ISSN 0026-8933, Molecular Biology, 2016, Vol. 50, No. 3, pp. 462–469. © Pleiades Publishing, Inc., 2016. Published in Russian in Molekulyarnaya Biologiya, 2016, Vol. 50, No. 3, pp. 530–539.

BIOINFORMATICS UDC 577.2

Detecting Shared Pathways Linked to Rheumatoid Arthritis with Other Autoimmune Diseases in a in silico Analysis1 W.-Y. Zheng, W.-X. Zheng, and L. Hua* College of Biomedical Engineering, Capital Medical University, Beijing 100069, PR China *e-mail: [email protected] Received May 25, 2015; in final form, August 12, 2015

Abstract—Pathway-based analysis approach has exploded in use during the last several years. It is successful in recognizing additional biological insight of disease and finding groupings of risk genes that represent disease developing processes. Therefore, shared pathways, with pleiotropic effects, are important for understanding similar pathogenesis and indicating the common genetic origin of certain diseases. Here, we present a pathway analysis to reveal the potential disease associations between RA and three potential RA-related autoimmune diseases: psoriasis, diabetes mellitus, type 1 (T1D) and systemic lupus erythematosus (SLE). First, a comprehensive knowledge mining of public databases is performed to discover risk genes associated with RA, T1D, SLE and psoriasis; then by enrichment test of these genes, disease-related risk pathways are detected to recognize the pathways common for RA and three other diseases. Finally, the underlying disease associations are evaluated with the association rules mining method. In total, we identify multiple RA risk pathways with significant pleiotropic effects, the most unsurprising of which are the immunology related pathways. Meanwhile for the first time we highlight the involvement of the viral myocarditis pathway related to cardiovascular disease (CVD) in autoimmune diseases such as RA, psoriasis, T1D and SLE. Further Association rule mining results validate the strong association between RA and T1D and RA and SLE. It is clear that pleiotropy is a common property of pathways associated with disease traits. We provide novel pathway associations among RA and three autoimmune diseases. These results ascertain that there are shared genetic risk profiles that predispose individuals to autoimmune diseases. Keywords: rheumatoid arthritis, pathways, association rules, share DOI: 10.1134/S0026893316030146

INTRODUCTION RA has been described in the medical literature for over two hundred years, but its etiology and relationship with other diseases remains to be completely deciphered [1]. As the most common systemic autoimmune disease, RA affects the patient directly or indirectly in almost all organ systems, from cardiovascular problems and infections to depression and gastrointestinal ulcers. In this paper, we focus on RA and three potential RA-related autoimmune diseases: T1D, SLE and psoriasis (TSP) to discover their potential association. There are two reasons for us to select these three autoimmune diseases. One reason is that autoimmune diseases might have a common pathogenesis and tend to occur together within individuals and families. For example, we know that RA and SLE are often confused because they share many similar symptoms. As another example, the inflammation that comes with rheumatoid arthritis may increase the risk of diabetes. For rationality, we used MimMiner score to select the autoimmune diseases which have a high similarity 1 The article is published in the original.

with RA [2]. We found that SLE, T1D and Psoriasis all ranked the top 15 in the disease association list (See Table 1). The other reason is that we wish to find the shared associated genes among these autoimmune diseases by pathway enrichment analysis. Previous studies have found that the variant of some genes in common pathways are shared by several diseases. For example, scientists have found that if a person has the variant of gene STAT4, he has a 50 percent greater chance of developing RA and a doubled risk of developing SLE. Therefore, the findings about the shared genes and pathways between autoimmune diseases will prompt researchers studying other autoimmune diseases to look closer at how they are all linked to each other. To date, Genome Wide Association Studies (GWAS) have successfully recognized thousands of potential genetic variants linked (or associated with) conventional traits, such as autoimmune diseases or cancers. Single gene approach tends to ignore genes with modest effects on phenotype traits. At the same time, genes in the same pathway usually act together to carry out a certain type of functions. So, as a compan-

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Table 1. Rank of RA, SLE, and TSP among 15 other significant similar disease phenotypes Rank 3 10 15

OMIM ID

Score

222100 152700 602723

0.4117 0.3637 0.3434

Disease DIABETES MELLITUS, INSULIN-DEPENDENT LUPUS ERYTHEMATOSUS, SYSTEMIC PSORIASIS SUSCEPTIBILITY 2

ion to GWAS, pathway analysis is a powerful approach since it tests the aggregate association between a pathway, which comprises a set of functionally-related genes, and a disease phenotype [3]. Its capacity of capturing biological interactions among genes and improving power and robustness has been well recognized [4]. Moreover, the pathway mechanism is a natural source for developing strategies to diagnose, treat, and prevent complex diseases [5]. Thus simultaneously analyzing pathways in multiple diseases would bring an important source of additional biological insight for links between diseases. The present study mainly investigates potential common pathways that might predispose individuals to RA and TSP. Here, a comprehensive knowledge mining (mainly via text mining in Pubmed, OMIM disease database and Malacards database) is performed to discover susceptible genes and GSEA analyses to recognize risk pathways for RA and TSP. The link of RA and TSP is speculated with the association rules method of data mining to explore the disease associations. As a result, we recognize multiple RA risk pathways having significant pleiotropic effects, most of which are immunology related pathways. Meanwhile we highlight for the first time the involvement of the viral myocarditis pathway related to CVD in autoimmune diseases RA and TSP. Besides this association rule mining results validate the strong association between RA and T1D as well as RA and SLE. These results could potentially provide insights into the etiology of RA diseases and molecular links with other autoimmune diseases. METHODS Data source. The list of disease-associated genes was mainly retrieved from knowledge databases Online Mendelian Inheritance in Man (OMIM), Pubmed and Malacards database [6]. OMIM, a compendium of human disease genes and phenotypes, represents an up to date repository of all known disease genes and the disorders they confer [7]. Since the disease gene record in OMIM is far from complete, we manually searched the Pubmed database and selected those genes that have been proved to be associated with RA and TSP. The Malacards web tool is a good integrated database of human diseases and their annotations, which includes 64 data sources. We also selected disease genes from Malacards as a complement. MOLECULAR BIOLOGY

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Disease-associated gene data set. In the Malacards database using GeneCards Suite and key words “Rheumatoid Arthritis” a total of 1614 genes was obtained. However, gene association does not imply causality between the gene and the disease. Therefore, we used the MalaCards composite related genes score (MCRGS) to filter genes. This score takes into account genes that are associated with a genetic test for the disease, genes that are associated with a known causative variation with respect to the disease, as well as genes that were manually mapped to the disease by other sources. In order to avoid the weakly associated with RA genes from coming into analysis, we filtered genes using the Relevance score. Considering that 31 RA numbered genes (such as RA1, RA2, RA3…) have no practical meaning, we thus removed these genes. Because the filter of RA-related genes has a very significant impact on our further study, we therefore used a strict criterion for the relevance score of 9.53 (the 95th percentile) to filter RA-related genes. Only those genes with relevant scores >9.53 are considered as RA-related genes. We also found that some confirmed RA disease-related genes according to the Pubmed database are not included in the MalaCards database, such as AIF1, an inflammatory cytokine influencing the immune system. AIF1 is reported to show increased levels in the synovial fluid of patients with RA [8]. In order to avoid missing the important RAassociated genes, we manually searched Pubmed and chose genes confirmed by literature as complements to RA-related genes. In order to get more potential disease-related genes common for RA, we did not implement the filter criterion for SLE, psoriasis and T1D associated genes obtained from MalaCards. Considering that some of the confirmed disease-related genes by the OMIM database, such as T1D-associated genes (FOXP3, HNF1A, OAS1, ITPR3, PTPN22) and SLE-associated genes (BANK1) are not included in the MalaCards database, we therefore integrated OMIM and Pubmed data sources to get SLE, psoriasis and T1D associated genes. Mapping genes to pathways. We used biology pathway information to compile a disease-associated gene set. These pathways were obtained from publicly available resources, including KEGG (http://www.kegg.jp/) and BioCarta (http://www.biocarta.com), for assigning genes to pathways. The susceptible genes for each disease were separately found by pathway enrichment analysis by DAVID (http://david.abcc.ncifcrf.gov/),

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in this procedure, multiple testing corrections are used, and the p-values were adjusted by the Benjamini method, p < 0.05 is considered to indicate a significant pathway. A shared pathway is recognized if it is linked to RA and at least one of the TSP. Exploring disease associations. In order to systematically verify the genetic associations between RA and related diseases, we used the association rules method [9] to estimate the strength of associations between RA and other diseases. Association rule is one of data mining methods, and the Apriori algorithm is often used to discover association rules. Association rule with an implication expression of the form A→B of if “A (Body)” then “B (Head)” reflects the interdependence and correlation between A and B. The strength of an association rule in the Apriori algorithm is determined by its support and confidence. The support of a rule is calculated as: (1) Support( A → B ) = P ( A ∪ B ). The support determines how often a rule is observed in the data. The support is the reliability of a rule. Low support may indicate that a rule has simply occurred by chance, and thus support is one of the parameters used to eliminate uninteresting rules. The confidence of a rule is calculated as:

Confidence( A → B ) = P (B A).

(2)

The confidence determines how often items in B appear in records that contain A, and provides an estimate of P (B A) the conditional probability of B given A. The confidence is the certainty of a rule. The correlation is calculated as:

Correlation( A → B ) = P (B ∪ A)

P ( A) ⋅ P (B ). (3)

The correlation is determined by the support and the confidence. It indicates the intensity of the associations. Here, we construct a gene-disease relationship matrix. Since the main goal of the research is on association of RA and TSP, we used RA susceptibility genes as a whole set. The element of the matrix aij was defined as

⎧1, if gene i is a susceptible gene ⎪ aij = ⎨of disease j. i = 1,2,…,81; j = 1,2,…,4 ⎪0, otherwise. ⎩ First, we give an ID i for every A-related gene, from 1 to 81, since we got 81 RA susceptible gene from the described above analysis; then, we also set an ID j for RA, T1D, SLE and psoriasis, from 1 to 4. If the ith gene is a susceptible gene of the jth disease, the corresponding value aij in the matrix is set to 1, otherwise to 0. This yields an 81-by-4 matrix. In our case, STATISTICA was used for association rules analysis (http://www.statsoft.com/Products/ STATISTICA). Results of association rules is formally

as “if Body then Head”. For instance, if “RA == 1” then “SLE == 1” indicates the rules between RA and SLE. By setting the minimum support threshold of 20% and the minimum confidence threshold of 30%, we aim to mine more association rules between RA and TSP. RESULTS A Global View of Human “Diseasome” That Links RA with Related Diseases Comprehensive text mining for pleiotropy between RA and other diseases was performed in order to obtain a global view of the human “diseasome” that links RA with related diseases [10]. For the detailed procedures, see the Methods section. When the MalaCards database and other literature data sources such as OMIM and Pubmed databases were integrated, 120 RA-associated genes were selected. After removing those genes that can not be supported by literature, such as RA* and CTS*, 81 RA-related genes was included in this analysis (See Supplementary, www.molecbio.com/downloads/2016/3/supp_zheng_ engl.pdf, Table 1). When selecting SLE, psoriasis and T1D associated genes in OMIM, Pubmed and MalaCards database, after removing those genes without practical meaning or without official gene symbol, 234 SLE associated genes, 58 T1D associated genes, and 744 Psoriasis associated genes were selected (See Supplementary, Table 2). These genes were used for subsequent pathway enrichment analysis and association rules analysis. In pathway enrichment analysis, in total, we obtained 25 statistically significant pathways in KEGG and 12 pathways in BioCarta for RA and TSP (Benjamini P < 0.05) (See Supplementary, Table 3). The RA disease risk pathways are shown in Fig. 1 and it is indicated whether they are shared by TSP. Left side is 12 RA pleiotropic pathways in KEGG, center side is RA, T1D, SLE and Psoriasis diseases, and right side is 2 RA pleiotropic pathways in BioCarta. Among the 12 overrepresented pathways in KEGG for RA (Table 2), according to the classification of KEGG, these pathways can be divided into 2 classes: cardiovascular disease (CVD) and immune system diseases. It is unsurprising to note that 11 of them are immunology related pathways, which include type 1 diabetes (hsa09490) and Systemic lupus erythematosus (hsa05322). Most of them are shared by TSP, again emphasizing the immune pathogenesis of RA, T1D, SLE and psoriasis diseases (See Supplementary, Table 3). Here, we highlight for the first time the involvement of the KEGG pathway related to CVD in RA. CVD has been the leading cause of death [11]. In our analysis, we determined that viral myocarditis (hsa05416) is significantly associated with RA ( p = 3.16 × 10 −5 ). Myocarditis is a cardiac disease associated with inflammation and injury of the myoMOLECULAR BIOLOGY

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Table 2. Significant RA pathways in KEGG as identified by text mining of public knowledge databases Pathway ID

Number of genes

Pathway

Gene

HLA-DRB1, HLA-A, IL12A, IL1B, HLA-DPB1, HLA-DMB, HLA-DOA, 12/45 (27%) HLA-G, HLA-DQA1, IL1A, LTA, HLA-DRA

KEGG clacification

P-value

hsa04940

Type I diabetes mellitus

immune system and diseases

6.48E-12

hsa05332

IL6, HLA-DRB1, HLA-A, IL1B, Graft-versus-host immune system 11/43 (26%) HLA-DPB1, HLA-DMB, HLA-DOA, disease and diseases HLA-G, HLA-DQA1, IL1A, HLA-DRA

6.02E-11

hsa05330

HLA-DRB1, HLA-A, IL12A, immune system Allograft rejection 10/39 (26%) HLA-DPB1, HLA-DMB, HLA-DOA, and diseases HLA-G, HLA-DQA1, IL10, HLA-DRA

7.27E-10

hsa05320

HLA-DRB1, HLA-A, CTLA4, Autoimmune thyimmune system 10/54 (19%) HLA-DPB1, HLA-DMB, HLA-DOA, roid disease and diseases HLA-G, HLA-DQA1, IL10, HLA-DRA

1.58E-08

hsa04612

Antigen processing and presentation

CIITA, HLA-DRB1, TAP2, HLA-A, 11/79 (14%) HLA-DPB1, HLA-DMB, HLA-DOA, HLA-G, HLA-DQA1, LTA, HLA-DRA

immune system and diseases

6.65E-08

hsa04060

Cytokine–cytokine receptor interaction

IL2RB, IL6, CCL2, IL2RA, IL6ST, CXCR3, IL10, IL17RA, TNFRSF1B, 16/265 (6%) IL17A, CCL21, CCR2, IL12A, IL1B, IL1A, LTA

immune system and diseases

1.86E-07

hsa05310

Asthma

IL10, HLA-DRB1, HLA-DPB1, 7/32 (22%) HLA-DMB, HLA-DOA, HLA-DQA1, HLA-DRA

immune system and diseases

2.48E-06

hsa04672

Intestinal immune network for IgA production

IL6, HLA-DRB1, HLA-DPB1, HLA8/49 (16%) DMB, HLA-DOA, HLA-DQA1, IL10, HLA-DRA

immune system and diseases

3.01E-06

hsa04514

Cell adhesion molecules (CAMs)

VCAM1, PTPRC, HLA-DRB1, HLA-A, immune system 11/145 (8%) CTLA4, HLA-DPB1, HLA-DMB, HLAand diseases DOA, HLA-G, HLA-DQA1, HLA-DRA

3.29E-06

hsa05416

Viral myocarditis

8/60 (13%)

HLA-DRB1, HLA-A, HLA-DPB1, Cardiovascular HLA-DMB, HLA-DOA, HLA-G, HLAdisease DQA1, HLA-DRA

3.16E-05

hsa04621

NOD-like receptor signalling pathway

7/57 (12%)

CARD8, NOD2, IL6, CCL2, MEFV, IL1B, TNFAIP3

immune system and diseases

1.59E-04

hsa05322

Systemic lupus erythematosus

8/136 (6%)

C5,HLA-DRB1, HLA-DPB1, HLADMB, HLA-DOA, HLA-DQA1, IL10, HLA-DRA

immune system and diseases

2.38E-04

cardium. Myocarditis may be caused by a pathological immune response to a persistent virus, or autoimmunity triggered by the viral infection. Viral infection and necrosis of myocytes may lead to the release of intracellular antigens, resulting in the activation of selfreactive T cells [12]. Viral myocarditis has been reported to be a risk pathway of Alzheimer’s disease [13], pancreatic cancer [14], and lung cancer [15] in MOLECULAR BIOLOGY

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pervious researches. Viral infection might be a functional pleiotropic pathway for many diseases. Nowadays, many works are trying to reveal the relationship between RA and CVD. RA may be an independent CVD risk factor and persistent inflammation is an additional risk factor [16]. Individuals with RA are at increased risk for morbidity and mortality from CVD and timely control of the inflammation, immunologic

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KEGG BioCarta

hsa04940 hsa05332 hsa05330 hsa05320 hsa04612 hsa04060

RA T1D h_inflamPathway

SLE h_cytokinePathway

hsa05310 hsa04672

TSP

hsa04514 hsa05416 hsa04621 hsa05322

Fig. 1. RA risk pathways shared by T1D, SLE and psoriasis. Left side is 12 RA pleiotropic pathways in KEGG, center side is RA, T1D, SLE and psoriasis diseases, and right side is 2 RA pleiotropic pathways in BioCarta.

disturbances, and metabolic changes seen in RA are crucial in the prevention of CVD complications [17]. Methotrexate also is recognized to protect against CVD mortality in patients with RA [16]. It is important to note that viral myocarditis (hsa05416) is also recognized as a risk pathway for SLE, T1D and psoriasis in our analysis. Cardiovascular involvement represents the leading cause of mortality in SLE [18]. Many researchers report that diabetes is a strong independent risk factor for CVD mortality [19–21]. B. Saboo’s research indicates that intensive diabetes treatment (including increased education, monitoring, and contact with the diabetes team) reduces the risk of any CVD event by 42% and the risk of a nonfatal myocardial infarction, stroke, or death from CVD by 57% [22]. E. Ortega et al. report a higher prevalence of CVD in the Mediterranean population of T1D individuals compared with non-diabetic subjects. This prevalence is similar to that observed in type 2 diabetes [23]. Psoriasis is known to be associated with increased risk of CVD comorbidities and all-cause mortality [24]. Meanwhile, there is a report pointing out that multiple cardiovascular risk factors are associated with psoriasis. Cardiovascular risk factors that are key components of the metabolic syndrome are more strongly associated with severe psoriasis than with mild psoriasis [24]. We also recognized 2 pathways in BioCarta for RA (See Supplementary, Table 3), one is h_inflamPathway: Cytokines and Inflammatory Response pathway

and the other is h_cytokinePathway: Cytokine Network pathway. They both are shared by at least one of TSP (see Fig. 1). Inflammation is a protective response of the immune system to infection that requires communication between different classes of immune cells to coordinate their actions. Acute inflammation is an important part of the immune response, but chronic inappropriate inflammation can lead to destruction of tissues in autoimmune disorders and perhaps neurodegenerative or CVD. As above, we identify multiple shared pleiotropic pathways implicated in RA and other diseases predisposition that have not been revealed using standard single-locus GWAS statistical analysis criteria. Genes in these pathways are speculated to carry out important pleiotropic functions that lead to disease predisposition. Validation of Genetic Association between Diseases In total, we identified three effective association rules between RA and TSP (See Supplementary, Table 4). There are two rules with credibility more than 50%. One is if “RA = =1” then “T1D = =1”, and the other is if “RA = =1” then “SLE = =1” (Fig. 2). The two rules denote that there is astrong association between RA and T1D, RA and SLE. The rule for RA and T1D has the highest support and confidence level, indicating that the two diseases are strongly correlated or similar at the molecular level. When carefully considering the two diseases, one can find strong MOLECULAR BIOLOGY

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(a)

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(b)

100 90 80 70 60 50 40 30 20

CI, %

CI, %

100 90 80 70 60 50 40 30 20

t ea H

RA

Tl

D

Tl

D

=

=

1

=

=

=

=

1

0

Bo

dy

1 == RA = = 1 0 E SL E = = SL

t ea H

1 == 1 A R == 0 T1D D = = T1

RA

SL

E

SL

E

=

=

1

=

=

=

=

1

0 dy o B

Fig. 2. Aassociations rules network: RA-T1D (а); RA-SLE (b). Strong associations between RA and T1D, RA and SLE are recognized using association rule mining method. Figure 2a shows if “RA = =1” then “T1D = =1” rule, and Fig. 2b shows if “RA = =1” then “SLE = =1”. As shown in Fig. 2a, the support of the rule is denoted by the size and color of the dot within the oval. The vertical axis shows the confidence value, dots within the oval indicate the size of the support, the greater of the dots means the higher of the support value. RA = =1 and T1D = =1 occur simultaneously support value is 63.41%; its height corresponding confidence level is 64.19%. Correlation—80.12%. Figure 2b has the similar explanation to Fig. 2a. Support—58.54%; confidence—59.26%; correlation—79.98%.

symmetrical similarities between T1D and RA [25, 26]. T1D is often found to be associated with an increased risk of RA. So far, there is only one established genetic risk factor shared by RA and T1D, the 620W allele of the PTPN22 gene encoding tyrosine-specific protein kinase [27–32]. Also HLA [33, 34] and several other genes identified in previous studies and this study appear to be associated with both diseases [35, 36]. Clearly, further studies on how the two diseases are related to each other at the molecular level can provide new insights into their etiology, classification, and shared biological mechanisms [10]. The second rule demonstrates the relationship between RA and SLE, with a confidence of 59.26%. This rule is also well documented in rich literature [1, 37]. For both RA and SLE, the underlying molecular mechanisms remain largely unknown although an autoimmune component is involved and both are chronic inflammatory connective tissue disorders that can involve many organs. In addition, both diseases display a significant gender bias towards women. Nevertheless, some patients with SLE will develop a symmetrical polyarthritis while others might not experience any arthritis et al. [1, 38]. The RA–psoriasis link is also recognized by our analysis with a correlation of 69.48%. However, for a confidence level of lower than 50%, and thus the association between RA and psoriasis cannot be supported by our analysis. It needs further analysis and verification. MOLECULAR BIOLOGY

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DISCUSSION Massive complex biological data has brought an onslaught of genetic profiles useful for identifying key inherited genetic variations that have critical but yet largely uncharacterized roles in the development of human diseases [39]. The pathway-based analysis allows to determine a relationship between the genotype and phenotype, and further build up the molecular bridges between genetically related diseases via shared pathways. One advantage of our approach is that it identifies the pathways shared among RA-related diseases by gene enrichment analysis. Here, we highlight for the first time the involvement of a KEGG pathway related to CVD in RA, T1D, SLE and psoriasis. The other advantage of our approach is the possibility to discover the potential association among these autoimmune diseases using the association rules mining method. We found high similarity between RA and T1D as well as RA and SLE. These results suggested a strong relation among autoimmune diseases. Even as so, we should point out the limitations of this study in exploring common mechanisms between complex diseases. First, because the disease-associated gene dataset is used to identify disease risk pathways, while these genes are always subject to further research, therefore, we believe that only a small proportion of common pathways is identified. Second, current knowledge about pathways involved in complex diseases, is incomplete and fragmented, and part of this analysis that relies on knowledge mining suffers

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from this limitation. For the same reason, our association rule mining method for exploring disease relations, therefore, is just an attempt, it might cause some biases, such as correlation between RA and psoriasis can not yet be validated, or some of the identified associations between related diseases inevitably exist with statistically significant error. Notwithstanding these limitations, the biological significant overlap between the pathophysiological pathways demonstrates the potential similarity or correlation of genetic causes between RA and TSP.

7. 8.

9. 10.

CONCLUSIONS Downstream pathway study, by recognizing multiple genes, especially with regard to shared between diseases pathways can serve as a promising method to build biologically-oriented bridges for the association between diseases. In this study, knowledge mining results show that some of the shared pathways have pleiotropic actions on multiple disease phenotypes. Additionally, comparison of pathways implicated in different autoimmune diseases reveals the association of CVD and autoimmune diseases.

11. 12. 13.

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ACKNOWLEDGMENTS This work is supported by Beijing Natural Science Foundation (grant no. 7142015) and the Science Technology Development Project of Beijing Municipal Commission of Education (SQKM201210025008). CONFLICT OF INTERESTS The authors have no financial conflict of interests.

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REFERENCES 1. Li Y., Begovich A.B. 2009. Unraveling the genetics of complex diseases: Susceptibility genes for rheumatoid arthritis and psoriasis. Semin. Immunol. 21, 318–327. 2. van Driel M.A., Bruggeman J., Vriend G., Brunner H.G., Leunissen J.A. 2006. A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 14, 535–542. 3. Fehringer G., Liu G., Briollais L., Brennan P., Amos C.I., Spitz M.R., Bickeboller H., Wichmann H.E., Risch A., Hung R.J. 2012. Comparison of pathway analysis approaches using lung cancer GWAS data sets. PLoS ONE. 7, e31836. 4. Jin L., Zuo X.Y., Su W.Y., Zhao X.L., Yuan M.Q., Han L.Z., Zhao X., Chen Y.D., Rao S.Q. 2014. Pathway-based analysis tools for complex diseases: A review. Genom. Proteom. Bioinform. 12, 210–220. 5. Ramanan V.K., Shen L., Moore J.H., Saykin A.J. 2012. Pathway analysis of genomic data: Concepts, methods, and prospects for future development. Trends Genet. 28, 323–332. 6. Rappaport N., Nativ N., Stelzer G., Twik M., GuanGolan Y., Stein T.I., Bahir I., Belinky F., Morrey C.P., Safran M., Lancet D. 2013. MalaCards: An integrated

17. 18.

19.

20.

21.

compendium for diseases and their annotation. Database (Oxford). 2013, bat018. Goh K.I., Cusick M.E., Valle D., Childs B., Vidal M., Barabasi A.L. 2007. The human disease network. Proc. Natl. Acad. Sci. U. S. A. 104, 8685–8690. Dabrowska-Zamojcin E., Herczynska M.M., Pawlik A. 2013. Association between AIF-1 gene polymorphisms and response to rheumatoid arthritis treatment with sulphasalazine. Ann. Acad. Med. Stetin. 59, 39–42. Agrawal R., I. T., Swami A. 1993. Mining association rules between sets of items in large databases. SIGMOD. 22, 207–216. Li Y., Agarwal P. 2009. A pathway-based view of human diseases and disease relationships. PLoS ONE. 4, e4346. Elliott J.R., Manzi S. 2009. Cardiovascular risk assessment and treatment in systemic lupus erythematosus. Best Pract. Res. Clin. Rheumatol. 23, 481–494. Kanehisa M., Goto S. 2000. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. Liu G., Yao L., Liu J., Jiang Y., Ma G., Genetic, Environmental Risk for Alzheimer’s disease (GERAD1) Consortium, Chen Z., Zhao B., Li K. 2014. Cardiovascular disease contributes to Alzheimer’s disease: Evidence from large-scale genome-wide association studies. Neurobiol. Aging. 35, 786–792. Wang B., Sun S., Liu Z. 2014. Analysis of dysregulation of immune system in pancreatic cancer based on gene expression profile. Mol. Biol. Rep. 41, 4361–4367. Wang Y., Xu G., Chen W., Pan Q., Huang K., Pan J., Zhang W., Chen J. 2015. Detection of long-chain nonencoding RNA differential expression in non-small cell lung cancer by microarray analysis and preliminary verification. Mol. Med. Rep. 11, 1925–1932. Pham T., Gossec L., Constantin A., Pavy S., Bruckert E., Cantagrel A., Combe B., Flipo R. M., Goupille P., Le Loet X., Mariette X., Puechal X., Schaeverbeke T., Sibilia J., Tebib J., et al. 2006. Cardiovascular risk and rheumatoid arthritis: Clinical practice guidelines based on published evidence and expert opinion. Joint Bone Spine. 73, 379–387. Kaplan M.J. 2006. Cardiovascular disease in rheumatoid arthritis. Curr. Opin. Rheumatol. 18, 289–297. Magder L.S., Petri M. 2012. Incidence of and risk factors for adverse cardiovascular events among patients with systemic lupus erythematosus. Am. J. Epidemiol. 176, 708–719. Imazu M., Sumii K., Yamamoto H., Toyofuku M., Tadehara F., Okubo M., Yamakido M., Kohno N., Onaka A.T., Hawaii–Angeles–Hiroshima Study. 2002. Influence of type 2 diabetes mellitus on cardiovascular disease mortality: Findings from the Hawaii–Los Angeles–Hiroshima study. Diabetes Res. Clin. Pract. 57, 61–69. Erkens J.A., Herings R.M., Stolk R.P., Spoelstra J.A., Grobbee D.E., Leufkens H.G. 2002. Cardiovascular risk factors and diseases precede oral hypoglycaemic therapy in patients with type 2 diabetes mellitus. J. Clin. Epidemiol. 55, 345–349. Gordon-Dseagu V.L., Shelton N., Mindell J. 2014. Diabetes mellitus and mortality from all-causes, cancer, cardiovascular and respiratory disease: Evidence from the Health Survey for England and Scottish MOLECULAR BIOLOGY

Vol. 50

No. 3

2016

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22. 23.

24.

25. 26. 27.

28.

29.

30.

Health Survey cohorts. J. Diabet. Complicat. 28, 791– 797. Saboo B. 2015. Key elements of successful intensive therapy in patients with type 1 diabetes. Indian J. Endocrinol. Metab. 19, S44–S46. Ortega E., Amor A.J., Rojo-Martinez G., Castell C., Gimenez M., Conget I. 2015. Cardiovascular disease in patients with type 1 and type 2 diabetes in Spain. Med. Clin (Barc). 145 (6), 233–238. Khraishi M., Aslanov R., Rampakakis E., Pollock C., Sampalis J.S. 2014. Prevalence of cardiovascular risk factors in patients with psoriatic arthritis. Clin. Rheumatol. 33, 1495–1500. Torkamani A., Topol E.J., Schork N.J. 2008. Pathway analysis of seven common diseases assessed by genomewide association. Genomics. 92, 265–272. Zenewicz L.A., Abraham C., Flavell R.A., Cho J.H. 2010. Unraveling the genetics of autoimmunity. Cell. 140, 791–797. Chelala C., Duchatelet S., Joffret M.L., Bergholdt R., Dubois-Laforgue D., Ghandil P., Pociot F., CaillatZucman S., Timsit J., Julier C. 2007. PTPN22 R620W functional variant in type 1 diabetes and autoimmunity related traits. Diabetes. 56, 522–526. Plenge R.M., Padyukov L., Remmers E.F., Purcell S., Lee A.T., Karlson E.W., Wolfe F., Kastner D.L., Alfredsson L., Altshuler D., Gregersen P.K., Klareskog L., Rioux J.D. 2005. Replication of putative candidategene associations with rheumatoid arthritis in >4,000 samples from North America and Sweden: Association of susceptibility with PTPN22, CTLA4, and PADI4. Am. J. Hum. Genet. 77, 1044–1060. Zucker-Franklin D., Hooper W.C., Evatt B.L. 1992. Human lymphotropic retroviruses associated with mycosis fungoides: Evidence that human T-cell lymphotropic virus type II (HTLV-II) as well as HTLV-I may play a role in the disease. Blood. 80, 1537–1545. Raychaudhuri S., Remmers E.F., Lee A.T., Hackett R., Guiducci C., Burtt N.P., Gianniny L., Korman B.D., Padyukov L., Kurreeman F.A., Chang M., Catanese J.J., Ding B., Wong S., van der Helm-van Mil A.H., et al. 2008. Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nat. Genet. 40, 1216–1223.

MOLECULAR BIOLOGY

Vol. 50

No. 3

2016

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31. Criswell L.A., Pfeiffer K.A., Lum R.F., Gonzales B., Novitzke J., Kern M., Moser K.L., Begovich A.B., Carlton V.E., Li W., Lee A.T., Ortmann W., Behrens T.W., Gregersen P.K. 2005. Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: The PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 76, 561– 571. 32. Liao K.P., Gunnarsson M., Källberg H., Ding B., Plenge R.M., Padyukov L., Karlson E.W., Klareskog L., Askling J., Alfredsson L. 2009. A specific association exists between type 1 diabetes and anti-CCP positive rheumatoid arthritis. Arthritis Rheum. 60, 653–660. 33. Torfs C.P., King M.C., Huey B., Malmgren J., Grumet F.C. 1986. Genetic interrelationship between insulin-dependent diabetes mellitus, the autoimmune thyroid diseases, and rheumatoid arthritis. Am. J. Hum. Genet. 38, 170–187. 34. Lin J.P., Cash J.M., Doyle S.Z., Peden S., Kanik K., Amos C.I., Bale S.J., Wilder R.L. 1998. Familial clustering of rheumatoid arthritis with other autoimmune diseases. Hum. Genet. 103, 475–782. 35. Schaub M.A., Kaplow I.M., Sirota M., Do C.B., Butte A.J., Batzoglou S. 2009. A classifier-based approach to identify genetic similarities between diseases. Bioinformatics. 25, i21–i29. 36. The Wellcome Trust Case Control Consortium. 2007. Genome-wide association study of 14000 cases of seven common diseases and 3000 shared controls. Nature. 447, 661–678. 37. Eleftherohorinou H., Wright V., Hoggart C., Hartikainen A.L., Jarvelin M.R., Balding D., Coin L., Levin M. 2009. Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS ONE. 4, e8068. 38. Jessica A., Stanich J.D.C., Judith Whittum-Hudson Alan P. Hudson. 2009. Rheumatoid arthritis: Disease or syndrome? Open Access Rheumatol. Res. Rev. 1, 179– 192. 39. The International HapMap Consortium. 2005. A haplotype map of the human genome. Nature. 437, 1299– 1320.