Dataset Information

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis.

ABSTRACT: 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.

SUBMITTER: Ni M

PROVIDER: S-EPMC6194157 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis.

Ni Mengwei M Liu Xinkui X Wu Jiarui J Zhang Dan D Tian Jinhui J Wang Ting T Liu Shuyu S Meng Ziqi Z Wang Kaihuan K Duan Xiaojiao X Zhou Wei W Zhang Xiaomeng X

Frontiers in genetics 20181012

<b>Background and Objective:</b> 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. <b>Methods:</b> Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expre ...[more]

PMID: 30369945

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Project description:BackgroundLung Squamous Cell Carcinoma (LUSC) is a major subtype of lung malignancies and is associated with the cause of cancer-mediated mortality worldwide. However, identification of transcriptomic signatures associated with survival-prognosis and immunity of tumor remains lacking.MethodThe GSE2088, GSE6044, GSE19188, GSE21933, GSE33479, GSE33532, and GSE74706 were integrated for identifying differentially expressed genes (DEGs) with combined effect sizes. Also, the TCGA LUSC cohort was used for further analysis. A series of bioinformatics methods were utilized for conducting the whole study.ResultsThe 831 genes (such as DSG3, PKP1, DSC3, TPX2, and UBE2C) were found upregulated and the 731 genes (such as ABCA8, SELENBP1, FAM107A, and CACNA2D2) were downregulated in the LUSC. The functional enrichment analysis identifies the upregulated KEGG pathways, including cell cycle, DNA replication, base excision repair, proteasome, mismatch repair, and cellular senescence. Also, the key hub genes (such as EGFR, HRAS, JUN, CDH1, BRCA1, CASP3, RHOA, HDAC1, HIF1A, and CCNA2) were identified along with the eight gene modules that were significantly related to the protein-protein interaction (PPI). The clinical analyses identified that the overexpression group of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, LOX, and ITPA are substantially associated with a poor survival prognosis and the downregulated group of IL18R1 showed a similar trend. Moreover, our investigation demonstrated that the survival-associated genes were correlated with the stromal and immune scores in LUSC, indicating that the survival-associated genes regulate tumor immunity. The survival-associated genes were genetically altered in 27% of LUSC patients and showed excellent diagnostic efficiency. Finally, the consistent expression level of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, and ITPA were found in the TCGA LUSC cohort.ConclusionsThe identification of key transcriptomic signatures can be elucidated by the crucial mechanism of LUSC carcinogenesis.

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Dataset Information

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis.

Publications

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis.

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