Causal association and shared mechanisms between Graves' disease and prostate cancer: insights from Mendelian randomization, machine learning, and comprehensive bioinformatics.

Causal relationship and pathogenic mechanism analysis of breast cancer and coronary heart disease based on mendelian randomization and transcriptome data analysis

Integrating Mendelian randomization, machine learning and retrospective clinical data: an exploratory analysis of the cross-disease association between CHB and PD, with a focus on eosinophil alterations

Background Epidemiological studies on the association between chronic hepatitis B (CHB) and Parkinson’s disease (PD) have yielded inconsistent findings, with causality obscured by confounding and limited mechanistic evidence. Methods The study employed an integrated approach encompassing two-sample Mendelian randomization(MR), multi-omics analysis, machine learning-driven gene screening, immune infiltration profiling, and multicenter retrospective clinical validation, with retrospective clinical validation conducted in two independent cohorts. Results MR provided evidence suggesting a genetically predicted inverse association between susceptibility to CHB and the risk of PD (OR = 0.82–0.94, p < 0.05). Through the integration of machine learning and multi-omics data, RTN3 and MAP4K3 were recognized as priority cross-disease genes linking these two conditions. Phenylalanine metabolism emerged as an amino acid pathway showing consistent dysregulated patterns between the two diseases, with peripheral phenylalanine levels exhibiting a divergent trend: elevated in CHB while relatively lower in PD. Immune infiltration analysis and clinical hematological data suggested that eosinophil levels tended to decline in CHB but rise in PD, and such divergent expression patterns may be linked to the observed inverse correlation between CHB and PD susceptibility. (OR = 8.99, p < 0.001). Conclusion Genetic susceptibility to chronic hepatitis B is inversely associated with Parkinson’s disease risk. The shared pathophysiological landscape potentially involves MAP4K3, RTN3, phenylalanine metabolism, and eosinophil. These factors represent candidate therapeutic targets and peripheral biomarkers for PD risk reduction.

Shared Genetic Basis of Autoimmune Diseases and Follicular Lymphoma by Mendelian Randomization and Multi-Omics

Background/Objectives: Epidemiological studies link autoimmune diseases (AIDs) to follicular lymphoma (FL) risk, but their shared genetic architecture and causal mechanisms remain unclear. Methods: A two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships between 15 AIDs and FL. Pleiotropic loci were identified through the Pleiotropy Analysis under Composite Null (PLACO). Bayesian colocalization analysis, functional mapping, and Multi-marker Analysis of GenoMic Annotation were applied to fine-map shared genetic variants and identify their target genes. Summary data-based MR was used with multitissue expression quantitative trait locus data to infer causal effects of gene expression. HyPrColoc analysis was applied to decipher shared genetic regulation of immune cell phenotypes. Results: MR revealed that rheumatoid arthritis increased FL risk (ORIVW = 1.55, nominal p = 7.16 × 10−5, FDR-corrected p = 1.07 × 10−3), whereas composite autoimmune disease reduced FL risk (ORIVW = 0.70, nominal p = 4.61 × 10−5, FDR-corrected p = 6.92 × 10−4). Hypothyroidism showed only a nominally suggestive protective trend (ORIVW = 0.88, nominal p = 0.015), which did not survive Benjamini–Hochberg multiple-testing correction (FDR-corrected p = 0.075). Fifty-five pleiotropic loci shared between FL and AIDs were identified, among which key loci such as 1p36.32, 6p21.32, 17p13.1, and 11q23.3 exhibited strong colocalization evidence. Core pleiotropic genes (e.g., TNFRSF14, MMEL1, CXCR5, and RNASET2) were prioritized, which implicated pathways related to MHC class II antigen presentation, interferon signaling, and T cell activation. HyPrColoc analysis demonstrated that these loci colocalized with the expression of immune receptors, including BAFF-R on B cells and HVEM (TNFRSF14) on naïve CD8+ T cells. Conclusions: Our study identifies divergent causal effects of selected AIDs on FL risk and demonstrates localized pleiotropy at key loci, providing novel insights into shared immunogenetic mechanisms.

Modifiable risk factors and skin cancers: a multi-omics Mendelian randomization study from causal inference to drug target discovery

Introduction Recent studies have linked modifiable risk factors (RFs) to melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). This study aimed to investigate the causal relationships between 11 modifiable RFs and these skin cancers and to identify novel therapeutic targets using multi-omics approaches. Methods Exposure data were obtained from genome-wide association studies (GWAS). Mendelian randomization (MR) analyses were performed using the inverse variance weighted (IVW) method as the primary approach, and results from discovery and replication cohorts were combined by meta-analysis. Functional Mapping and Annotation (FUMA) and summary-data-based MR (SMR) were used to prioritize therapeutic targets. Drug prediction, phenome-wide association studies (PheWAS), and single-cell analyses were conducted to evaluate target druggability and biological relevance. Results Actinic keratosis (AK) was associated with an increased risk of melanoma (OR = 1.24, 95% CI 1.07-1.43, P < 0.01), whereas alcohol consumption was negatively associated with SCC risk (OR = 0.77, 95% CI 0.62-0.95, P = 0.02). No causal relationships were observed between the investigated RFs and BCC. One potential therapeutic target for melanoma (EDEM2, PSMR = 0.03) and five candidate therapeutic targets for SCC (MAPK3, PSMR = 5.30E-04; NRBP1, PSMR = 4.32E-04; ANKK1, PSMR = 1.89E-06; IL27, PSMR = 3.04E-03; ADH5, PSMR = 0.02) were identified. Drug prediction, PheWAS, and single-cell analyses further supported the therapeutic potential of these genes. Discussion AK appears to increase the risk of melanoma, whereas alcohol consumption may be protective against SCC. EDEM2 may represent a potential therapeutic target for melanoma, while MAPK3, NRBP1, ANKK1, IL27, and ADH5 are promising candidate targets for SCC. Further experimental and clinical studies are warranted to validate these findings.

Potential Shared Immunometabolic Signatures Between Familial Hypercholesterolemia and Major Depressive Disorder: Integrative Transcriptomic Analysis, Mendelian Randomization, and Clinical Validation

Familial hypercholesterolemia (FH) has been associated with an increased risk of major depressive disorder (MDD), but their shared molecular signatures remain unclear. This study aimed to identify candidate genes associated with FH-MDD co-occurrence through integrative transcriptomic analysis, machine learning, summary-data-based Mendelian randomization (SMR), and clinical validation. Transcriptomic datasets from the Gene Expression Omnibus were analyzed, including GSE6054 and GSE13985 for FH and GSE98793 for MDD. Differential expression analysis and weighted gene co-expression network analysis were used to identify shared candidate genes. Candidate biomarkers were screened using least absolute shrinkage and selection operator regression and Random Forest algorithms, followed by the construction of an exploratory nomogram. SMR analysis was performed to assess genetically supported associations between candidate-gene expression and MDD risk. Selected genes were validated by PBMC RT-qPCR in an independent four-group clinical cohort comprising healthy controls and participants with FH, MDD, or co-occurring FH and MDD. Flow cytometry was subsequently used to characterize the peripheral CD4+ T-cell profile. A total of 54 shared candidate genes were identified. CD4, MRPS21, and CRTC2 were selected by machine learning and incorporated into the nomogram, which showed good discrimination in the training set and moderate performance in external validation. Immune infiltration and enrichment analyses highlighted monocyte alterations, reduced T-cell-related signals, and enrichment of T-cell receptor and TNF-mediated pathways. SMR analysis indicated that genetically predicted higher CD4 expression was inversely associated with MDD risk, whereas RT-qPCR showed higher CD4 expression in patients with FH-MDD. Flow-cytometric analysis showed that FH-MDD participants had a higher circulating CD3+CD4+ T-cell proportion, an increased effector-memory CD4+ T-cell proportion, and a higher proportion of HLA-DR+ CD4+ T cells than participants in the comparison groups. These findings suggest that CD4 may represent a promising immune-related candidate signature associated with the co-occurrence of FH and MDD, warranting further functional validation.

Integrated Mendelian Randomization and Single-Cell Transcriptomics Reveal T Cell Immune Mechanisms in Systemic Lupus Erythematosus–Bladder Cancer Comorbidity

Objective: Patients with systemic lupus erythematosus (SLE) exhibit elevated malignancy risk, with increased bladder cancer incidence. This study integrated Mendelian randomization (MR) with single-cell sequencing (scRNA-seq) to nominate exploratory prioritized candidate genes in SLE–bladder cancer comorbidity and their T cell regulatory roles. Methods: Single-cell datasets for SLE (GSE266852) and bladder cancer (GSE222315) were retrieved from GEO. Quality control, clustering, and annotation were performed using Seurat. T cell differentially expressed genes were intersected for bidirectional two-sample MR using IEU Open GWAS statistics. Heterogeneity, pleiotropy, and sensitivity analyses assessed robustness. GeneMANIA, miRNA databases, and CTD were used for network and functional analyses. Wilcoxon tests and Monocle 2 were used to characterize expression and T cell differentiation trajectories. Results: Cross-disease intersection nominated 1010 candidate genes. In an exploratory MR screen (uncorrected p < 0.05), four candidate genes were nominated (GBP3, LMAN1, SLC40A1, MIS18BP1); none survived FDR correction in both directions. At the uncorrected threshold, LMAN1 showed a shared risk direction (OR > 1) and MIS18BP1 a protective direction (OR < 1). Both showed significant T cell differential expression (p < 0.001) and elevated late differentiation expression. LMAN1 was involved in COPII vesicle transport; MIS18BP1 in CENP-A chromatin assembly. Twenty high-confidence miRNAs targeted each gene. CTD indicated liver injury associations and cisplatin/cyclosporine interactions. Conclusions: LMAN1 and MIS18BP1 are proposed as hypothesis-generating exploratory candidate genes in SLE–bladder cancer comorbidity, potentially involved in immune dysregulation through T cell terminal differentiation modulation. This study provides preliminary evidence suggestive of autoimmune–malignancy comorbidity mechanisms.