Author/Authors :
Izadi, Fereshteh Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center - Research Institute for Gastroenterology and Liver Diseases - Shahid Beheshti University of Medical Sciences, Tehran, Iran , Rezaei Tavirani, Mostafa Proteomics Research Center - Shahid beheshti University of Medical Sciences, Tehran, Iran , Honarkar, Zahra Gastroenterology Department - Atiyeh Hospital, Tehran, Iran , Rostami-Nejad, Mohammad Gastroenterology and Liver Diseases Research Center - Research Institute for Gastroenterology and Liver Diseases - Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract :
Aim: we mainly aimed to elucidate potential comorbidities between celiac disease and hepatitis c by means of data and network
analysis approaches.
Background: understanding the association among the disorders evidently has important impact on the diagnosis and therapeutic
approaches. Celiac disease is the most challenging, common types of autoimmune disorders. On the other hand, hepatitis c virus genome
products like some proteins are supposed to be resemble to gliadin types that in turn activates gluten intolerance in people with inclined to
gluten susceptibilities. Moreover, a firm support of association between chronic hepatitis and celiac disease remains largely unclear.
Henceforth exploring cross-talk among these diseases will apparently lead to the promising discoveries concerning important genes and
regulators.
Methods: 321 and 1032 genes associated with celiac disease and hepatitis c retrieved from DisGeNET were subjected to build a gene
regulatory network. Afterward a network-driven integrative analysis was performed to exploring prognosticates genes and related
pathways.
Results: 105 common genes between these diseases included 11 transcription factors were identified as hallmark molecules where by
further screening enriched in biological GO terms and pathways chiefly in immune systems and signaling pathways such as
chemokines, cytokines and interleukins.
Conclusion: in silico data analysis approaches indicated that the identified selected combinations of genes covered a wide range of
known functions triggering the inflammation implicated in these diseases.