Document Type : Research articles


1 Research Center of Molecular Medicine of Yunnan Province, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, P.R. China

2 Medical Oncology, The First People’s Hospital of Yunnan Province, Kunming, P.R. China

3 Institute of Molecular and Clinical Medicine, Kunming Medical University, Kunming, P.R. China


Background: Non-small cell lung cancer (NSCLC) is the most common type of lung Neoplasms, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological pathways and key genes implicated in NSCLC, and provid- ing insight into the molecular mechanism underlying NSCLC. Methods: In this case-control bioinformatics study, the researchers used four microarray data of NSCLC from public gene expres- sion omnibus (GEO) database at the national center for biotechnology information (NCBI) website. The microarray data came from
studies of American, Spanish, and Taiwanese NSCLC patients, and in total contained 190 NSCLC tissue and 180 normal lung tissue. A standardized- microarray preprocessing and gene set enrichment analysis (GSEA) were used to analyze each microarray data and obtained significantly regulated pathways. Venn analysis was used to identify the common significantly regulated biological path- ways. Protein and protein interaction (PPI) network analysis was used to identify the key genes within common significantly reg- ulated pathways. The PPI information was retrieved from the STRING database, and Cytoscape software was used to construct and visualize the PPI network. Results: Through integrating GSEA results of four microarray data, finally, the researchers identified 22 common up-regulated and 85 common down-regulated pathways. Many genes within 107 common significantly regulated pathways were significantly en- riched within cell cycle pathway (P value of 2.58e-79) and focal adhesion pathway (P value of 2.44e-81). The PPI network showed that
up-regulated CDK1 (P value = 1.33e-18 and logFC = 1.41) and down-regulated PIK3R1 (P value = 5.09e-22 and logFC = -1.13) genes shared the most abundant edges, and were associated with NSCLC. Conclusions: This cross-sectional study showed increased concordance between gene expression profiling data. These identified pathways and genes provide some insight into the molecular mechanisms of NSCLC, and the genes may serve as candidate diagnos-
tic and therapeutic targets of NSCLC.