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Using bioinformatics approach identifies key genes and pathways in idiopathic pulmonary fibrosis.

ABSTRACT: Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, eliminating batch difference. R software was used to process and to screen differentially expressed genes (DEGs) between the IPF and normal tissues. Then, functional enrichment analysis of these DEGs was carried out, and a protein-protein interaction network (PPI) was also constructed. A total of 276 DEGs (152 up and 134 down-regulated genes) were identified in the IPF lung samples. The PPI network was established with 227 nodes and 763 edges. The top 10 hub genes were CAM1, CDH1, CXCL12, JUN, CTGF, SERPINE1, CXCL1, EDN1, COL1A2, and SPARC. Analyzing the PPI network modules with close interaction, the 3 key modules in the whole PPI network were screened out. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the module containing DEGs contained the viral protein interaction with cytokine and the cytokine receptor, the TNF signaling pathway, and the chemokine signaling pathway. The identified key genes and pathways may play an important role in the occurrence and development of IPF, and may be expected to be biomarkers or therapeutic targets for the diagnosis of IPF.

SUBMITTER: Xu Z

PROVIDER: S-EPMC7478566 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Using bioinformatics approach identifies key genes and pathways in idiopathic pulmonary fibrosis.

Xu Zhongbo Z Mo Lisha L Feng Xin X Huang Mingru M Li Lin L

Medicine 20200901 36

Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, elimi ...[more]

PMID: 32899090

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Project description:Objective: Ferroptosis has an important role in developing pulmonary fibrosis. The present project aimed to identify and validate the potential ferroptosis-related genes in pulmonary fibrosis by bioinformatics analyses and experiments. Methods: First, the pulmonary fibrosis tissue sequencing data were obtained from Gene Expression Omnibus (GEO) and FerrDb databases. Bioinformatics methods were used to analyze the differentially expressed genes (DEGs) between the normal control group and the pulmonary fibrosis group and extract ferroptosis-related DEGs. Hub genes were screened by enrichment analysis, protein-protein interaction (PPI) analysis, and random forest algorithm. Finally, mouse pulmonary fibrosis model was made for performing an exercise intervention and the hub genes' expression was verified through qRT-PCR. Results: 13 up-regulated genes and 7 down-regulated genes were identified as ferroptosis-related DEGs by comparing 103 lung tissues with idiopathic pulmonary fibrosis (IPF) and 103 normal lung tissues. PPI results indicated the interactions among these ferroptosis-related genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment and Genome-Ontology (GO) enrichment analyses showed that these ferroptosis-related genes involved in the organic anion transport, response to hypoxia, response to decrease oxygen level, HIF-1 signaling pathway, renal cell carcinoma, and arachidonic acid metabolism signaling pathway. The confirmed genes using PPI analysis and random forest algorithm included CAV1, NOS2, GDF15, HNF4A, and CDKN2A. qRT-PCR of the fibrotic lung tissues from the mouse model showed that the mRNA levels of NOS2 and GDF15 were up-regulated, while CAV1 and CDKN2A were down-regulated. Also, treadmill training led to an increased expression of CAV1 and CDKN2A and a decrease in the expression of NOS2 and GDF15. Conclusion: Using bioinformatics analysis, 20 potential genes were identified to be associated with ferroptosis in pulmonary fibrosis. CAV1, NOS2, GDF15, and CDKN2A were demonstrated to be influencing the development of pulmonary fibrosis by regulating ferroptosis. These findings suggested that, as an aerobic exercise treatment, treadmill training reduced ferroptosis in the pulmonary fibrosis tissues, and thus, reduces inflammation in the lungs. Aerobic exercise training initiate concomitantly with induction of pulmonary fibrosis reduces ferroptosis in lung. These results may develop our knowledge about pulmonary fibrosis and may contribute to its treatment.

Project description:Background Using bioinformatic methods to explore the differentially expressed genes (DEGs) of human idiopathic pulmonary fibrosis (IPF) and to elucidate the pathogenesis of IPF from the genetic level. Methods The GSE110147 gene expression profile was downloaded from the GEO database. The data of lung adenocarcinoma (LUAD) samples, lung squamous cell carcinoma (LUSC) samples and normal samples were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. DEGs between IPF patients and healthy donors were analyzed using the GEO2R tool. Use the “clusterprofiler” package in R software to perform gene ontology (GO) and KEGG pathway enrichment analysis, and then perform function annotation and protein-protein interaction (PPI) network construction in the STRING online tool. The Genome Browser tool of the university of california santa cruz (UCSC) online website was used to predict transcription factors (TFs) of genes. In the final, the results were analyzed synthetically. Results A total of 9,183 DEGs were identified, of which 4,545 genes were down-regulated, and 4638 were up-regulated. MMP1, SPP1, and BPIFB1 were the top three DEGs with the highest significant up-regulation. These DEGs played an important role in the occurrence of IPF through the MAPK (mitogen-activated protein kinase) signaling pathway. Furthermore, 50 DEGs were enriched in the expression of PD-L1 and the PD-1 checkpoint pathway in cancer, of which 11 genes were re-enriched in the pathway of non-small cell lung cancer. The expression of the 11 genes were extensively regulated by CTCFL, SP2 and ZNF341. Most of them were differentially expressed between lung cancers and normal lung tissues. The overall survival (OS) curve of LUAD were significantly stratified by AKT2, KRAS, PIK3R1, meanwhile the OS curve of LUAC was significantly stratified by MAPK3. Conclusions Bioinformatics analysis revealed that DEGs including MPP1 might be potential targets and biomarkers of IPF, and the MAPK signaling pathway is related to the occurrence and development of IPF. The development of IPF lung cancer complications may be related to the activation of genes enriched in PD-L1 expression and PD-1 checkpoint pathway, which provides clues to the pathogenesis of IPF combined with lung cancer.

Project description:BackgroundLiver fibrosis is often a consequence of chronic liver injury, and has the potential to progress to cirrhosis and liver cancer. Despite being an important human disease, there are currently no approved anti-fibrotic drugs. In this study, we aim to identify the key genes and pathways governing the pathophysiological processes of liver fibrosis, and to screen therapeutic anti-fibrotic agents.MethodsExpression profiles were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by R packages (Affy and limma). Gene functional enrichments of each dataset were performed on the DAVID database. Protein-protein interaction (PPI) network was constructed by STRING database and visualized in Cytoscape software. The hub genes were explored by the CytoHubba plugin app and validated in another GEO dataset and in a liver fibrosis cell model by quantitative real-time PCR assay. The Connectivity Map L1000 platform was used to identify potential anti-fibrotic agents.ResultsWe integrated three fibrosis datasets of different disease etiologies, incorporating a total of 70 severe (F3-F4) and 116 mild (F0-F1) fibrotic tissue samples. Gene functional enrichment analyses revealed that cell cycle was a pathway uniquely enriched in a dataset from those patients infected by hepatitis B virus (HBV), while the immune-inflammatory response was enriched in both the HBV and hepatitis C virus (HCV) datasets, but not in the nonalcoholic fatty liver disease (NAFLD) dataset. There was overlap between these three datasets; 185 total shared DEGs that were enriched for pathways associated with extracellular matrix constitution, platelet-derived growth-factor binding, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling. In the PPI network, 25 hub genes were extracted and deemed to be essential genes for fibrogenesis, and the expression trends were consistent with GSE14323 (an additional dataset) and liver fibrosis cell model, confirming the relevance of our findings. Among the 10 best matching anti-fibrotic agents, Zosuquidar and its corresponding gene target ABCB1 might be a novel anti-fibrotic agent or therapeutic target, but further work will be needed to verify its utility.ConclusionsThrough this bioinformatics analysis, we identified that cell cycle is a pathway uniquely enriched in HBV related dataset and immune-inflammatory response is clearly enriched in the virus-related datasets. Zosuquidar and ABCB1 might be a novel anti-fibrotic agent or target.

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.

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Using bioinformatics approach identifies key genes and pathways in idiopathic pulmonary fibrosis.

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Using bioinformatics approach identifies key genes and pathways in idiopathic pulmonary fibrosis.

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