Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis.
Background. The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods. We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paraca...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/11/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152954299&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152954299 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/11/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 152954299 152954299 152954299 10.1155/2021/5516218 152954299 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis. aug: au: Wang, Yu Hu, Sibo Bai, Xianguang Zhang, Ke Yu, Ruixue Xia, Xichao Zheng, Xinhua affil: College of Medicine, Pingdingshan University, Pingdingshan, Henan, China sug: subj: Genes Lung Neoplasms Non-Smokers Bioinformatics Methods Women Carrier Proteins Metabolic Networks and Pathways Databases Tumor Markers, Biological Gene Expression Profiling Microarray Analysis Molecular Structure Software Cancer Screening RNA Biochemical Phenomena Genetic Techniques Utilization Ontologies Signal Transduction Genetic Research World Wide Web Utilization Data Analysis, Computer Assisted Molecular Probe Techniques Human Female Female ab: Background. The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods. We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣ log 2 fold change FC ∣ > 2. Results. A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. Conclusions. We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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