Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis

Shengjin Cui; Shuang Lou; Jingying Feng; Xi Tang; Xiaowei Xiao; Rong Huang; Weiquan Guo; Yiwen Zhou; Feixia Huang

doi:10.21037/tcr-21-1986

Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis

Transl Cancer Res. 2022 Apr;11(4):710-724. doi: 10.21037/tcr-21-1986.

Authors

Shengjin Cui¹, Shuang Lou¹, Jingying Feng¹, Xi Tang¹, Xiaowei Xiao¹, Rong Huang¹, Weiquan Guo¹, Yiwen Zhou¹, Feixia Huang²

Affiliations

¹ Department of Clinical Laboratory, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
² Department of Traditional Chinese Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.

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 (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.

Keywords: Non-small cell lung cancer (NSCLC); bioinformatics; differentially expressed genes (DEGs).