Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Song, Jaeseung; Kim, Daeun; Lee, Sora; Jung, Junghyun; Joo, Jong Wha J.; Jang, Wonhee

doi:10.1038/s42003-022-03564-w

Cited by 16 publications

(12 citation statements)

References 103 publications

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“…Although previous studies have attempted to identify the MPB-associated genetic variants, the associations between genetically regulated gene expression and MPB have not been studied yet. To investigate gene expression changes influenced by genetics in MPB, we utilized TWAS, a method that calculates the expected changes in gene expression modulated by genetic variants and identifies associations between genes and the trait [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…The posterior probabilities (PP) for hypotheses 0–4 were as follows: H 0 (no causal variant), H 1 (only causal variants between genotype and phenotype), H 2 (only causal variants for eQTL), H 3 (phenotype and gene expressions driven by two independent causal variants), and H 4 (phenotype and gene expressions share one causal variant). Following previous studies, we set the significance threshold as PP3 + PP4 > 0.8 and PP4 / PP3 > 2 [ 16 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

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Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

Choi,

Song,

Lee

et al. 2024

Hum Genomics

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Background Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. Results In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. Conclusions Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

Choi,

Song,

Lee

et al. 2024

Hum Genomics

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“…Six tissue panels from Genotype-Tissue Expression version 7 (GTEx v7) including three female reproductive organs (ovary, uterus, and vagina), two tissue panels related to the regulation of ovarian hormones (hypothalamus and pituitary), and whole blood panel were selected as reference panels [ 23 ]. To uncover as many associations as possible, two different blood panels from individual studies representing whole blood (Young Finns Study, YFS) and peripheral blood (Netherlands Twin Register, NTR) were also included following previous studies [ 24 , 25 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…H0: no association; H1: functional association but no GWAS association; H2: GWAS association but no functional association; H3: association with gene expression and GWAS signals but each is independent; H4: gene expression and GWAS association are colocalized. Genes satisfying the threshold of PP3+PP4 > 0.8 and PP4/PP3 > 2 were prioritized in the colocalization tests, following previous studies [ 28 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

et al. 2022

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Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical–gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid.

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“…Prognosis-related genes were used in 5 articles, employing the univariate Cox proportional hazards regression model [52][53][54] . Three articles combined DEG with other methods to build signatures, such as selecting genes satisfying both DEG and overall survival prognosis criteria [55,56] . Additionally, four speci c approaches were identi ed, including linear discriminant analysis, network hub genes, time-course trend genes, and transcriptome correlations.…”

Section: Review Of Current Signature-based Drug Repurposing Approache...mentioning

confidence: 99%

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

Hao,

Li,

Qiao

et al. 2024

Preprint

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Traditional drug discovery encounters challenges, including high costs, time-intensive processes, and inherent risks. Drug repurposing emerges as a compelling alternative strategy to identify new indications for investigational or approved drugs, circumventing these obstacles. Among the various drug repurposing methods, the Disease-specific Signature-based Connectivity Map (Cmap) approach is widely utilized. However, the commonly employed method for constructing disease-specific signatures, known as Differentially Expressed Genes (DEG), faces issues related to inconsistencies between dysregulated genes and the prognosis of genes in tumor tissue, as well as discrepancies in prognosis genes between tumor and normal tissues. In this study, we propose a novel approach, Prognosis Consistency Scoring (PCS), aimed at addressing these inconsistencies. PCS measures the consistency of gene prognosis between tumor and normal tissues by combining the Recurrence-Free Survival (RFS) prognosis power of genes in both contexts. Disease-specific signatures are then constructed based on PCS, and drug repurposing is performed using the Cmap and Lincs Unified Environment (CLUE). Validation of predicted drugs is conducted using data from DrugBank and ClinicalTrials databases. Our findings reveal that the aforementioned inconsistencies are pervasive. Compared to signatures based on DEGs, PCS-based signatures exhibit superior performance, identifying more drugs with higher prediction accuracy, as confirmed by DrugBank annotations. Notably, a significant proportion of predicted drugs without corresponding indications were subsequently validated in the ClinicalTrials database. Additionally, PCS-based signatures demonstrate elevated disease specificity and association with Drug Related Gene (DRG).

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Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Cited by 16 publications

References 103 publications

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

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