Identification of genes and analysis of prognostic values in nonsmoking females with non-small cell lung carcinoma by bioinformatics analyses

Yang, Guangda; Chen, Qianya; Xiao, Jieming; Zhang, Ye; Wang, Zhichao; Lin, Xinhua

doi:10.2147/cmar.s174409

Cited by 25 publications

(22 citation statements)

References 48 publications

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“…An abnormal cell cycle may lead to the occurrence of a variety of different types of tumor, such as hepatocellular carcinoma (33,34), breast cancer (35), pancreatic cancer (36), ovarian cancer (37) and NSCLC in non-smoking females (38). Wang et al (39) used different microarray expression profiles in their study of primitive neuro-ectodermal tumors, and revealed the same four pathways that were identified in the present study, and several of the same genes in the modules that they identified as being important.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…Another of our candidate prognostic signature gene, KIF20A , has also been reported to be strongly expressed in nasopharyngeal carcinoma (NPC) (49), NSCLC (14,50,51), HCC (52), cervical cancer (53), glioma (54), OC (55) and clear cell renal cell carcinoma (ccRCC) (56). Furthermore, a high expression of KIF20A in these types of cancer has also been shown to be associated with an increased risk of an unfavorable prognosis (14,49–53,55,56).…”

Section: Discussionmentioning

confidence: 99%

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

et al. 2019

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The current study aimed to identify the potential clinical significance and molecular mechanisms of kinesin (KIF) family member genes in lung adenocarcinoma (LUAD) using genome-wide RNA sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) database. Clinical parameters and RNA-seq data of patients with LUAD from the TCGA database enabled the assessment of the clinical significance of KIF genes, while the potential mechanisms of their interactions in LUAD were investigated by gene set enrichment analysis (GSEA). A gene signature with potential prognostic value was constructed via a stepwise multivariable Cox analysis. In total, 23 KIF genes were identified to be differentially expressed genes (DEGs) between the LUAD tumor and adjacent non-cancerous tissues. Of these, 8 differentially expressed KIF genes were strongly found to be strongly associated with the overall survival of patients with LUAD. Three of these genes were found to be able to be grouped as a potential prognostic gene signature. Patients with higher risk scores calculated using this gene signature were found to have a markedly higher risk of mortality (adjusted P=0.003; adjusted HR, 1.576; 95% CI, 1.166–2.129). Time-dependent receiver operating characteristic analysis indicated that this prognostic signature was able to accurately predict patient prognosis with an area under curve of 0.636, 0.643,0.665, 0.670 and 0.593 for the 1-, 2-, 3-, 4- and 5-year survival, respectively. This prognostic gene signature was identified as an independent risk factor for LUAD and was able to more accurately predict prognosis in comparison to other known clinical parameters, as shown via comprehensive survival analysis. GSEA enrichment revealed that that KIF14, KIF18B and KIF20A mediated basic cell physiology through the regulation of the cell cycle, DNA replication, and DNA repair biological processes and pathways. On the whole, the findings of this study identified 23 KIF genes that were DEGs between LUAD tumor and adjacent non-cancerous tissues. In total, 8 of these genes had the potential to function as prognostic and diagnostic biomarkers in patients with LUAD.

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“…Lung cancer is one of the most common malignant tumors in the world, and it has become the first cause of death in malignant tumors in urban population in China (Yang et al., 2018). Non-small cell lung cancer (NSCLC) as the main type of lung cancer accounts for about 80% of all cases compared with small cell carcinoma and about 75% of patients are already in the advanced stage (Boolell et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Zhang

Peng

Sun

et al. 2019

PeerJ

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Background. Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC. Methods. We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA. Results. In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research. P. 2018. Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis. PeerJ 6:e5180 DOI 10.7717/peerj.5180. Ebrahimiyan H, Rezaei N, Vojdanian M, Aslani S. 2019. microRNA involvement in the regulation of survivin in peripheral blood mononuclear cells from rheumatoid arthritis patients.

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Identification of genes and analysis of prognostic values in nonsmoking females with non-small cell lung carcinoma by bioinformatics analyses

Cited by 25 publications

References 48 publications

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

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

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

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