Abstract
Background: The lung cancer has become the most lethal cause of cancer-related death in China and is responsible for more than 1 million deaths all of the world every year, especially non-small cell lung cancer (NSCLC). Although great advance in pharmaceutical therapies for lung cancer patients, the overall survival is still poor. It is necessary to find out the effective biomarkers in order to improve and predict the prognosis of lung cancer patients. The integrated bioinformatical analysis, as a useful tool to dig up the valuable clues, can be applied to search new effective therapeutic targets. Methods: In this work, we utilized four NSCLC datasets (GSE18842, GSE31210, GSE33532 and GSE101929) from Gene Expression Omnibus (GEO) to analyze. We totally found that there were 162 differentially expressed genes (DEGs) in these four datasets, including 41 up-regulated genes and 121 down-regulated genes in NSCLC tissues. The analysis of GO enrichment and KEGG pathway was done by DAVID software. Then, we identified 10 core oncogenes by constructing protein-protein interaction (PPI) network. Last, we further analyzed the 10 core oncogenes through Kaplan Meier plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) respectively. Results: We discovered 10 key oncogenes which were associated with the progression and poor prognosis for NSCLC, including ANLN, CCNA2, CDCA7, DEPDC1, DLGAP5, HMMR, KIAA0101, RRM2, TOP2A, and UBE2T. Conclusion: These 10 genes can be served as the therapeutic targets and useful prognostic biomarkers for NSCLC treatment.
Identification of potential tumor-educated platelets RNA biomarkers in non-small-cell lung cancer by integrated bioinformatical analysis