Balkan Journal of Medical Genetics

MICROARRAY TECHNOLOGY REVEALS POTENTIALLY NOVEL GENES AND PATHWAYS INVOLVED IN NON-FUNCTIONING PITUITARY ADENOMAS
Qiao X, Wang H, Wang X, Zhao B, Liu J,
*Corresponding Author: Jun Liu, M.D., Department of Neurosurgery, The Second Hospital of Jilin University, 218 Ziqiang Road, Changchun, 130021, Jilin Province, People’s Republic of China. Tel: +86-138-0431-7080. E-mail: LiuJun66@126.com
page: 5

MATERIALS AND METHODS

Microarray Data. Microarray dataset of gene expression, GSE26966 [14], was downloaded from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE26966). In this dataset, nine normal human pituitary samples were collected from individuals without an endocrine dysfunction at autopsy 2-18 hours post death, and 14 NFPAs samples were obtained from patients at the time of transsphenoidal surgery after obtaining the patient’s or their families’ permission [14]. Moreover, the 14 NFPA samples contained 10 human GnPA samples [histological analysis: >5.0% staining for α-subunit (ASU), follicle-stimulating hormone (FSH) or lutein-izing hormone (LH)] and four ncPA samples (histological analysis: <5.0% staining for ASU, FSH or LH) [14]. Clinical characteristics of tumor samples were: male/female = 8/6, mean age (years) = 61.4, invasive/noninvasive = 7/7, and recurrent/non-recurrent = 5/9. Clinical characteristics of normal controls were: male/female = 4/5 and mean age (years) = 55.9 years that had no significant difference in comparison with tumor samples (p value = 0.39) [14]. Raw microarray data were collected using Affymetrix Human Genome U133 Plus 2.0 Array (http:// www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GPL570) in the previous study [14]. Pre-Treatment and Differential Analyses. Robust multi-array average algorithm in the affy package (from http://www.bioconductor/org/package/release/bioc/ html/ affy.html) [17] in R was chosen for background correction, data normalization, and calculation of expression values. T-test in package simpleaffy [18] was performed, and fold change (FC) values were determined. Then, p values were corrected using the Bonferroni method, and corrected p value <0.05 and [log2 FC] >2 were set as the cut-off to identify DEGs. Thereafter, package Pheatmap (https:// cran.r-project/org/web/packages/pheatmap/index. html) [19] in R was utilized to cluster genes and samples based on the expression values of DEGs. Functional and Pathway Enrichment Analyses. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using package GOstats (http://www.biocon ductor. org/packages/release/bioc/GOstats.html) [20]. The p value <0.05 was set as the threshold. User data mapping module in the KEGG database (http://www.kegg/jp) was utilized to visualize the significantly enriched pathways. Construction of Protein-Protein Interaction Network. For all of the identified DEGs, a PPI network was constructed with information from a well-known online server, Search Tool for the Retrieval of Interacting Genes/ Proteins version 10 (STRING v10) (http://string-db.org) [21]. Only the PPIs with a confidence score of >0.4 were defined as significant PPIs, which were then utilized to construct the PPI network. The network was visualized using software Cytoscape version 2.8 (http://www.cyto scape.org) [22], and node degrees were determined. Potential Novel Non-Functioning Pituitary Adenoma- Related Genes and Sub-Network. In order to find potential novel disease genes, known genes implicated in pituitary tumorigenesis were obtained from the Comparative Toxicogenomics Database (CTD) (the most recently released version was up-dated on February 9 2016, http:// ctdbase.org/) [23]. Afterwards, the appearance of these known genes were checked in the PPI network to see whether the known genes were DEGs. Common genes, namely, the overlapped genes, were marked in the PPI network. Other DEGs were defined as potential novel NFPAs-related genes, as they were significantly altered in NFPA specimens and interacted with known disease genes. Furthermore, the top 10 significant DEGs, and DEGs directly interacting with the top DEGs, were extracted to construct a sub-network.

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