Literature data mining based enrichment analysis on 1,925 genes for lung cancer

Abstract

Background: Approximately 8 % of all lung cancer is due to inherited factors. Risk more than doubles for those with relatives who have had lung cancer. To date, numerous genetic studies have found large number of genes that are related to lung cancer. Most of the studies focused on separate activities of genes that influence disease development.Methods: Literature data mining (LDM) of over 17,884 articles for publications from 1978 to February 2016 was conducted. The articles reported multiple types of marker-disease associations between 1,925 genes and lung cancer. A gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) was performed to discover functional profiles and validate pathogenic significances of these genes to lung cancer. Finally, a network connectivity analysis (NCA) was performed to study associations between the reported genes.Results: The reported genes demonstrate multiple types of association with lung cancer. Results from the enrichment analysis confirm the reports and suggest that these genes play significant roles in lung cancer pathogenesis, as well as in the pathogenesis of other lung cancer-related disorders. NCA results demonstrate that these genes, especially those with high RScores, present strong functional associations with each other.Conclusion: The results suggest that lung cancer genetic causes are linked to a network composed of a large number of genes. LDM together with enrichment and network analysis could serve as an effective approach in finding these potential target genes.