1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Using Liquid Association Analysis to detect controller genes involved in pituitary non-functioning adenoma invasiveness
Paper ID : 1120-ICB10
Authors:
Nasibeh Khayer *1, Mehdi Mirzaie2, Maryam Jalessi3
1Skull Base Research Center, The Five Senses Health Institute, Iran University of Medical Sciences, Tehran, Iran
2Tarbiat Modares University
3دانشگاه علوم پزشکی ایران
Abstract:
Nowadays, considerable disease-related high-throughput "omics" datasets are freely available. Such datasets contain 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 is not sufficient. The present study aimed to identify critical biomarkers and biological pathways associated with invasiveness in the NFPAs using a three-way interaction model [1-3]. This model can detect the dynamic nature of the co-expression relationship of two genes ({X1, X2}) by introducing a third gene (X3), which is sometimes referred to as the controller gene [4]. Indeed, the expression level of the controller gene modulates the correlation between X1 and X2. One of the statistical methods for this purpose is liquid association analysis [5,6].
This study used the Liquid association method to capture the statistically significant triplets involved in NFPAs invasiveness. Random Forest analysis [7,8] was applied to select the most critical controller genes. Finally, gene set enrichment [9,10] and gene regulatory network [11] analyses were applied to detect the biological relevance of the statistically significant triplets. This study suggests Nkx3-1 and Fech as two controller genes that might be critical in the invasiveness behavior of NFAPs. Moreover, the "mRNA processing" and "spindle organization" pathways are suggested as two crucial pathways involved in the NFAPs' invasiveness.
Keywords:
Non-functioning pituitary adenomas, Invasiveness, fast liquid association, Random forest classification, Gene set enrichment analysis.
Status : Paper Accepted (Oral Presentation)