Identifying Potential Drug Targets for the Treatment of Ulcerative Colitis Using Mendelian Randomization Combined with Co-localization Analysis DOI Creative Commons
Tianyu Zhang

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 28, 2024

Abstract Purpose To identify potential therapeutic targets for ulcerative colitis by integrating Mendelian randomization (MR) and Bayesian colocalization analysis to pinpoint gene expression quantitative trait loci (eQTLs) associated with risk. Methods Leveraging peripheral blood eQTL data from the eQTLGen Consortium genome-wide association study (GWAS) summary statistics, we performed MR eQTLs significantly risk in discovery replication datasets. The identified were then subjected evaluate whether same single nucleotide polymorphisms (SNPs) influence both disease Finally, Drug Gene Interaction database (DGIdb) was queried known drugs targeting genes. Results 15 potentially positive eQTLs, of which 7 (CD300C, GPX1, LAMC3, RORC, SIGLEC6, SLC22A5, WFIKKN1) replicated be (Correction P -value < 0.005). Colocalization provided strong evidence that SNPs driving these also impact susceptibility. While LAMC3 have approved other indications, CD300C, WFIKKN1 represent novel drug targets. Conclusions By colocalization, this pinpointed colitis-associated genes genome, including 3 existing 4 new WFIKKN1), providing important leads development colitis.

Language: Английский

CXCR1 and CXCR2 are potential neutrophil extracellular trap-related treatment targets in ulcerative colitis: insights from Mendelian randomization, colocalization and transcriptomic analysis DOI Creative Commons

Yichuan Xv,

Yiyi Feng,

Lin Jiang

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 12, 2024

There is already substantial evidence indicating that neutrophil extracellular trap (NET) formation contributes to the inflammatory cascade in ulcerative colitis (UC). However, precise regulatory mechanisms governing this process remain elusive. This study aimed determine role of NET-related genes UC and reveal possible mechanisms.

Language: Английский

Citations

6
Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses DOI Creative Commons
Bohong Chen, Lihui Wang, Shengyu Pu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 18, 2024

Abstract Hyperparathyroidism (HPT) manifests as a complex condition with substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying progression remain lacking. Identifying putative genetic drivers exploring novel drug targets that can impede HPT critical unmet needs. A Mendelian randomization (MR) analysis was performed uncover therapeutic implicated hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving instrumental variables were obtained from eQTLGen Consortium Genotype-Tissue Expression (GTEx) portal. summary statistics single nucleotide polymorphism (SNP) associations sourced FinnGen study (5590 cases; 361,988 controls). Colocalization determine probability shared causal variants SNP-hyperparathyroidism SNP-eQTL links. Five (CMKLR1, FSTL1, IGSF11, PIK3C3 SLC40A1) showed significant causation both GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) solute carrier family 40 member 1 (SLC40A1) strong evidence colocalization HPT. Multivariable Phenome-Wide Association Study analyses indicated these two not associated other traits. Additionally, prediction implies potential future clinical applications. This identifies SLC40A1 genetically proxied druggable genes promising hyperparathyroidism. Targeting may offer effective pharmacotherapies impeding reducing risk. These findings provide preliminary insight into amenable manipulation, though further investigation is imperative validate translational preclinical models through

Language: Английский

Citations

5
Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction DOI Creative Commons
Yihao Zhu, Yuxi Chen,

Jiajin Xu

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(7), P. 1626 - 1626

Published: July 22, 2024

Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance biological mechanism acute myocardial infarction (AMI) yet to be fully explored.

Language: Английский

Citations

4
Causal Inference and Annotation of Phosphoproteomics Data in Multi-omics Cancer Studies DOI Creative Commons

Qun Dong,

Minjia Tan, Yingchun Zhou

et al.

Molecular & Cellular Proteomics, Journal Year: 2025, Volume and Issue: unknown, P. 100905 - 100905

Published: Jan. 1, 2025

Highlights•Phoslink is a computational approach designed for causal inference in cancer proteomics and phosphoproteomics.•Phoslink demonstrates superior performance compared with canonical Mendelian randomization correlation-based methods.•Phoslink uncovers novel regulatory phosphosites their network.•Phoslink available as an R package.AbstractProtein phosphorylation plays crucial role regulating diverse biological processes. Perturbations protein are closely associated downstream pathway dysfunctions, while alterations expression could serve sensitive indicators of pathological status. However, there currently few methods that can accurately identify the links between expression, given issues like reverse causation confounders. Here, we present Phoslink, model to infer effects integrating prior evidence multi‐omics data. We demonstrated feasibility advantages our method under various simulation scenarios. Phoslink exhibited more robust estimates lower FDR than commonly used Pearson Spearman correlations, better IV selection randomization. Applying this approach, identified 345 involving 109 310 proteins 79 lung adenocarcinoma (LUAD) samples. Based on these links, constructed network 26 key regulators strongly LUAD. Notably, 16 were exclusively through phosphosite-protein relationships, highlighting significance inference. explored potentially druggable phosphoproteins provided critical clues drug repurposing also significant mediation LUAD expression. In summary, study introduces new phosphoproteomics studies. its utility potential target identification thereby accelerating clinical translation phosphoproteomic data.Graphical abstract

Language: Английский

Citations

0
Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibin subunit βb as a novel therapeutic target DOI Creative Commons

Qinqing Wu,

Ye Lu,

Yuwei Wu

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 9, 2025

Colon adenocarcinoma (COAD) is a malignancy with high mortality rate and complex biological characteristics heterogeneity, which poses challenges for clinical treatment. Anoikis type of programmed cell death that occurs when cells lose their attachment to the extracellular matrix (ECM), it plays crucial role in tumor metastasis. However, specific link between anoikis COAD, as well its mechanisms progression, remains unclear, making potential new direction therapeutic strategy research. We employed transcriptomic data information from The Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) pinpoint differentially expressed anoikis-related genes (ARGs) COAD. Using Cox proportional hazards models Lasso regression analysis, we developed prognostic signature derived these ARGs. also investigated roles interactions microenvironment by analyzing single-cell RNA sequencing data. Additionally, molecular docking techniques evaluate inhibin subunit beta B (INHBB) targets assess binding affinity candidate drugs. Finally, used gene knockout silence key INHBB explored functions vitro. In our study, expression differences ARGs, successfully classified patients Kaplan-Meier survival analysis demonstrated elevated risk scores experienced poorer prognosis, finding was confirmed both training validation cohorts. immune infiltration revealed notable increase presence within high-risk patients. Molecular identified drug candidates INHBB, including risperidone. Furthermore, vitro experiments showed downregulation COAD lines significantly reduced cellular viability migration capacity. summary, research, based on provides insights into precise classification, prognosis assessment, identification It validates progression establishing foundation future personalized treatment strategies.

Language: Английский

Citations

0
Integrative genetics and multiomics analysis reveal mechanisms and therapeutic targets in vitiligo highlighting JAK STAT pathway regulation of CTSS DOI Creative Commons

Zi-yue Dong,

Mingjie He,

Yongkai Yu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 17, 2025

Vitiligo is a complex autoimmune disease characterized by the loss of melanocytes, leading to skin depigmentation. Despite advances in understanding its genetic and molecular basis, precise mechanisms driving vitiligo remain elusive. Integrating multiple layers omics data can provide comprehensive view pathogenesis identify potential therapeutic targets. The study aims delineate using an integrative multiomics strategy. We focus on exploring regulatory influence JAK/STAT pathway Cathepsin S, target vitiligo. Our GWAS-meta analysis pinpointed five druggable genes: ERBB3, RHOH, CDK10, MC1R, NDUFAF3, underwent drug exploration docking. SMR linked CTSS, CTSH, STX8, KIR2DL3, GRHPR through pQTL eQTL associations. Microarray single-cell RNA-seq showed differential expression CTSS STAT1/3 patients' blood lesions. offers novel perspectives vitiligo's highlighting pathway's role regulating for antigen processing melanocytes. Further research needed confirm these results assess related genes.

Language: Английский

Citations

0
Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma DOI Creative Commons
Yi Li, Rui Zeng, Yuhua Huang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: March 3, 2025

Purpose Type 1 diabetes mellitus (T1DM), as an autoimmune disease, can increase susceptibility to clear cell renal carcinoma (ccRCC) due its proinflammatory effects. ccRCC is characterized by subtle onset and unfavorable prognosis. Thus, the aim of this study was highlight prevention early detection opportunities in high-risk populations identifying common biomarkers for T1DM ccRCC. Methods Based on multiple publicly available datasets, WGCNA applied identify gene modules closely associated with T1DM, which were then integrated prognostic DEGs Subsequently, LASSO SVM algorithms employed shared hub genes between two diseases. Additionally, clinical samples used validate expression patterns these genes, scRNA-seq data utilized analyze types expressing explore potential mechanisms communication. Results Overall, three (KIF21A, PIGH, RPS6KA2) identified TIDM Analysis datasets revealed that KIF21A PIGH significantly downregulated PIG upregulated disease group. are mainly expressed NK T cells, PRS6KA2 endothelial epithelial MIF signaling pathway may be related genes. Conclusion Our results demonstrated pivotal roles These hold promise novel biomarkers, offering avenues preventive strategies development new precision treatment modalities.

Language: Английский

Citations

0
Identification and validation of transcriptome-wide association study-derived genes as potential druggable targets for osteoarthritis DOI Creative Commons
Xindie Zhou, Xinjian Ye, Jiapei Yao

et al.

Bone and Joint Research, Journal Year: 2025, Volume and Issue: 14(3), P. 224 - 235

Published: March 13, 2025

Aims Osteoarthritis (OA) is a widespread chronic degenerative joint disease with an increasing global impact. The pathogenesis of OA involves complex interactions between genetic and environmental factors. Despite this, the specific mechanisms underlying remain only partially understood, hindering development targeted therapeutic strategies. Methods A transcriptome-wide association study (TWAS) was conducted for site-specific phenotypes using functional summary-based imputation (FUSION). High-confidence candidate genes were identified through rigorous quality control measures, including joint/conditional analysis, permutation tests, best model evaluation, colocalization analysis. Co-expression network analysis performed to elucidate biology these genes. Druggable gene targets their structural models retrieved from DrugBank SWISS-MODEL databases. Finally, enrichment mitogen-activated protein kinase 3 ( MAPK3 ) SMAD3 in validated biochemically vitro vivo models, as well human histological sections. Results Utilizing FUSION algorithm, TWAS 794 OA. After control, 14 classified high-confidence genes, seven recognized potential drug GCAT, MAPK3, MST1R, PFKM, RAD9A, SMAD3, USAP8 . revealed strong biological Both experiments demonstrated high activity enriched expression two Conclusion present tissue-specific druggable OA, providing new insights into landscape processes involved Further studies are warranted confirm findings. Cite this article: Bone Joint Res 2025;14(3):224–235.

Language: Английский

Citations

0
Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning DOI Creative Commons
Junyang Ma,

Shufu Hou,

Xinxin Gu

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

Background Recent studies have suggested a potential association between gastric cancer (GC) and myocardial infarction (MI), with shared pathogenic factors. This study aimed to identify these common factors pharmacologic targets. Methods Data from the IEU Open GWAS project were used. Two-sample Mendelian randomization (MR) analysis was used explore causal link MI GC. Transcriptome identified differentially expressed genes, followed by enrichment analysis. Drug target MR eQTLs validated associations GC, Steiger direction test confirmed their direction. The random forest Lasso algorithms genes diagnostic value, leading nomogram construction. performance of model evaluated via ROC, calibration, decision curves. Correlations immune cell infiltration analyzed. Results linked increased GC risk ( OR =1.112, P =0.04). Seventy-four which are related mainly ubiquitin-dependent proteasome pathways, commonly Nine consistently associated eight had value. built on strong predictive AUC =0.950, validation set =0.957). Immune revealed significant correlations several cells, such as T macrophages, neutrophils, B dendritic cells. Conclusion is an developing both share constructed based 8 value good performance.

Language: Английский

Citations

0
Multi-omic studies on the pathogenesis of Sepsis DOI Creative Commons

Hongjie Tong,

Yuhang Zhao, Ying Cui

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 24, 2025

Language: Английский

Citations

0