Dataset Information

Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis.

ABSTRACT: Background:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease, characterized by a decline in lung function. To date, the pathophysiologic mechanisms associated with lung dysfunction remain unclear, and no effective therapy has been identified to improve lung function. Methods:In the present study, we used weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with lung function in IPF. Three datasets, containing clinical information, were downloaded from Gene Expression Omnibus. WGCNA was performed on the GSE32537 dataset. Differentially expressed gene s (DEGs) between IPF patients and healthy controls were also identified to filter hub genes. The relationship between hub genes and lung function was then validated using the GSE47460 and GSE24206 datasets. Results:The red module, containing 267 genes, was positively correlated with the St. George's Respiratory Questionnaire score (r = 0.37, p < 0.001) and negatively correlated with the percent predicted forced vital capacity (FVC% predicted) (r = - 0.46, p < 0.001) and the percent predicted diffusion capacity of the lung for carbon monoxide (Dlco% predicted) (r = - 0.42, p < 0.001). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that the genes in the red module were primarily involved in inflammation and immune pathways. Based on Module Membership and Gene Significance, 32 candidate hub genes were selected in the red module to construct a protein-protein interaction network . Based on the identified DEGs and the degree of connectivity in the network, we identified three hub genes, including interleukin 6 (IL6), suppressor of cytokine signaling-3 (SOCS3), and serpin family E member 1 (SERPINE1). In the GSE47460 dataset, Spearman correlation coefficients between Dlco% predicted and expression levels of IL6, SERPINE1, SOCS3 were -0.32, -0.41, and -0.46, respectively. Spearman correlation coefficients between FVC% predicted and expression levels of IL6, SERPINE1, SOCS3 were -0.29, -0.33, and -0.27, respectively. In the GSE24206 dataset, all three hub genes were upregulated in patients with advanced IPF. Conclusion:We identified three hub genes that negatively correlated with the lung function of IPF patients. Our results provide insights into the pathogenesis underlying the progressive disruption of lung function, and the identified hub genes may serve as biomarkers and potential therapeutictargets for the treatment of IPF patients.

SUBMITTER: Xia Y

PROVIDER: S-EPMC7485506 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis.

Xia Yuechong Y Lei Cheng C Yang Danhui D Luo Hong H

PeerJ 20200908

<h4>Background</h4>Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease, characterized by a decline in lung function. To date, the pathophysiologic mechanisms associated with lung dysfunction remain unclear, and no effective therapy has been identified to improve lung function.<h4>Methods</h4>In the present study, we used weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with lung function in IPF. Three dat ...[more]

PMID: 33194355

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Project description:Background: Lung cancer is the most common comorbidity of idiopathic pulmonary fibrosis. Thus there is an urgent need for the research of IPF and carcinogenesis Objective: The objective of this study was to explore hub genes which are common in pulmonary fibrosis and lung cancer progression through bioinformatic analysis. Methods: All the analysis was performed in R software. Differentially expressed genes (DEGs) were explored by comparing gene expression profiles between IPF tissues and healthy lung tissues from GSE24206, GSE53845, GSE101286 and GSE110147 datasets. Venn Diagram analysis was used to identify the overlapping genes, while GO and KEGG pathway enrichment analysis were used to explore the biological functions of the DEGs using clusterprofiler package. Hub genes were identified by analyzing protein-protein interaction networks using Cytoscape software. Nomogram was constructed using the rms package. Tumor immune dysfunction and exclusion (TIDE) and Genomics of Drug Sensitivity in Cancer (GDSC) analysis was used to quantify the immunotherapy and chemotherapy sensitivity of non-small cell lung cancer (NSCLC) patients. Results: COL1A1, COL3A1, MMP1, POSTN1 and TIMP3 were identified as the top five hub genes. The five hub genes were used to construct a diagnostic nomogram that was validated in another IPF dataset. Since the hub genes were also associated with lung cancer progression, we found that the nomogram also had diagnostic value in NSCLC patients. These five genes achieved a statistically difference of overall survival in NSCLC patients (p < 0.05). The expression of the five hub genes was mostly enriched in fibroblasts. Fibroblasts and the hub genes also showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy. Conclusion: We identified five hub genes as potential biomarkers of IPF and NSCLC progression. This finding may give insight into the underlying molecular mechanisms of IPF and lung cancer progression and provides potential targets for developing new therapeutic agents for IPF patients.

Project description:BackgroundLaryngeal squamous cell carcinoma (LSCC) is the prominent cancer in head and neck, which greatly affects life quality of patients. The pathogenesis of LSCC is not clear. Presently, the LSCC treatments include chemotherapy, surgery and radiotherapy; however, these methods have poor efficacy in patients with recurrent and persistent cancer. Therefore, the study identified the hub genes accompanied with LSCC, which may be a potential therapeutic target in the future.MethodsWe extracted whole transcriptome high-throughput sequencing (HTS) LSCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and calculate differentially expressed genes (DEGs) between LSCC and normal samples using statistical software RStudio. Through weighted gene co-expression network analysis (WGCNA), enrichment examination of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) functions, and examination of protein-protein interaction (PPI) network, we obtained network hub genes and validated the hub genes prognostic value and expression levels of protein.ResultsThrough analysis of differential gene expression, from the GEO and TCGA databases 2,139 and 2,774 DEGs were obtained, respectively, 13 and 15 modules were screened from TCGA-LSCC and GSE127165 datasets by WGCNA, respectively. The most significant positive and negative correlation modules in the WGCNA and DEG lists were overlapped, and overall 36 co-expressed overlapping genes were retrieved. Through enrichment analysis of GO and KEGG, it was found that the gene functions were highly concentrated in cell junction assembly, basement membrane, extracellular matrix (ECM) structural constituent etc., and the pathways were mainly concentrated in ECM receptor interaction, focal adhesion, small cell lung cancer, and toxoplasmosis. Through analysis of PPI network analysis, 10 network hub genes (SNAI2, ITGA6, LAMB3, LAMC2, CAV1, COL7A1, GJA1, EHF, OAT, and GPT) were obtained. Finally, survival analysis and protein expression validation of these genes confirmed that low OAT expression and high CAV1 expression remarkably influenced the survival of patient's prognosis with LSCC.ConclusionsWe recognized the hub genes and key modules nearly associated to LSCC and these genes were validated by survival analysis and the database of Human Protein Atlas (HPA), which is of high importance for unveiling the pathogenesis of LSCC and probing for new precise biological marker and potential therapeutic targets.

Project description:ObjectivesThe study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses.MethodsThe GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein-protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets.ResultsFour common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses.ConclusionThough IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Project description:Background: Intervertebral disc degeneration impairs the quality of patients lives. Even though there has been development of many therapeutic strategies, most of them remain unsatisfactory due to the limited understanding of the mechanisms that underlie the intervertebral disc degeneration. Questions/purposes: This study is meant to identify the key modules and hub genes related to the annulus fibrosus in intervertebral disc degeneration (IDD) through: (1) constructing a weighted gene co-expression network; (2) identifying key modules and hub genes; (3) verifying the relationships of key modules and hub genes with IDD; and (4) confirming the expression pattern of hub genes in clinical samples. Methods: The Gene Expression Omnibus provided 24 sets of annulus fibrosus microarray data. Differentially expressed genes between the annulus fibrosus of degenerative and non-degenerative intervertebral disc samples have gone through the Gene Ontology (GO) and pathway analysis. The construction of a gene network and classification of genes into different modules were conducted through performing Weighted Gene Co-expression Network Analysis. The identification of modules and hub genes that were most related to intervertebral disc degeneration was proceeded. In order to verify the relationships of the module and hub genes with intervertebral disc degeneration, Ingenuity Pathway Analysis was operated. Clinical samples were adopted to help verify the hub gene expression profile. Results: One thousand one hundred ninety differentially expressed genes were identified. Terms and pathways associated with intervertebral disc degeneration were presented by GO and pathway analysis. The construction of a Weighted Gene Coexpression Network was completed and clustering differentially expressed genes into four modules was also achieved. The module with the lowest P-value and the highest absolute correlation coefficient was selected and its relationship with intervertebral disc degeneration was confirmed by Ingenuity Pathway Analysis. The identification of hub genes and the confirmation of their expression profile were also realized. Conclusions: This study generated a comprehensive overview of the gene networks underlying annulus fibrosus in intervertebral disc degeneration. Clinical Relevance: Modules and hub genes identified in this study are highly associated with intervertebral disc degeneration, and may serve as potential therapeutic targets for intervertebral disc degeneration.

Dataset Information

Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis.

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Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis.

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