DNA genotype can affect gene expression, and gene expression can influence the onset and progression of diseases. Here we conducted a comprehensive study, we integrated analysis of gene expression profile and single nucleotide polymorphism (SNP) microarray data in order to scan out the critical genetic changes that participate in the onset and development of non-small cell lung cancer (NSCLC). Gene expression profile datasets were downloaded from the GEO database. Firstly, differentially expressed genes (DEGs) between NSCLC samples and adjacent normal samples were identified. Next, by STRING database, protein-protein interaction (PPI) network was constructed. At the same time, hub genes in PPI network were identified. Then, some functional SNPs in hub genes that may affect gene expression have been annotated. Finally, we carried a study to explore the relationship between functional SNPs and NSCLC risk and overall survival in Chinese female non-smokers. A total of 488 DEGs were identified in our study. There are 29 proteins with a higher degree of connectivity in the PPI network, including FOS, IL6 and MMP9. By using database annotation, we got 8 candidate functional SNPs that may affect the expression level of hub proteins. In the case-control study, we found that rs4754-T allele, rs959173-C allele and rs2239144-G allele were the protective allele of NSCLC risk. In dominant model, rs4754-CT+TT genotype were associated with a shorter survival time. In general, our study provides a novel research direction in the field of multi-omic data integration, and helps us find some critical genetic changes in disease.
Keywords: differentially expressed genes; functional single nucleotide polymorphism; non-small cell lung cancer; risk; survival.