Cancer Genetics and Epigenetics 2015, Vol.3, No.12, 1-7
1
Research Article Open Access
The Identification of Differentially Expressed Genes of Human Prolactinoma by
Microarray
Zhang C.L.
1
, Zhao N.
1
, Wu S.Y.
3
, Song J.
2
, Kang Y.J.
2
, Liu S.
2
, Zhang D.W.
2
1. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
2. The 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
Corresponding author email
Cancer Genetics and Epigenetics, 2015, Vol.3, No.12 doi: 10.5376/cge.2015.03.00012
Received: 17 Aug., 2015
Accepted: 18 Sep., 2015
Published: 30 Sep., 2015
© 2015 Zhang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:
Zhang C., Zhao N., Song J., Kang Y., Liu S., and Zhang D., 2015, The Identification of Differentially Expressed Genes of Human Prolactinoma by Microarray,
Vol.3, No.12, 1-7
Abstract
Prolactinoma is the most common intracranial neoplasms. Although prolactinoma is always treated with anticarcinogen,
many patients recurrence after curing. This indicates that we need to identify a new mechanism for the treatment of prolactinoma. In
order to recognize new biomarkers, we identify the differentially expressed genes (DEGs) by the microarray. A total of 86 DEGs are
identified including 35 up-regulated genes and 51 down-regulated genes. The set of DEGs can distinguish tumor samples and normal
samples significantly. The genes are mainly enriched in 33 Go terms and 2 kegg pathways associated with prolactinoma. In order to
recognize the function of DEGs, we import these genes into protein-protein interaction network to analyze these genes. For example,
MDM2, LYN, CDH1, GH1, ACTG1 and FUS play an important role in prolactinoma. In summary, the gene set we recognize can
provide potential effect for treatment of prolactinoma..
Keywords
Microarray, Bioinformatics, Biomarker, Prolactinoma, Differentially expressed gene
Introduction
Prolactinoma is a benign tumor that secretes too much
prolactin. it is also one of the most adult pituitary
tumor and appears in pituitary gland. It often happens
in young women with 20-30 years old. The tumor
often leads to amenorrhea, galactorrhea, loss of
axillary and pubic hair, hypogonadism, gynecomastia
and erectile dysfunction. Finally the tumor can result
in the cessation of growth. Till now, the main way for
the treatment of prolactinoma is dopamine, and it can
decrease prolactin secretion(Asa and Ezzat, 1998).
Although there are many researches for the analysis of
prolactinoma, the pathogenesis is still unclear. The
microarray technique can analyze the differential
genes at expression level more accurately (Elston et
al., 2008; Evans et al., 2003). It can identify many
tumor-related genes for the further analysis of cancers.
In this study, we recognize many DEGs and
biological process which are correlated with
prolactinoma and can provide some information about
the mechanism for tumors. Furthermore we can find
new biomarkers for the treatment of prolactinoma
(Ramasamy et al., 2008).
1.Materials and Methods
1.1 Data
The microarray data we use are downloaded from
gene expression omnibus (GEO) with the number
GSE36314. There are four tumor samples and three
normal samples for our analysis. The tumor samples
are available during trans-sphenoidal surgery, and the
normal samples are available from dead individuals.
All samples are analyzed through the platform
Affymetrix Human Genome U95 Version 2 Array
(GPL8300) [HG_U95Av2].
1.2 Microarray analysis
The format of data we download from GEO is CEL.
We apply the method “RMA” in the package “Affy”
to preprocess the data (Guo et al., 2014). The
preprocessing includes background adjustment, quantile
normalization, finally summarization, log 2 and so on.
Next we use perl to delete the arrays with “NA”.
Coefficient of variation (CV) is used to assess the
discrete degree of all data. The genes with CV less
than 0.5 in more than 80% samples are remained.
CV=SD/AVE. CV is the coefficient of variation
of arrays belonging to the same gene in one sample,
