PRCP is a promising drug target for intracranial aneurysm rupture supported via multi-omics analysis.

BACKGROUND: Cerebral aneurysms are life-threatening cerebrovascular disorders. Currently, there are no effective treatments for preventing disease progression. Mendelian randomisation (MR) is widely used to repurify licensed drugs and identify new therapeutic targets. Therefore, this study aims to investigate effective drug targets for preventing the formation and rupture of cerebral aneurysms and analyse their potential mechanisms. METHODS: We performed a comprehensive study integrating two-sample MR analysis, colocalisation analysis and summary data-based Mendelian randomisation (SMR) to assess the causal effects of blood and brain druggable cis-expression quantitative trait loci (cis-eQTLs) on intracranial aneurysm (IA), unruptured intracranial aneurysm (UIA) and subarachnoid haemorrhage of IA rupture (SAH). Druggable genes were obtained from the study by Chris Finan et al, cis-eQTLs from the eQTLGen and PsychENCODE consortia. Results were validated using proteomic and transcriptomic data. Single-gene functional analyses probed potential mechanisms, culminating in the construction of a drug-gene regulation network. RESULTS: Through the MR analysis, we identified four potential drug targets in the blood, including prolylcarboxypeptidase (PRCP), proteasome 20S subunit alpha 4 (PSMA4), LTBP4 and GPR160 for SAH. Furthermore, two potential drug targets (PSMA4 and SLC22A4) were identified for IA and one potential drug target (KL) for UIA after accounting for multiple testing (P(inverse-variance weighted)<8.28e-6). Strong evidence of colocalisation and SMR analysis confirmed the relevance of PSMA4 and PRCP in outcomes. Elevated PRCP circulating proteins correlated with a lower SAH risk. PRCP gene expression was significantly downregulated in the disease cohort. CONCLUSIONS: This study supports that elevated PRCP gene expression in blood is causally associated with the decreased risk of IA rupture. Conversely, increased PSMA4 expression in the blood is causally related to an increased risk of IA rupture and formation.

Prioritization of therapeutic targets for cancers using integrative multi-omics analysis.

BACKGROUND: The integration of transcriptomic, proteomic, druggable genetic and metabolomic association studies facilitated a comprehensive investigation of molecular features and shared pathways for cancers' development and progression. METHODS: Comprehensive approaches consisting of transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR) and MR were performed to identify genes significantly associated with cancers. The results identified in above analyzes were subsequently involved in phenotype scanning and enrichment analyzes to explore the possible health effects and shared pathways. Additionally, we also conducted MR analysis   to investigate metabolic pathways related to cancers. RESULTS: Totally 24 genes (18 transcriptomic, 1 proteomic and 5 druggable genetic) showed significant associations with cancers risk. All genes identified in multiple methods were mainly enriched in nuclear factor erythroid 2-related factor 2 (NRF2) pathway. Additionally, biosynthesis of ubiquinol and urate were found to play an important role in gastrointestinal tumors. CONCLUSIONS: A set of putatively causal genes and pathways relevant to cancers were identified in this study, shedding light on the shared biological processes for tumorigenesis and providing compelling genetic evidence to prioritize anti-cancer drugs development.

Causal Effects of Blood Metabolites and Obstructive Sleep Apnea: A Mendelian Randomization Study.

BACKGROUND: Obstructive sleep apnea (OSA) is one of the most common forms of sleep-disordered breathing. Studies have shown that certain changes in metabolism play an important role in the pathophysiology of OSA. However, the causal relationship between these metabolites and OSA remains unclear. AIMS: We use a mendelian randomization (MR) approach to investigate the causal associations between the genetic liability to metabolites and OSA. METHODS: We performed a 2-sample inverse-variance weighted mendelian randomization analysis to evaluate the causal effects of genetically determined 486 metabolites on OSA. Multiple sensitivity analyses were performed to assess pleiotropy. We used multivariate mendelian randomization analyses to assess confounding factors and mendelian randomization Bayesian model averaging to rank the significant biomarkers by their genetic evidence. We also conducted a metabolic pathway analysis to identify potential metabolic pathways. RESULTS: We identified 14 known serum metabolites (8 risk factors and 6 protective factors) and 12 unknown serum metabolites associated with OSA. These 14 known metabolites included 8 lipids( 1-arachidonoylglycerophosphoethanolamine, Tetradecanedioate, Epiandrosteronesulfate, Acetylca Glycerol3-phosphate, 3-dehydrocarnitine, Margarate17:0, Docosapentaenoaten3;22:5n3), 3 Aminoacids (Isovalerylcarnitine,3-methyl-2-oxobutyrate,Methionine), 2 Cofactors and vitamins [Bilirubin(E,ZorZ,E),X-11593--O-methylascorbate], 1Carbohydrate(1,6-anhydroglucose). We also identified several metabolic pathways that involved in the pathogenesis of OSA. CONCLUSION: MR (mendelian randomization) approach was performed to identify 6 protective factors and 12 risk factors for OSA in the present study. 3-Dehydrocarnitine was the most significant risk factors for OSA. Our study also confirmed several significant metabolic pathways that were involved in the pathogenesis of OSA. Valine, leucine and isoleucine biosynthesis metabolic pathways were the most significant metabolic pathways that were involved in the pathogenesis of OSA.

The causal relationship between genetically determined telomere length and meningiomas risk.

Background: Studies have shown that longer leukocyte telomere length (LTL) is significantly associated with increased risk of meningioma. However, there is limited evidence concerning the causal association of LTL with benign and malignant meningiomas or with the location of benign tumors. Methods: We used three LTL datasets from different sources, designated by name and sample size as LTL-78592, LTL-9190, and LTL-472174. The linkage disequilibrium score (LDSC) was used to explore the association between LTL and meningioma. We utilized two-sample bidirectional Mendelian randomization (TSMR) to evaluate whether LTL is causally related to meningioma risk. We adjusted for confounders by conducting multivariable Mendelian randomization (MVMR). Results: In the LTL-78592, longer LTL was significantly associated with increased risk of malignant [odds ratio (OR) = 5.14, p = 1.04 × 10-5], benign (OR = 4.81, p < 0.05), benign cerebral (OR = 5.36, p < 0.05), and benign unspecified meningioma (OR = 8.26, p < 0.05). The same results were obtained for the LTL-9190. In the LTL-472174, longer LTL was significantly associated with increased risk of malignant (OR = 4.94, p < 0.05), benign (OR = 3.14, p < 0.05), and benign cerebral meningioma (OR = 3.59, p < 0.05). Similar results were obtained in the MVMR. In contrast, only benign cerebral meningioma displayed a possible association with longer LTL (OR = 1.01, p < 0.05). No heterogeneity or horizontal pleiotropy was detected. Conclusion: In brief, genetically predicted longer LTL may increase the risk of benign, malignant, and benign cerebral meningiomas, regardless of the LTL measure, in European populations.