Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide. Herein, we employed an integrated bioinformatics approach to identify crucial genes and to investigate the underlying mechanism in the poor prognosis of NSCLC. We performed an integrated analysis of microarray datasets (GSE33532, GSE19188, GSE102287, GSE27262) containing 319 NSCLC and 232 normal lung tissues. 52 up-regulated and 173 down-regulated overlapping-differentially expressed genes were identified, further validated by cBioportal. DAVID was employed for Gene Ontology, KEGG pathway analysis, and STRING for protein-protein interaction network analysis. Cytoscape plug-ins, MCODE and CytoHubba, were used to acquire module 1–2 and to screen the top ten core genes, respectively. GEPIA and Kaplan-Meier plotter analyzed the expression levels and survival rates of the ten core genes, respectively. Four genes were up-regulated (COL1A1, MMP1, ADAM12, CTHRC1), and six genes were down-regulated (VWF, CD36, OGN, EDN1, CAV1, ITGA8) in NSCLC. Seven genes (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with NSCLC's poor prognosis (P < 0.01), four of which (VWF, CAV1, ITGA8, COL1A1) were enriched in the focal adhesion pathway (P = 1.04E-04), as per KEGG pathway analysis. It suggests that VWF, CAV1, ITGA8, and COL1A1 might promote NSCLC via the focal adhesion pathway.
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 (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with poor prognosis in NSCLC via multivariate Cox regression (P<0.01). Four genes (VWF, CAV1, ITGA8, COL1A1) were found to be significantly enriched in the focal adhesion pathway (P=1.04E-04) and to be upstream regulators of PTK2. PTK2 was upregulated in NSCLC and associated with poor survival prognosis in lung squamous cell carcinoma (LUSC).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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