Background: Circadian rhythms have been shown to play a significant role in the etiology and progression of immune‐related morbidities, including cancer and autoimmune diseases. As an autoimmune disorder, psoriatic arthritis (PsA) has been underexplored in the context of circadian rhythms. This study aimed to explore the relationship between circadian rhythm and PsA.Methods: We conducted a Summary‐data–based Mendelian randomization (SMR) analysis, obtaining summary data on gene methylation, expression, and protein abundance levels of circadian clock‐related genes (CRGs) from European ancestry individuals, with a total of 1749 genes selected from the GeneCards database using “biological clock” as the search term. The discovery cohort was obtained from the GWAS catalog database, and the replication cohort was obtained from the FinnGen database. Candidate genes related to circadian rhythm and PsA were identified through research, and their pharmacological potential and molecular docking were further validated as drug targets.Results: After integrating multi‐omics data, we identified 11 methylation sites in three genes (HLA‐DQB1, ITPR3, and GABBR1) of CRGs that were causally related to PsA, and the effects produced were not consistent. The three gene expressions of CRGs (IL4, HLA‐DQB1, and OPI‐AIS5) were related to PsA. At the protein level, we identified three proteins (GCKR, STAT3, and CSNK2B) of CRGs related to PsA. The top 20 drug candidates underwent drug prediction screening, resulting in the identification of 12 compounds that demonstrated effective outcomes with three (HLA‐DQB1, STAT3, and IL‐4) specific therapeutic targets through molecular docking.Conclusion: This study suggests altered expression of circadian clock genes and proteins, including HLA‐DQB1, ITPR3, GABBR1, IL4, OIP5‐AS1, GCKR, CSNK2B, and STAT3, as factors contributing to the increased risk of PsA.

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.

Background: Melanoma, a type of skin cancer that can spread to other parts of the body, currently lacks highly precise individualized treatment options. Methods: We performed multi-omics integration on The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort to identify melanoma molecular subtypes. The identified genes were validated in independent meta cohorts from GEO, followed by transcriptome-wide association study (TWAS) validation using Genotype-Tissue Expression (GTEx) and UK Biobank datasets. Additionally, we analyzed machine learning-driven signature (CMLS) development, tumor microenvironment characteristics, immunotherapy response, and potential therapeutic targets. Finally, single-cell and spatial transcriptomics provided further biological insights and the pathomechanisms. Result: Our study identified two distinct molecular subtypes of SKCM using multimodal data integration with the MOVICS package: Cancer Subtype 1 (CS1) and CS2. CS2 showed a better prognosis and was enriched in immune-suppressive pathways such as WNT–β signaling, while CS1 exhibited higher activation of the PI3K pathway and DNA repair mechanisms, along with greater tumor invasiveness. TWAS analysis results combined the findings from TCGA-SKCM and the meta-cohort, identifying six significant prognostic-related genes (SPRGs). The CMLS prognostic model, based on SPRGs ( CAP2 , SELL , and LAPTM5 as risk factors and GZMA , FCER1G , and LYZ as protective factors), stratified patients into high-group (poorer survival) and low-risk groups. Single-cell and spatial transcriptomic analyses further validated CMLS prognostic results, highlighting distinct tumor microenvironment interactions and progression trajectories. Conclusion: Identifications of molecular subtypes and CMLS represent a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice foundation for personalized therapies.