Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis.
Journal
Molecular medicine reports
ISSN: 1791-3004
Titre abrégé: Mol Med Rep
Pays: Greece
ID NLM: 101475259
Informations de publication
Date de publication:
Mar 2019
Mar 2019
Historique:
received:
27
12
2017
accepted:
16
10
2018
pubmed:
22
1
2019
medline:
28
5
2019
entrez:
22
1
2019
Statut:
ppublish
Résumé
Lung cancer is the leading cause of cancer‑associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll‑like receptor pathway was observed to be important. Protein‑protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein‑protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end‑product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.
Identifiants
pubmed: 30664219
doi: 10.3892/mmr.2019.9878
pmc: PMC6390056
doi:
Substances chimiques
Biomarkers, Tumor
0
Neoplasm Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2029-2040Références
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