Diagnosis and Prognosis of Non-small Cell Lung Cancer based on Machine Learning Algorithms

Abstract

Background: Non-small cell lung cancer (NSCLC) has been the subject of intense scholarly debate. We aimed to identify the potential biomarkers via bioinformatics analysis. Methods: Three datasets were downloaded from gene expression omnibus database (GEO). R software was applied to screen differentially expressed genes (DEGs)and analyze immune cell infiltrates. Gene set enrichment analysis (GSEA) showed significant function and pathway in two groups. The diagnostic markers were further investigated by multiple machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)). Various online analytic platforms were utilized to explore the expression and prognostic value of differential genes. Furthermore, western blotting was performed to test the effects of genes on cell proliferation in vitro. Results: We identified 181 DEGs shared by two datasets and selected nine diagnostic markers. Those genes were also significantly overexpressed in the third dataset. Topoisomerase II alpha (TOP2A) is overexpressed in lung cancer and associated with a poor prognosis, which was confirmed using immunohistochemistry (IHC) and western blotting. Additionally, TOP2A showed a negative correlation with immune cells, such as CD8+ T cells, eosinophils and natural killer (NK) cell. Conclusion: Collectively, for the first time, we applied multiple machine learning algorithms, online databases and experiments in vitro to show that TOP2A is a potential biomarker for lung adenocarcinoma and could facilitate the development of new treatment strategies.