ArticleInternational journal of endocrinology and metabolism2018
Human Prolactinoma: A View of Protein-Protein Interaction Pattern.
Article in International journal of endocrinology and metabolism, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Assessment of colon cancer molecular mechanism: a system biology approach.Gastroenterology and hepatology from bed to bench · 2021Article
- Gestational Diabetes Mellitus Regulatory Network Identifies hsa-miR-145-5p and hsa-miR-875-5p as Potential Biomarkers.International journal of endocrinology and metabolism · 2019Article
- Evaluation of gene expression change in eosinophilic gastroenteritis.Gastroenterology and hepatology from bed to bench · 2019Article
- Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network.Galen medical journal · 2019Article
- Gene expression analysis of colon high-grade dysplasia revealed new molecular mechanism of disease.Gastroenterology and hepatology from bed to bench · 2018Article
- Metabolic analysis of acute appendicitis by using system biology approach.Gastroenterology and hepatology from bed to bench · 2018Article
- Barrett's esophagus network analysis revealed that arginine, alanine, aspartate, glutamate, valine, leucine and isoleucine can be biomarkers.Gastroenterology and hepatology from bed to bench · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to identify the highly expressed genes in terms of interaction concept in Prolactinoma. The study was conducted by additional analysis of the available data from the GEO database. The online tool, GEO2R, was used to analyze the gene expression profile of GSE36314 dataset using the GPL8300 platform. Consequently, a PPI network of up-regulated and down-regulated genes was constructed and examined to introduce the possible targets with possible therapeutic values. A number of 46 genes were dysregulated in Prolactinoma and their network indicated 15 essential genes via topological analysis. Moreover, the present study found that the highlighted genes of prolactinoma are involved in two major biological processes including growth regulation and metabolic function. Thus, the determined genes may be valuable for diagnosis, treatment, and patient follow-up. However, further studies are essential to validate this conclusion.
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