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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes were analyzed by integration with annotations from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by construction of a protein-protein interaction network and weighted gene coexpression analysis. We identified 139 differentially expressed genes associated with the progression of COPD, among which 14 Hub genes were identified and found to be enriched in certain categories, including immune and inflammatory response, response to lipopolysaccharide and receptor for advanced glycation end products binding; in addition, these Hub genes are involved in multiple signaling pathways, particularly hematopoietic cell lineage and cytokine-cytokine receptor interaction. The 14 Hub genes were positively or negatively associated with COPD by wgcna analysis. The genes CX3CR1, PTGS2, FPR1, FPR2, S100A12, EGR1, CD163, S100A8 and S100A9 were identified to mediate inflammation and injury of the lung, and play critical roles in the pathogenesis of COPD. These findings improve our understanding of the underlying molecular mechanisms of COPD.

SUBMITTER: Huang X

PROVIDER: S-EPMC6823288 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

Huang Xinwei X Li Yunwei Y Guo Xiaoran X Zhu Zongxin Z Kong Xiangyang X Yu Fubing F Wang Qiang Q

FEBS open bio 20190929 11

Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes ...[more]

PMID: 31419078

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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:Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

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Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Dataset Information

Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

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