Primary sclerosing cholangitis (PSC) and ulcerative colitis (UC) exhibit a striking clinical comorbidity, with 60–80% of PSC patients concurrently harboring UC, yet the shared immunogenetic mechanisms remain poorly understood. Here, we constructed a multi-omics integrative framework to systematically dissect the cellular and molecular basis of this comorbidity. GWAS meta-analyses were performed for each disease, followed by tissue-level enrichment assessment using QTLEnrich, MAGMA, and gsMap spatial mapping. Single-cell transcriptomic atlases were constructed, and cell-type prioritization was conducted using four complementary methods. Core genes were identified through cross-validation of five algorithms, with subsequent genomic fine-mapping via FUMA and GCTA-COJO. Tissue-level analyses consistently identified the intestine and immune-related tissues as commonly affected. Multi-dimensional evidence integration prioritized natural killer (NK) cells as the core effector cell type for both diseases, supported principally by CELLECT (Cell-type Expression-specific Integration for Complex Traits) heritability enrichment and single-cell differential analysis. Convergence of five gene-level algorithms pinpointed STAT3 as the sole high-confidence comorbidity gene, broadly expressed across immune cell populations and exhibiting tissue-differential alternative splicing. Colocalization identified a high-risk variant (rs3736161) within the STAT3 locus, with conditional analysis revealing 35 additional independent signals. These findings identify the NK cell–STAT3 axis as a central immunogenetic hub connecting PSC and UC, offering potential therapeutic targets for comorbidity management.

Thyroid hormones profoundly modulate hepatic fatty acid and cholesterol synthesis and turnover. Although nonalcoholic fatty liver disease (NAFLD) shows epidemiological links to hypothyroidism, the genetic substrates of this relationship remain unresolved. Integrating large-scale genome-wide association studies with single-cell transcriptomics, spatial transcriptomics, and single-cell chromatin accessibility via state-of-the-art computational approaches, we interrogated the association between NAFLD and hypothyroidism across organ systems, cellular expression landscapes, and molecular–genetic strata. We uncovered pronounced spatial specificity in genetic risk within the liver, prioritized hepatocytes as the principal shared cell type affected, and, leveraging spatial transcriptomics, advanced a dynamic spatiotemporal two-hit model. We further nominated MAGI3, RRNAD1, and PRCC as high-confidence candidate genes and pinpointed a key risk locus, rs926103. These findings deliver a dynamic, testable framework for the full pathophysiological continuum linking NAFLD and hypothyroidism and yield new targets and leads for precision intervention.

Background/Objective: Histone deacetylase inhibitors (HDACi) represent a novel class of antineoplastic agents, yet their comprehensive safety profile warrants further investigation. This study aimed to examine the safety of HDACi using the FDA Adverse Event Reporting System (FAERS) and to explore causal relationships through Mendelian randomization (MR) analysis of drug targets. Methods: Adverse drug event (ADE) reports for Vorinostat, Romidepsin, Belinostat, and Panobinostat submitted to the FAERS from their respective market entry dates through 31 December 2023, were analyzed using disproportionality analyses with four algorithms, supplemented by time-to-onset analysis, logistic regression, and MR analysis. Results: A total of 1360, 1065, 225, and 1234 ADE reports were documented for Vorinostat, Romidepsin, Belinostat, and Panobinostat, respectively. Eight preferred terms, including decreased white blood cell, platelet, and neutrophil counts, hypophosphatemia, hypocalcemia, QT prolongation, increased aspartate aminotransferase, and anemia, exhibited positive signals across all four HDACi. A temporal decline in the risk of most HDACi-related ADEs was observed, and age, gender, and weight were identified as potential confounding factors for important medical events. Notably, MR analysis revealed a positive correlation between HDAC5 expression and serum phosphate levels. Conclusions: This pharmacovigilance study provides hypothesis-generating evidence that hypophosphatemia may represent a potential class effect of HDACi.

Hepatocellular carcinoma (HCC) progression is shaped by crosstalk between the tumor immune microenvironment (TME) and metabolic reprogramming. This study aims to characterize a macrophage–lactylation molecular axis in HCC and to develop a quantitative prognostic stratification model. Using the TCGA-LIHC cohort, differentially expressed genes were intersected with Paeoniflorin (PF)-related targets, HCC disease targets, and macrophage-/lactylation-related genes to identify candidate genes. Prognostic genes were selected through Cox and LASSO-Cox analyses to construct a risk score model, followed by survival analysis and ROC curve evaluation. Immune infiltration was assessed using ESTIMATE and ssGSEA algorithms, and PF–protein binding interactions were explored via molecular docking and molecular dynamics simulations. Intersection analysis identified eight key genes, and prognostic model genes (HNRNPU, LDHA, and NPM1) were used to construct the prognostic model. High-risk patients exhibited significantly poorer overall survival (p < 0.001), with 1- and 3-year AUC values ranging from 0.70 to 0.90. HNRNPU was positively correlated with activated CD4 T cells (r = 0.385) and negatively correlated with eosinophils (r = −0.498). Molecular docking indicated favorable binding of PF to the model proteins, with the highest predicted affinity observed for LDHA (Vina score = −8.9 kcal/mol), and molecular dynamics simulations suggested the formation of a stable LDHA–PF complex during the later stage of the simulation. We propose a prognostic risk model for HCC constructed using three prognostic model genes and provide computational evidence linking PF to key molecular nodes such as LDHA. External cohort validation and experimental studies are warranted.

Background/Objectives: Immune evasion remains a critical barrier to effective hepatocellular carcinoma (HCC) therapy. Lactate dehydrogenase A (LDHA) drives lactate accumulation and histone lysine lactylation (Kla), reshaping the immunosuppressive microenvironment, while bromodomain-containing protein 4 (BRD4) sustains B7-H3 transcription via super-enhancer occupancy. Despite their synergistic roles in the lactate–Kla–B7-H3 immunosuppressive axis, no dual-target inhibitor simultaneously engaging both proteins has been reported. This study aimed to discover dual LDHA/BRD4 inhibitors from natural product libraries using an integrated AI-driven computational pipeline. Methods: We established a multi-tier virtual screening cascade comprising Lipinski/QED drug-likeness filtration, DiffDock-based AI docking, QuickVina binding energy validation, PLIP interaction profiling, 200 ns all-atom molecular dynamics simulations, MM-GBSA binding free energy calculations, and density functional theory analysis. Natural product libraries from COCONUT and CMNPD databases (84,730 compounds post-filtration) were screened against both targets. Results: High-throughput DiffDock screening identified 11 dual-target hits, from which CNP0038114.1 and CMNPD16582 emerged as prioritized lead candidates. All four protein–ligand complexes maintained structural stability throughout MD simulations, with MM-GBSA binding free energies ranging from −27.24 to −32.45 kcal/mol, predominantly driven by van der Waals interactions. DFT calculations revealed distinct electronic profiles: CNP0038114.1 exhibited a narrow HOMO–LUMO gap (2.718 eV) favoring charge-transfer reactivity, whereas CMNPD16582 displayed a larger gap (4.822 eV), suggesting superior chemical stability. Conclusions: This computational study furnishes two novel natural product leads for targeting the lactate–Kla–B7-H3 immunosuppressive axis in HCC, establishing a generalizable AI-driven workflow for dual-target inhibitor discovery.

The co-occurrence of liver cirrhosis (LC) and heart failure (HF) poses considerable clinical challenges, yet the cellular and molecular determinants of this comorbidity remain poorly characterized. To address this, we developed an integrative multi-omics pipeline encompassing GWAS meta-analysis, gsMap-based spatial transcriptomic projection, GeneEnrich functional annotation, single-cell atlas construction, seismicGWAS and ECLIPSER cell-type scoring, eCAVIAR and fastenloc colocalization, hdWGCNA network inference, scTenifoldKnk in silico gene perturbation, and GCTA-COJO fine-mapping. Quality-controlled meta-analysis yielded 12,347,758 and 9,256,862 variant-level associations for LC and HF, respectively. Spatial projection confirmed preferential enrichment of disease signals within embryonic hepatic and cardiac compartments. Pathway analyses disclosed that LC-linked loci were concentrated in lipid metabolic programs, whereas HF-linked loci implicated mitochondrial bioenergetics and lysosomal degradation. At the cellular level, endothelial cells emerged as the dominant HF-associated population. Convergent evidence from five orthogonal algorithms pinpointed CRIM1 as the sole robustly supported shared gene, selectively enriched in HF endothelial cells; virtual perturbation further identified LCP1 and PTPRC as downstream regulatory nodes. Fine-mapping of the chromosome 2 locus harboring rs12476437 revealed multiple statistically independent signals in the vicinity of CRIM1. Collectively, these findings computationally prioritize the endothelial–CRIM1 axis as a previously unappreciated candidate mechanistic bridge between LC and HF requiring experimental validation.