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Computational analysis identifies invasion-associated genes in pituitary adenomas

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Pituitary adenomas are considered to be benign tumours. However, they can infiltrate surrounding tissues, which may cause a failure of complete removal during surgical resection. Thus far, no molecular biomarkers have been identified, which are able to reliably predict the behaviour of this type of tumour. In the present study, a list of differentially expressed genes in invasive pituitary adenomas was obtained using a computational bioinformatics analysis on the DNA microarray expression profiles. The gene expression datasets of a total of 16 samples were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. The gene set enrichment analysis was later conducted on the significantly (FDR<0.05) differentially expressed genes. A total of 194 genes were identified as differentially expressed. The pathway impact analysis revealed that cell adhesion molecules may be vital in the progression of pituitary adenoma invasion. A total of six genes, claudin 7, contactin associated proteinlike 2, integrin α6, junctional adhesion molecule 3, protein tyrosine phosphatase, receptor type C and cadherinassociated protein α1 were identified as molecular biomarkers for pituitary adenoma invasion. The present study identified six novel molecular biomarkers, which may be used for diagnostic or therapeutic purposes. However, further experimental investigations are required to validate the present findings.
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Document Type: Research Article

Affiliations: Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China

Publication date: August 1, 2015

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  • Molecular Medicine Reports is a monthly, peer-reviewed journal available in print and online, that includes studies devoted to molecular medicine, underscoring aspects including pharmacology, pathology, genetics, neurosciences, infectious diseases, molecular cardiology and molecular surgery. In vitro and in vivo studies of experimental model systems pertaining to the mechanisms of a variety of diseases offer researchers the necessary tools and knowledge with which to aid the diagnosis and treatment of human diseases.
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