ArticleTranslational cancer research2022
Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Effectiveness and Bioinformatics Analysis of Yiqi and Blood-Activating Therapy Combined with Chemotherapy in the Treatment of Non-Small Cell Lung Cancer.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Article
- Function and prognostic value of basement membrane -related genes in lung adenocarcinoma.Frontiers in pharmacology · 2023Article
- A network-biology approach for identification of key genes and pathways involved in malignant peritoneal mesothelioma.Genomics & informatics · 2021Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses. Methods: Gene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape software platform. The expression of hub genes in NSCLC was validated through the GEPIA database. Kaplan-Meier plotter was used to analyse the survival rate with multivariate Cox regression. The expression of protein tyrosine kinase 2 (PTK2) in NSCLC and adjacent lung tissues was evaluated on the UALCAN database platform. Results: A total of 225 significant DEGs were obtained between NSCLC and adjacent lung tissues, containing 52 upregulated genes and 173 downregulated genes. The DEGs were clustered based on functions and signaling pathways that may be closely associated with NSCLC occurrence. A total of 174 DEGs were identified from the PPI network complex. Top 10 hub genes were selected by CytoHubba plugin. As independent predictors, seven genes ( Conclusions: Taken together, the important genes and pathways in NSCLC were identified by using integrated bioinformatics analysis. PTK2 could be a key gene associated with the biological process of NSCLC formation and progression and a potential therapeutic target for NSCLC treatment.
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