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Exomic and transcriptomic alterations of hereditary gingival fibromatosis
Author(s) -
Han Seong Kyu,
Kong Jungho,
Kim Sanguk,
Lee JaeHoon,
Han DongHoo
Publication year - 2019
Publication title -
oral diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.953
H-Index - 87
eISSN - 1601-0825
pISSN - 1354-523X
DOI - 10.1111/odi.13093
Subject(s) - phenotype , fibromatosis , disease , gene , exome sequencing , transcriptome , etiology , genetic heterogeneity , bioinformatics , medicine , biology , in silico , genetics , pathology , computational biology , gene expression
Objective Hereditary gingival fibromatosis (HGF) is a rare oral disease characterized by either localized or generalized gradual, benign, non‐hemorrhagic enlargement of gingivae. Although several genetic causes of HGF are known, the genetic etiology of HGF as a non‐syndromic and idiopathic entity remains uncertain. Subjects and methods We performed exome and RNA‐seq of idiopathic HGF patients and controls, and then devised a computational framework that specifies exomic/transcriptomic alterations interconnected by a regulatory network to unravel genetic etiology of HGF. Moreover, given the lack of animal model or large‐scale cohort data of HGF, we developed a strategy to cross‐check their clinical relevance through in silico gene–phenotype mapping with biomedical literature mining and semantic analysis of disease phenotype similarities. Results Exomic variants and differentially expressed genes of HGF were connected by members of TGF‐β/SMAD signaling pathway and craniofacial development processes, accounting for the molecular mechanism of fibroblast overgrowth mimicking HGF. Our cross‐check supports that genes derived from the regulatory network analysis have pathogenic roles in fibromatosis‐related diseases. Conclusions The computational approach of connecting exomic and transcriptomic alterations through regulatory networks is applicable in the clinical interpretation of genetic variants in HGF patients.

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