulates non-volatile compounds and taste perception, offering new insights into enhancing coffee quality. Full article

Miscanthus lutarioriparius exhibits strong potential for cadmium (Cd) accumulation, making it a promising candidate for the phytoremediation of Cd-contaminated soils. However, its full remediation potential remains underexploited, highlighting the need for targeted genetic improvement This study presents a comprehensive genome-wide identification and systematic characterization of 20 Ml4CL (4-coumarate: CoA ligase genes) in the M. lutarioriparius. Results indicate that the Ml4CL gene family has undergone substantial evolutionary divergence and expansion. Phylogenetic classification is highly consistent with gene structures ad conserved motifs suggesting potential functional diversification. Promoter analysis revealed a complex cis-regulatory landscape enriched in n ABA- and light-responsive elements, frequently co-occuring with hormone-responsive elements associated with jasmonic acid (JA), gibberellins (GAs), salicylic acid (SA), and strigolactones (SLs) signaling. This pattern suggests that the Ml4CL family may function as an integrative regulatory node linking multiple stress and hormonal signaling pathways. Importantly, under Cd stress, Ml4CL genes exhibited diverse expression dynamics, including gene-specific repression and dose-dependent biphasic responses. Notably, Ml4CL4 showed strong repression, while other members displayed “induction-then-repression” or “repression-then-induction” patterns, suggesting a staged or hierarichical transcriptional response. These findings further suggest that Cd-responsive signaling networks may involve non-linear or threshold-dependent mechanismsthat activate distinct transcriptional programs depending on stress levels. Collectively, this study highlights the regulatory role of the Ml4CL family in plant adaptation to complex environments and identifies candidate dose-resonsive regulatory elements and key allelic variations. These findings provide valuable targets for molecular breeding and synthetic biology aimed at improving crop stress resilience. Full article

Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient outcomes, yet reliable biomarkers are lacking. This study integrated transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE96804, GSE30528, and GSE142025) with machine learning algorithms and Mendelian randomization (MR) to identify diagnostic biomarkers for DKD. Differentially expressed genes (DEGs) were identified and intersected with key modules from weighted gene co-expression network analysis (WGCNA). Four machine learning methods—least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost)—were applied for feature selection. Five hub genes (SPP1, CD44, VCAM1, C3, and TIMP1) were identified at the intersection of these approaches. Two-sample MR analysis using eQTL data from the eQTLGen Consortium and kidney function GWAS from the CKDGen Consortium provided evidence supporting potential causal associations between SPP1, C3, and TIMP1 expression and estimated glomerular filtration rate decline. Immune infiltration analysis via CIBERSORT estimated elevated proportions of M1 macrophages and activated CD4${}^{+}$ memory T cells in DKD samples, with all five hub genes showing correlations with macrophage infiltration. A diagnostic model based on these five genes achieved a cross-validated area under the receiver operating characteristic curve (CV-AUC) of 0.938 in the discovery dataset and AUC values of 0.917 and 0.889 in two independent external validation cohorts. Drug–gene interaction analysis identified 10 candidate compounds targeting the hub genes. These findings provide a computational framework for identifying candidate diagnostic biomarkers and generating hypotheses regarding potential therapeutic targets for DKD; however, all results are derived from in silico analyses and require experimental validation—including qPCR, immunohistochemistry, and prospective clinical cohort studies—before clinical applicability can be established. Full article

Endosymbiotic bacteria in insects are known to influence plant–insect interactions by altering host plant physiology. This study reveals that the endosymbiont Wolbachia significantly impairs photosynthesis in cotton plants. Comparative transcriptomic analysis of cotton leaves infested by Wolbachia-infected spider mites (Tt-I) and uninfected spider mites (Tt-UI) identified 1912 differentially expressed genes (DEGs). Photosynthesis was the most adversely affected biological process, with 17 genes downregulated in the photosynthesis pathway (e.g., key genes psbW and PETF), as supported by GO and KEGG enrichment analyses. Gene co-expression network analysis further highlighted core genes involved in photosynthesis disruption and carbon fixation. Physiological assessments showed that Wolbachia infection led to significantly reduced chlorophyll content and elevated reactive oxygen species (ROS) levels, inducing oxidative stress. These findings demonstrate that Wolbachia disrupts cotton photosynthesis through transcriptional repression and ROS-mediated oxidative stress, providing novel insights into plant–insect-symbiont interactions and a theoretical basis for managing mite pests in cotton. Full article

The bone marrow tumor microenvironment (TME) is critical for acute myeloid leukemia (AML) progression, immune evasion, and treatment resistance. SRC, a non-receptor tyrosine kinase involved in multiple oncogenic pathways, has not been systematically characterized in AML in relation to prognosis and immune regulation. We integrated bulk transcriptomic and single-cell RNA-sequencing datasets from TCGA, BeatAML, and GEO. Immune-related targets were identified using xCell-based immune scoring and weighted gene co-expression network analysis (WGCNA), followed by protein–protein interaction analysis and multi-algorithm machine-learning screening. We then evaluated SRC expression patterns, prognostic associations, immune microenvironment features, predicted drug sensitivity, single-cell differentiation dynamics, intercellular communication, and in silico virtual knockout perturbation (scTenifoldKnk). SRC emerged as the most robust hub gene after integration of WGCNA, PPI analysis, machine-learning feature selection, and survival screening. SRC was significantly upregulated in AML compared with normal controls and was independently associated with poor overall survival (HR = 1.231, p = 0.037). High SRC expression was linked to adverse ELN/FAB features, increased immune checkpoint expression, enrichment of inflammatory and immunoregulatory pathways, and a higher proportion of primitive leukemia-associated cell states. Single-cell analyses further suggested that SRC was enriched in CD34⁺ progenitor compartments, associated with altered cell–cell communication, and accompanied by distinct mutation and pathway profiles. Drug-response prediction and in silico network perturbation analysis further supported the potential biological and translational relevance of SRC-centered programs. SRC is a prognostically relevant and immune-associated hub linked to AML microenvironment remodeling, and may serve as a candidate biomarker and potential therapeutic target that warrants further experimental validation. Full article

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article