# In silico screening system based on a transcription factors regulatory network only using transcriptomic data

## TL;DR

This study introduces a computational method to identify key transcription factors involved in cell differentiation using only gene expression data.

## Contribution

The novel ISNA method identifies core transcription factors using regulatory network analysis and dissociation constants from transcriptomic data.

## Key findings

- ISNA successfully predicted core TFs in endothelial-to-mesenchymal transition and mesodermal differentiation.
- HMGA2 was identified as a novel core TF in uterine epithelium, regulating cell proliferation in response to estrogen.

## Abstract

In this study, we developed a method to identify core transcription factors (TFs) involved in differentiation using only comprehensive gene analysis. The theory of in silico screening using TFs regulatory network analysis (ISNA) required the following requirements: (1) estimating promoter regions, (2) constructing TFs regulatory network (TRN) relationships using the nucleotide sequence information in the promoters and score matrices derived from TF consensus sequences, and (3) identifying candidate core TFs by determining dissociation constants (Kd values) within the relationships of TRN. ISNA demonstrated the ability to predict the core TFs involved in the endothelial-to-mesenchymal transition of human umbilical vein endothelial cell (HUVEC) and the differentiation of human embryonic stem cells into mesodermal cells. Using ISNA, we identified HMGA2 as a novel core TF in uterine epithelium. Notably, HMGA2 expression was predominantly detected in uterine epithelium, where it regulated cell proliferation in response to estrogen. These findings highlight ISNA’s potential to identify core TFs based on transcriptomic data.