Dataset Information

Screening Potential Drugs for the Development of NAFLD Based on Drug Perturbation Gene Set.

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) has become one of the problems affecting the health of the population worldwide. The progressive disease includes nonalcoholic steatohepatitis (NASH) and fibrosis, which with no approved therapy, system identification of effective drugs remains challenging. In this work, we applicated drug perturbation gene set enrichment analysis to screen drugs for the development of NAFLD. A total 15490 small-molecule compounds were analyzed in our study; based on the p value of enrichment score, 7 small-molecule compounds were found to have a potential role in NASH and fibrosis. After pathway analyses, we found indoximod had effects on nonalcoholic fatty liver disease through regulated TNFa, AP-1, AKT, PI3K, etc. Furthermore, we established the NAFLD cell model with LO2 cells induced using PA; ELISA showed that the levels of TG, ALT, and AST were significantly improved by indoximod. In summary, our study offers optimal therapeutic drugs, which may provide novel insight into the precise treatment of NAFLD and promote researches.

SUBMITTER: Gao Z

PROVIDER: S-EPMC9034918 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Screening Potential Drugs for the Development of NAFLD Based on Drug Perturbation Gene Set.

Gao Zhengzheng Z Dai Lina L Zhang Haifeng H

Computational and mathematical methods in medicine 20220416

Nonalcoholic fatty liver disease (NAFLD) has become one of the problems affecting the health of the population worldwide. The progressive disease includes nonalcoholic steatohepatitis (NASH) and fibrosis, which with no approved therapy, system identification of effective drugs remains challenging. In this work, we applicated drug perturbation gene set enrichment analysis to screen drugs for the development of NAFLD. A total 15490 small-molecule compounds were analyzed in our study; based on the ...[more]

PMID: 35469222

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Project description:ObjectivesGlioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.MethodsWe first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.ResultsWe identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.ConclusionsIn this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Project description:BackgroundRhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR.MethodsWe acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes.ResultsA total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways.ConclusionsNFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.

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Screening Potential Drugs for the Development of NAFLD Based on Drug Perturbation Gene Set.

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Screening Potential Drugs for the Development of NAFLD Based on Drug Perturbation Gene Set.

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