Identification of Intron Retention Regions in Breast Cancer by Bioinformatics Approaches

This study aimed to evaluate intron retention in publicly available breast cancer (Triple Negative Breast Cancer, TNBC) samples using IRFinder, IRFinder-S, İREAD, and Whippet algorithms. The data were subjected to quality control. The reference genome indexed and aligned with in STAR 2.7.10b algorithm. DiÉerential expression analyses in IRFinder, and IRFinder-S were determined by DEseq2, edgeR in IREAD and Delta PSI results in the Whippet. The genes remaining in the intersection set of the genes passing the threshold values of the algorithms in the Venny 2.1.0 application were analyzed regarding the functions and pathways they are related to in ShinyGO 0.80 and gProfiler g:GOSt functional profiling web tools. According to the results obtained, in the intersection set of IRFinder, IRFinder-S, and IREAD algorithms in terms of intron involvement, 47 (0.4%) genes out of 10395 gene regions were increased and 3 (0.3%) of 1184 genes were decreased. According to the results of pathway analysis of upstream intron retention genes, especially ENSG00000074181 (NOTCH3), ENSG00000142949 (PTPRF), ENSG00000104946 (TBC1D17), ENSG00000180900 (SCRIB), ENSG00000143614 (GATAD2B), ENSG00000182492 (BGN) were found to be retained in breast cancer and general cancer pathways. According to the Cohen Kappa analyses it was determined that the most compatible results were found in the IRFinder and IRFinder-S algorithms, the agreement of the IREAD algorithm was low with both algorithms, and the Whippet algorithm was incompatible by not showing a common intersection gene region. In this direction, it can be suggested that researchers should consider the genes in the intersection regions of IRFinder, IRFinder-S, and IREAD algorithms. It is recommended that researchers verify the regions of intron retention to understand the mechanism of TNBC.