Dataset Information

Identification of key genes in non?alcoholic fatty liver disease progression based on bioinformatics analysis.

ABSTRACT: Due to economic development and lifestyle changes, the incidence of non?alcoholic fatty liver disease (NAFLD) has gradually increased in recent years. However, the pathogenesis of NAFLD is not yet fully understood. To identify candidate genes that contribute to the development and progression of NAFLD, two microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and functional enrichment analyses were performed. A protein?protein interaction network was constructed and modules were extracted using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. The enriched functions and pathways of the DEGs included 'cellular macromolecule biosynthetic process', 'cellular response to chemical stimulus', 'extracellular matrix organization', 'metabolic pathways', 'insulin resistance' and 'forkhead box protein O1 signaling pathway'. The DEGs, including type?1 angiotensin II receptor, formin?binding protein 1?like, RNA?binding protein with serine?rich domain 1, Ras?related C3 botulinum toxin substrate 1 and polyubiquitin?C, were identified using multiple bioinformatics methods and validated in vitro with reverse transcription?quantitative polymerase chain reaction analysis. In conclusion, five hub genes were identified in the present study, and they may aid in understanding of the molecular mechanisms underlying the development and progression of NAFLD.

SUBMITTER: Li L

PROVIDER: S-EPMC5983972 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis.

Li Lin L Liu Huabao H Hu Xiaoyu X Huang Yi Y Wang Yanan Y He Yansha Y Lei Qingsong Q

Molecular medicine reports 20180405 6

Due to economic development and lifestyle changes, the incidence of non‑alcoholic fatty liver disease (NAFLD) has gradually increased in recent years. However, the pathogenesis of NAFLD is not yet fully understood. To identify candidate genes that contribute to the development and progression of NAFLD, two microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and functional enrichment analyses were performed. A pr ...[more]

PMID: 29620197

Similar Datasets

Project description:BackgroundNon-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Autophagy plays a vital role in NAFLD development and progression. We aimed to establish a novel autophagy-related gene (ARG) signature as a therapeutic target in NAFLD patients based on high-throughput sequencing data.MethodsARGs obtained from the HAMdb and high-sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed ARGs (DEARGs) between normal and NASH tissues. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore potential biological and pathological functions of DEARGs. The protein-protein interaction (PPI) network of the DEARGs was established through the STRING website, and visualized by Cytoscape. In addition, hub genes were validated by an independent dataset GSE89632. Finally, we performed Gene Set Variation Analysis (GSVA) pathway-related analysis to identify the pivotal signaling pathways and genes for the progression of non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH).ResultsA total of 76 DEARGs were identified in the GSE126848 dataset, of which 45 genes were upregulated and 31 genes were downregulated. GO analysis showed that the biological functions of DEARGs focused primarily on autophagy, cellular response to external stimulus, fibroblast proliferation, late endosome, and ubiquitin protein ligase binding. KEGG pathway analysis showed that these DEARGs were mainly involved in the apoptosis, PI3K-Akt signaling pathway, and estrogen signaling pathway. Among DEGs, 9 most closely related genes were identified from the PPI network. Furthermore, NOS3, IGF1, VAMP8, FOS, and HMOX1 were verified in the GSE89632 dataset. At last, the MAPK signal pathway was identified as important pathway, and JUN was identified as a key gene involved in the progression from NAFL to NASH.ConclusionThis study may provide credible molecular biomarkers in terms of screening and diagnosis for NAFLD. Meanwhile, it also serves as a basis for exploring the molecular mechanisms underlying the progression of NAFL to NASH.

Project description:Background: Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrinopathies disorder in women of reproductive age and non-alcoholic fatty liver (NAFLD) is one of the most common liver diseases worldwide. Previous research has indicated potential associations between PCOS and NAFLD, but the underlying pathophysiology is still not clear. The present study aims to identify the differentially expressed genes (DEGs) between PCOS and NAFLD through the bioinformatics method, and explore the associated molecular mechanisms. Methods: The microarray datasets GSE34526 and GSE63067 were downloaded from Gene Expression Omnibus (GEO) database and analyzed to obtain the DEGs between PCOS and NAFLD with the GEO2R online tool. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the DEGs were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, NetworkAnalyst was used to construct the network between the targeted microRNAs (miRNAs) and the hub genes. Results: A total of 52 genes were identified as DEGs in the above two datasets. GO and KEGG enrichment analysis indicated that DEGs are mostly enriched in immunity and inflammation related pathways. In addition, nine hub genes, including TREM1, S100A9, FPR1, NCF2, FCER1G, CCR1, S100A12, MMP9, and IL1RN were selected from the PPI network by using the cytoHubba and MCODE plug-in. Then, four miRNAs, including miR-20a-5p, miR-129-2-3p, miR-124-3p, and miR-101-3p, were predicted as possibly the key miRNAs through the miRNA-gene network construction. Conclusion: In summary, we firstly constructed a miRNA-gene regulatory network depicting interactions between the predicted miRNA and the hub genes in NAFLD and PCOS, which provides novel insights into the identification of potential biomarkers and valuable therapeutic leads for PCOS and NAFLD.

Project description:BackgroundThe morbidity and mortality rates of patients with non-alcoholic fatty liver disease (NAFLD) have been steadily increasing in recent years. Previous studies have confirmed the important role of ferroptosis in NAFLD development; however, the precise mechanism through which ferroptosis influences NAFLD occurrence remains unclear. The present study aimed to identify and validate ferroptosis-related genes involved in NAFLD pathogenesis and to investigate the underlying molecular mechanisms of NAFLD.MethodsWe downloaded microarray datasets GSE72756 and GSE24807 to identify differentially expressed genes (DEGs) between samples from healthy individuals and patients with NAFLD. From these DEGs, we extracted ferroptosis-related DEGs. GSE89632, another microarray dataset, was used to validate the expression of ferroptosis-related genes. A protein-protein interaction (PPI) network of ferroptosis-related genes was then constructed. The target genes were also subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Finally, competing endogenous RNA networks were constructed. We used the CIBERSORT package to evaluate the infiltration of immune cells infiltration in NAFLD.ResultsFive ferroptosis-related genes (SCP2, MUC1, DPP4, SLC1A4, and TF) were identified as promising diagnostic biomarkers for NAFLD. Enrichment analyses revealed that these genes are mainly involved in metabolic processes. NEAT1-miR-1224-5p-SCP2, NEAT1-miR-485-5p-MUC1, MALAT1-miR-485-5p-MUC1, and CNOT6-miR-145-5p-SLC1A4 are likely to be the potential RNA regulatory pathways that affect NAFLD development. Principal component analysis indicated significant differences in immune cell infiltration between the two groups.ConclusionsThis study identified five ferroptosis-related genes as potential biomarkers for diagnosing NAFLD. The correlations between the expression of ferroptosis-related genes and immune cell infiltration might shed light on the study of the molecular mechanism underlying NAFLD development.

Project description:BackgroundStudies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear.MethodsThe NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks.ResultsThere were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1.ConclusionThe study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Dataset Information

Identification of key genes in non?alcoholic fatty liver disease progression based on bioinformatics analysis.

Publications

Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets