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ORIGINAL ARTICLE
Year : 2018  |  Volume : 14  |  Issue : 4  |  Page : 833-837

A differential expression network method identifies ankylosing spondylitis-related genes


1 Department of Traumatology, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China
2 Department of Rehabilitation, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China
3 Department of Orthopaedics, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China

Date of Web Publication27-Jun-2018

Correspondence Address:
Xin Zi
Department of Orthopaedics, Linyi People's Hospital, The Northern Segment of Yimeng Road, Linyi 276000, Shandong Province
P.R. China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.188294

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 > Abstract 


Background: The exact pathogenic mechanism of ankylosing spondylitis (AS) is still unclear.
Objective: we aimed to screen key genes associated with AS using differential expression network (DEN), and further to reveal the molecular mechanism of AS.
Materials and Methods: First, the gene expression data of AS were recruited and preprocessed. Meanwhile, differentially expressed genes (DEGs) were identified. Then, the DEN including the differential interactions and the nondifferential interactions were constructed, and the hub genes were determined according to degree centrality analysis of nodes. Finally, pathway enrichment analysis was conducted on these genes contained in the DEN to further to determine the importance of the hub genes.
Results: A total of 20,102 genes were obtained and 145 DEGs which including 99 upregulated genes and 46 downregulated genes were identified. Then, a DEN which contained 434 differential interactions and 2 nondifferential interactions were constructed. In the following, four hub genes which were USP7, hepatoma-derived growth factor, EP300, and split hand/foot malformation type 1 (SHFM1) were screened out. None of them was DEGs. Finally, the hub genes of EP300 and SHFM1 were enriched in the pathways of prostate cancer and adherens junction and proteasome pathway, respectively.
Conclusions: Compared to the traditional differential genes methods, DEN is a more useful and comprehensive method to conduct on the AS. We predict that these genes (such as EP300 and SHFM1) could be chosen as novel predictive markers for AS.

Keywords: Ankylosing spondylitis, centrality analysis, differential expression network, hub genes


How to cite this article:
Gao P, Fu S, Liu Y, Zi X. A differential expression network method identifies ankylosing spondylitis-related genes. J Can Res Ther 2018;14:833-7

How to cite this URL:
Gao P, Fu S, Liu Y, Zi X. A differential expression network method identifies ankylosing spondylitis-related genes. J Can Res Ther [serial online] 2018 [cited 2019 Nov 17];14:833-7. Available from: http://www.cancerjournal.net/text.asp?2018/14/4/833/188294




 > Introduction Top


Ankylosing spondylitis (AS), an immune-mediated inflammatory disease, is characterized by inflammation of the spine and sacroiliac joints, and ultimately progressive spinal ankylosis caused by new bone formation.[1] Patients with AS not only have a poor quality of life because they suffer from significant pain but also increase socioeconomic burden due to their work disability.[2] Even though it is generally accepted that AS is highly familial and heritable, with >90% of risk to develop the disease determined genetically, the exact pathogenic mechanism of AS yet remains unclear.[3],[4]

At present, gene expression data-related analysis, such as network analysis, has provided an effective way to reveal potential pathogenic mechanism underlying certain disease. Network-based systems biology offers systematically information of the complex interactions and intricate interwoven relationships among those disease-related genes, which can assist us to figure out the molecular processes during disease development and progression.[5] Traditionally, studies just focus on differential genes [6] or differential network.[7] Differential expression network (DEN), which has been proposed by Sun et al.,[8] is a network that contains not only “differential interactions” but also “nondifferential interactions.” A differential interaction is an edge whose strength significantly changed under different conditions while a nondifferential interaction is defined as an edge with no significant difference, but the linked two nodes were differentially expressed genes (DEGs).[8] The DEN scheme has been proved to cover 3–4 folds more known disease genes than traditional DEGs.[8]

In this study, to filter AS-related genes and further to understand the pathological mechanism of AS, the analysis was performed based on the DEN. First, we conducted recruitment and preprocessing of the gene expression profiles that obtained from ArrayExpress database. Immediately following, DEGs were screened. Then, the DEN which contained not only differential interactions but also contained nondifferential interactions was constructed. We gained hub genes through analyzing the degree centrality of the DEN. Finally, pathway analysis was performed on the genes contained in the main DEN based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to further to determine the importance of the hub genes. The results might be potential biomarkers for early diagnose and therapy of AS, give great insights to reveal pathological mechanism underlying this disease, or even provide a hand for future study of related disease researches.


 > Materials and Methods Top


Gene expression profile data recruitment and preprocessing

Gene expression profile of AS disease was downloaded from ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/), under the accession number of E-GEOD-11886.[9] E-GEOD-11886 existed on the A-AFFY-44 - Affymetrix GeneChip Human Genome U133 Plus 2.0 (HG-U133_Plus_2) platform, and composed of 33 samples (18 normal controls and 15 AS samples).

All of the microarray data and annotation files of healthy human beings and AS were downloaded for further analysis. Since deceptive data also existed in the gene expression profile, we performed the standard pretreatments of datasets on probe level. Background correction was performed by a robust multichip average method,[10] and normalization was used quantile-based algorithm.[11] In addition, perfect match correction was revised by MicroArray Suite 5.0 (MAS 5.0) algorithm [12] and calculation of expression values from probe intensities were conducted by Affy package.[13] Finally, a total of 20,102 genes were obtained.

Identification of differentially expressed genes

It is well-known that DEGs between cases and controls are good disease gene candidates.[14] Thus, it is reasonable for us to select out the genes which have different expression levels in the case group (i.e., the AS patients in this document) relative to control group (i.e., the normal human in this document). After having preprocessed the profile, significance analysis of microarrays (SAM),[15] a robust and straightforward method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements, was used to calculate gene expression values, so as to select out DEGs. For genes with scores greater than an adjustable threshold, the false discovery rate (FDR)[16] was used to estimate the percentage of genes identified by chance. In this study, the threshold value FDR <0.05 and a delta cutoff value of >0.694 were used.

Identification of differential interactions and nondifferential interactions

The interaction or edge strength between genes is usually described by co-expression using correlation coefficient. Here, we employed the Spearman's correlation coefficient (SCC)[17] to evaluate the differential interactions. First, the human protein-protein interaction network (human PPI) were obtained from the Biological General Repository for Interaction Datasets (BioGrid).[18] Next, the genes contained in expression profile were mapped to the human PPI network to filter unnecessary interactions. Then, we calculated the SCC of each remaining edge in normal cases (denoted as A1) and disease strains (denoted as A2), respectively, based on the corresponding gene expression values, the absolute difference between two groups were denoted as |A1–A2|. The subsequent step was to confirm the threshold when P = 0.05.

The programs of determining the threshold were as follows: first, two models (one for the control group and one for the experimental group) which, respectively, include 200,000 random gene relationships were constructed. Second, the SCC of each edge in each model and the absolute difference (|A1–A2|) between two groups were computed separately. At last, the edges were listed in a descending order according to the absolute difference. When P value was set as 0.05, the absolute difference was 0.967. In this case, we picked out those PPI relationships whose absolute difference was more than 0.967 and at the same time, A1 or A2 was >0.7 (strong coefficients) as differential interactions. On the other hand, nondifferential interactions were referred to as the interactions whose absolute difference between A1 and A2 were not >0.967 but the linked genes were DEGs.

Construction of the differential expression network and centrality analysis

As it was indicated in the introduction, a DEN included the differential interactions and nondifferential interactions. Therefore, a DEN was integrated by Cytoscape 2.1 software based on the differential interactions and nondifferential interactions identified above. There were highly connected protein nodes (recognized as hubs) and poorly connected protein nodes (recognized as nonhubs) in a network.[19] It was widely acknowledged that hub nodes are central to the architecture of the network [20],[21] and many experiments had demonstrated that hub proteins tend to be lethal not only in lower organisms but also in higher organisms (including human and mice).[22],[23] Three different measures including degree, closeness, and betweenness were usually utilized to measure the centrality of nodes.[24] Among them, the degree was the simplest which was defined as the number of nodes that a central node was connected to. Although the threshold for a hub gene was defined arbitrary, the identification of hub genes was a compelling one because hub genes were more likely to have special biological properties.[19],[25] In our network, the centralities of the nodes were analyzed using degree measurement, and the top 1% of genes were defined as hub genes.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the nodes

KEGG pathway database is a recognized and comprehensive database including all kinds of biochemistry pathways.[26] In this study, the KEGG database was applied to investigate the enrichment analysis of the nodes contained in the DEN to find the biochemistry pathways which might be involved in the occurrence and development of AS. The Database for Annotation, Visualization, and Integrated Discovery [27] was used to perform the KEGG pathway enrichment analysis with the P < 0.05 and gene count >5.


 > Results Top


Identification of differentially expressed genes

In the present study, to assess gene expression in the normal control group and disease group, DEGs were isolated using SAM analysis. Under the threshold value of FDR <0.05 and a delta cutoff value of >0.694, a total of 145 DEGs which including 99 upregulated genes and 46 downregulated genes were screened out.

Identification of differential interactions and nondifferential interactions

Prior to analysis, the PPI data, which contained 15,750 genes (248,584 interactions) were downloaded from the BioGrid database. After mapping the genes contained in expression profile to the human PPI network, a total of 13,868 genes (222,546 interactions) were remained for further analysis. By performing SCC to evaluate the differential interactions, we obtained the SCC of these 222,546 interactions in different conditions, respectively. The details were shown in [Figure 1]. Under the threshold value of |A1–A2| >0.967, as well as at least one of A1 or A2 >0.7, we gained a total of 434 gene interactions. In addition, among the interactions of whose |A1–A2| ≤0.967, there were 2 nondifferential interactions. In this case, there were 434 differential interactions and 2 nondifferential interactions in all.
Figure 1: The distribution of Spearman's correlation coefficient of normal and ankylosing spondylitis groups

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Differential expression network construction and centrality analysis

All of the 434 differential interactions and 2 nondifferential interactions were integrated by Cytoscape 2.1 software to construct a DEN. A DEN consisted of 436 interactions and 610 nodes were constructed. However, there were some gene pairs that with no relationship with the main DEN, so we discarded them. The main DEN was shown in [Figure 2]. By performing centrality analysis on the DEN and based on the threshold value of the top 1% of genes were hub genes, four hub genes were identified. They were USP7, hepatoma-derived growth factor (HDGF), EP300, and split hand/foot malformation type 1 (SHFM1). None of them was DEGs.
Figure 2: The main differential expression network implicated in ankylosing spondylitis. The enlarged nodes represented the hub genes. The red nodes represented upregulated genes. Red edges and green edges represented differential interactions with positive expressed and negative expressed, respectively

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Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the nodes

To further to disclose the functional action of the nodes contained in the DEN, KEGG pathways enrichment analysis was conducted on the nodes contained in the DEN. Under the P < 0.05 and gene count >5, 13 KEGG pathways were obtained. The details were shown in [Table 1]. Moreover, the hub gene EP300 was enrichment in pathways of prostate cancer and adherens junction, and the hub gene SHFM1 was enrichment in proteasome pathway. However, neither of them is DEGs.
Table 1: The pathway details of the Kyoto Encyclopedia of Genes and Genomes pathway analysis on the genes contained in the main differential expression network

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 > Discussion Top


In this paper, to obtain biological insights into the development and progression of AS at the network level, DEN was utilized to characterize the AS. As centrality analysis was conducted on the DEN and under the threshold value of the top 1% of genes were hub genes, we identified four hub genes, which were USP7, HDGF, EP300, and SHFM1. None of them was DEGs. In addition, under the P < 0.05 and gene count >5, 13 KEGG pathways were obtained. Moreover, the hub gene EP300 was enrichment in the pathways of prostate cancer and adherens junction, and the hub gene SHFM1 was enrichment in proteasome pathway.

It is well confirmed that differential gene expression levels can reflect the propensity of many disease genes.[28] Hence, genes show different expression levels between sick subjects and control group may be candidate disease genes. Differential expression levels are usually detected by microarray studies.[29] In this study, we isolated 145 DEGs between normal case and AS case by SAMR program with △ = 0.694. However, gene products usually not function in isolation but interact with each other. Thus, we selected out those interactions whose strength had a significant difference between the case crowd and the control strains. Note that genes or proteins in a differential interaction may not be differentially expressed. Compared to the traditional methods to find DEGs such as t-test or fold change,[30],[31] DEN approach provide us a more comprehensive access to analysis candidate disease genes from a new vision. Since the DEN not only involved the significantly changed genes but also the significantly changed interactions.

There was a phenomenon known as the centrality-lethality rule in the PPI network, which meant a small number of highly connected protein nodes (known as hubs) was more likely to be lethal to organisms.[19] Thus, we carried out the centrality analysis of the DEN and screened out four hub genes from the net frame. The four hub genes were all not DEGs and have not been reported to be associated with AS directly. However, a previous study discovered that the interaction between ERAP1 and HLA-B27 had important roles in the mechanism of AS susceptibility. Hence, we suggested that our hub genes may influence the susceptibility of AS by interacting with other genes. The hub gene USP7, which had the highest degree of 13, is an ubiquitin-specific protease that functions key roles in the p53 pathway.[32],[33] It is well-known that p53 is indispensable to many cellular processes, such as cell-cycle control and apoptosis.[34] We deduced that USP7 may influence AS through p53 pathway. Another hub gene HDGF, a nuclear protein with both mitogenic and angiogenic activity, has been reported to have a wide range of biological functions. Reports have demonstrated that downregulated expression of HDGF can both suppress cell growth and invasion in cancer.[35],[36]HDGF also had many other biological functions that we not mentioned,[37],[38],[39] thus we conjectured that HDGF may also play an important role in AS. Similar to HDGF, EP300 (E1A binding protein p300), an acetyltransferase which can regulate transcription through chromatin remodeling and the processes of cell proliferation and differentiation, also had important biological functions in many aspects.[40] We speculated that there may be some unclear mechanisms existed between EP300 and AS. The last hub gene was SHFM1, which had a great effect on the regulation of gene transcription and cell proliferation.[41]

This method was a brand new approach for AS; we successfully identified four hub genes. Although they had not been reported to be associated with AS previously, they were a potential candidate for being involved in heritable disease susceptibility due to their biological functions. However, several drawbacks of our work must be taken into account. First, the gene expression profiles may have a high false positive rate because they were downloaded from the online database instead of obtained by ourselves. Second, the data of hub genes on AS were so limited that further investigations need to be carried out to confirm our results.


 > Conclusions Top


Therefore, the DEN method was a more comprehensive method to analyze differential expressions for AS from a network vision. Based on this method, several key genes (USP7, HDGF, EP300, and SHFM1) were identified that may be susceptibility with AS. We predict that these genes could be chosen as novel predictive markers for AS.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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