Bioinformatics analysis of differentially expressed genes in non-small cell lung cancer / 西安交通大学学报(医学版)

ABSTRACT

【Objective】 To analyze the data of non-small cell lung cancer (NSCLC) gene chip using the bioinformatics method, screen differential expression genes (DEGs), and explore the biomarkers related to the prognosis of NSCLC so as to provide a new target for the treatment of NSCLC. 【Methods】 The NSCLC gene chip data were downloaded from the GEO database and the common DEGs in the two datasets were screened by GEO2R tool and FunRich3.1.3 software. The DAVID database was used in GO analysis and KEGG analysis of the DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database; Cytoscape 3.8.0 software was used to select the top 20 hub genes. Then Kaplan-Meier plotter was used to analyze the prognosis of the identified hub genes, and multiple external databases were used to verify the expressions of the hub genes and their relationship with prognosis. 【Results】 A total of 159 intersect DEGs were screened from the two datasets. A total of 20 hub genes were identified via PPI network. Survival analysis and validation results from multiple external databases showed that SPP1 was highly expressed in NSCLC tumor tissues and was significantly correlated with the patients’ poor prognosis (P<0.05). The subgroup analysis showed that SPP1 might cause the poor prognosis by affecting lymph node metastasis. 【Conclusion】 SPP1 may be a biomarker for evaluating the prognosis of NSCLC patients, providing a new idea for the targeted therapy of NSCLC.