Dataset Information

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis.

ABSTRACT: The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.

SUBMITTER: Liao Y

PROVIDER: S-EPMC6732946 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis.

Liao Yi Y Yin Guofang G Wang Xue X Zhong Ping P Fan Xianming X Huang Chengliang C

Oncology letters 20190729 4

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every da ...[more]

PMID: 31516585

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Project description:Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80-85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein-protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

Dataset Information

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis.

Publications

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis.

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