Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy.
Bioinformatics
Cell cycle
Differentially expressed genes
Drug
Esophageal squamous cell carcinoma
Hub genes
Journal
Cancer cell international
ISSN: 1475-2867
Titre abrégé: Cancer Cell Int
Pays: England
ID NLM: 101139795
Informations de publication
Date de publication:
2019
2019
Historique:
received:
18
03
2019
accepted:
10
05
2019
entrez:
30
5
2019
pubmed:
30
5
2019
medline:
30
5
2019
Statut:
epublish
Résumé
Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC. mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC. Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC. In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.
Sections du résumé
BACKGROUND
BACKGROUND
Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC.
METHODS
METHODS
mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC.
RESULTS
RESULTS
Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC.
CONCLUSIONS
CONCLUSIONS
In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.
Identifiants
pubmed: 31139019
doi: 10.1186/s12935-019-0854-6
pii: 854
pmc: PMC6530124
doi:
Types de publication
Journal Article
Langues
eng
Pagination
142Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
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