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Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis
Small cell lung cancer (SCLC) is one of the highly malignant tumors and a serious threat to human health. The aim of the present study was to explore the underlying molecular mechanisms of SCLC. mRNA microarray datasets GSE6044 and GSE11969 were downloaded from Gene Expression Omnibus database, and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072191/ https://www.ncbi.nlm.nih.gov/pubmed/29845250 http://dx.doi.org/10.3892/mmr.2018.9095 |
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author | Wen, Pushuai Chidanguro, Tungamirai Shi, Zhuo Gu, Huanyu Wang, Nan Wang, Tongmei Li, Yuhong Gao, Jing |
author_facet | Wen, Pushuai Chidanguro, Tungamirai Shi, Zhuo Gu, Huanyu Wang, Nan Wang, Tongmei Li, Yuhong Gao, Jing |
author_sort | Wen, Pushuai |
collection | PubMed |
description | Small cell lung cancer (SCLC) is one of the highly malignant tumors and a serious threat to human health. The aim of the present study was to explore the underlying molecular mechanisms of SCLC. mRNA microarray datasets GSE6044 and GSE11969 were downloaded from Gene Expression Omnibus database, and the differentially expressed genes (DEGs) between normal lung and SCLC samples were screened using GEO2R tool. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed by the STRING database and visualized with Cytoscape software. In addition, the hub genes in the network and module analysis of the PPI network were performed using CentiScaPe and plugin Molecular Complex Detection. Finally, the mRNA expression levels of hub genes were validated in the Oncomine database. A total of 150 common DEGs with absolute fold-change >0.5, including 66 significantly downregulated DEGs and 84 upregulated DEGs were obtained. The Gene Ontology term enrichment analysis suggested that common upregulated DEGs were primarily enriched in biological processes (BPs), including ‘cell cycle’, ‘cell cycle phase’, ‘M phase’, ‘cell cycle process’ and ‘DNA metabolic process’. The common downregulated genes were significantly enriched in BPs, including ‘response to wounding’, ‘positive regulation of immune system process’, ‘immune response’, ‘acute inflammatory response’ and ‘inflammatory response’. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified that the common downregulated DEGs were primarily enriched in the ‘complement and coagulation cascades’ signaling pathway; the common upregulated DEGs were mainly enriched in ‘cell cycle’, ‘DNA replication’, ‘oocyte meiosis’ and the ‘mismatch repair’ signaling pathways. From the PPI network, the top 10 hub genes in SCLC were selected, including topoisomerase IIα, proliferating cell nuclear antigen, replication factor C subunit 4, checkpoint kinase 1, thymidylate synthase, minichromosome maintenance protein (MCM) 2, cell division cycle (CDC) 20, cyclin dependent kinase inhibitor 3, MCM3 and CDC6, the mRNA levels of which are upregulated in Oncomine SCLC datasets with the exception of MCM2. Furthermore, the genes in the significant module were enriched in ‘cell cycle’, ‘DNA replication’ and ‘oocyte meiosis’ signaling pathways. Therefore, the present study can shed new light on the understanding of molecular mechanisms of SCLC and may provide molecular targets and diagnostic biomarkers for the treatment and early diagnosis of SCLC. |
format | Online Article Text |
id | pubmed-6072191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-60721912018-08-06 Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis Wen, Pushuai Chidanguro, Tungamirai Shi, Zhuo Gu, Huanyu Wang, Nan Wang, Tongmei Li, Yuhong Gao, Jing Mol Med Rep Articles Small cell lung cancer (SCLC) is one of the highly malignant tumors and a serious threat to human health. The aim of the present study was to explore the underlying molecular mechanisms of SCLC. mRNA microarray datasets GSE6044 and GSE11969 were downloaded from Gene Expression Omnibus database, and the differentially expressed genes (DEGs) between normal lung and SCLC samples were screened using GEO2R tool. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed by the STRING database and visualized with Cytoscape software. In addition, the hub genes in the network and module analysis of the PPI network were performed using CentiScaPe and plugin Molecular Complex Detection. Finally, the mRNA expression levels of hub genes were validated in the Oncomine database. A total of 150 common DEGs with absolute fold-change >0.5, including 66 significantly downregulated DEGs and 84 upregulated DEGs were obtained. The Gene Ontology term enrichment analysis suggested that common upregulated DEGs were primarily enriched in biological processes (BPs), including ‘cell cycle’, ‘cell cycle phase’, ‘M phase’, ‘cell cycle process’ and ‘DNA metabolic process’. The common downregulated genes were significantly enriched in BPs, including ‘response to wounding’, ‘positive regulation of immune system process’, ‘immune response’, ‘acute inflammatory response’ and ‘inflammatory response’. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified that the common downregulated DEGs were primarily enriched in the ‘complement and coagulation cascades’ signaling pathway; the common upregulated DEGs were mainly enriched in ‘cell cycle’, ‘DNA replication’, ‘oocyte meiosis’ and the ‘mismatch repair’ signaling pathways. From the PPI network, the top 10 hub genes in SCLC were selected, including topoisomerase IIα, proliferating cell nuclear antigen, replication factor C subunit 4, checkpoint kinase 1, thymidylate synthase, minichromosome maintenance protein (MCM) 2, cell division cycle (CDC) 20, cyclin dependent kinase inhibitor 3, MCM3 and CDC6, the mRNA levels of which are upregulated in Oncomine SCLC datasets with the exception of MCM2. Furthermore, the genes in the significant module were enriched in ‘cell cycle’, ‘DNA replication’ and ‘oocyte meiosis’ signaling pathways. Therefore, the present study can shed new light on the understanding of molecular mechanisms of SCLC and may provide molecular targets and diagnostic biomarkers for the treatment and early diagnosis of SCLC. D.A. Spandidos 2018-08 2018-05-29 /pmc/articles/PMC6072191/ /pubmed/29845250 http://dx.doi.org/10.3892/mmr.2018.9095 Text en Copyright: © Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Wen, Pushuai Chidanguro, Tungamirai Shi, Zhuo Gu, Huanyu Wang, Nan Wang, Tongmei Li, Yuhong Gao, Jing Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title_full | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title_fullStr | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title_full_unstemmed | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title_short | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis |
title_sort | identification of candidate biomarkers and pathways associated with sclc by bioinformatics analysis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072191/ https://www.ncbi.nlm.nih.gov/pubmed/29845250 http://dx.doi.org/10.3892/mmr.2018.9095 |
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