Using Liquid Association Analysis to detect controller genes involved in pituitary nonfunctioning adenoma invasiveness

Nowadays, considerable disease-related high-throughput "omics" datasets are freely available. Such datasetscontain valuable information about disease-related pathways and their corresponding gene interactions.Currently, knowledge of the non-functioning pituitary adenoma (NFPAs) invasion at the molecular level isnot sufficient. The present study aimed to identify critical biomarkers and biological pathways associatedwith invasiveness in the NFPAs using a three-way interaction model. This model can detect the dynamicnature of the co-expression relationship of two genes ({X۱, X۲}) by introducing a third gene (X۳), which issometimes referred to as the controller gene. Indeed, the expression level of the controller gene modulatesthe correlation between X۱ and X۲. One of the statistical methods for this purpose is liquid associationanalysis.This study used the Liquid association method to capture the statistically significant triplets involved inNFPAs invasiveness. Random Forest analysis was applied to select the most critical controller genes. Finally,gene set enrichment and gene regulatory network analyses were applied to detect the biological relevance ofthe statistically significant triplets. This study suggests Nkx۳-۱ and Fech as two controller genes that mightbe critical in the invasiveness behavior of NFAPs. Moreover, the "mRNA processing" and "spindleorganization" pathways are suggested as two crucial pathways involved in the NFAPs' invasiveness.

کلیدواژه ها:

Non-functioning pituitary adenomas ، Invasiveness ، fast liquid association ، Random forestclassification ، Gene set enrichment analysis