Abstract
Obesity has been reported to be associated with many diseases. However, common obesity-induced biological processes have not been evaluated across these diseases. We identified genes associated with obesity and obesity-related diseases, and used them to construct protein‒protein interaction networks. We also analyzed gene ontology (GO) in those genes overlapping between obesity and disease. Our work identifies gene modules common to obesity and obesity-related diseases, which can provide a basis for understanding the process of how obesity induces disease.
Keywords: Obesity, Obesity-related diseases, CAD, Diabetes, Hypertension, Breast cancer, Polycystic ovarian syndrome, Kidney cancer
Obesity has been reported to be associated with many diseases. However, common obesity-induced biological processes have not been evaluated across these diseases. We identified genes associated with obesity and obesity-related diseases, and used them to construct protein‒protein interaction networks. We also analyzed gene ontology (GO) in those genes overlapping between obesity and disease. Our work identifies gene modules common to obesity and obesity-related diseases, which can provide a basis for understanding the process of how obesity induces disease.
Obesity is a global epidemic recognized as a public health problem that influences multiple systems. Obesity has been reported to be associated with many diseases. Studies of molecular mechanisms show that obesity is tightly linked with diseases such as cardiovascular events (Rogge et al., 2013; Eckel et al., 2016), diabetes (Hwang et al., 2016; Vilarrasa et al., 2017), gynecological disease (Gateva and Kamenov, 2012; Gateva et al., 2013), and cancer (Gao et al., 2016; Nagrani et al., 2016).
Although a strong association between obesity and these diseases is apparent, it is not clear whether obesity-induced biological processes are common across these diseases. Therefore, this research selected six obesity-related diseases: coronary artery disease (CAD), diabetes, hypertension, breast cancer, polycystic ovarian syndrome (PCOS), and kidney cancer, and then used genes from six obesity-related disease databases and one obesity database to construct networks to explore the relationship between these diseases. CAD is the most common type of cardiovascular disease, which has many common risk factors including obesity, hypertension, diabetes, family history, lack of exercise, stress, and high blood lipids (Mehta et al., 2015). Obesity is associated with about 20% of CAD cases (Kivimäki et al., 2012). Diabetes, a group of metabolic diseases, can double the risk of cardiovascular disease (Sarwar et al., 2010). Long-term hypertension increases the risk of CAD, stroke, heart failure, and chronic kidney disease (Lackland and Weber, 2015). Many reports have shown that hypertension can be influenced by both genetic and environmental factors including high salt intake, lack of exercise, obesity, and depression (Poulter et al., 2015). Breast cancer occurs in breast tissue. The risk factors for breast cancer include smoking, lack of physical exercise, a high-fat diet, drinking alcohol, and obesity-related high cholesterol levels (Boffetta et al., 2006; Blackburn and Wang, 2007; Johnson et al., 2011; Kaiser, 2013). PCOS is a hormonal problem that occurs in 5% to 10% of women and includes signs and symptoms such as menstrual disorders, infertility, high levels of masculinizing hormones and metabolic syndrome (Nafiye et al., 2010). PCOS is associated with obesity and there is some evidence that weight loss can restore normal ovulation (Moran et al., 2013). Kidney cancer has also been linked to adult obesity and hypertension (Wilson and Cho, 2016) and the incidence rate of this type of cancer is higher in men than in women (Hancock and Georgiades, 2016).
Our research identifies gene modules common to obesity and obesity-related diseases that can provide a basis for understanding the processes by which obesity induces disease. Our research found that the six obesity-related disease gene sets included 2499 genes (summarized in Table 1), of which some overlapped between sets. The obesity gene set included 835 genes (Table 1). A total of 3275 disease or obesity genes were found in the STRING 10.0 database (Table 1). Table 2 lists the genes that were present in not less than three gene sets. Five genes in the obesity gene set were shown to overlap: estrogen receptor 1 (ESR1), phosphatase and tensin homolog (PTEN), serine/threonine kinase 1 (AKT1), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), and E1A binding protein p300 (EP300). Although no genes were present in all six disease sets, ten genes, including five obesity genes, were present in not less than three obesity-related disease gene sets.
Table 1.
Genes in the gene sets and networks
| Set name | Gene number | Seed gene number (in STRING 10.0) |
| Coronary artery disease (CAD) | 604 | 599 |
| Diabetes | 703 | 682 |
| Hypertension | 837 | 828 |
| Breast cancer | 80 | 78 |
| Polycystic ovary syndrome (PCOS) | 208 | 195 |
| Kidney cancer | 67 | 67 |
| Obesity | 835 | 826 |
Table 2.
Genes present in not less than three obesity-related diseases
| Gene symbol | Disease count |
| ESR1 | 5 |
| PTEN | 4 |
| AKT1 | 3 |
| PIK3R1 | 3 |
| CDKN2A | 3 |
| EP300 | 3 |
| CHEK2 | 3 |
| RYR2 | 3 |
| CASP8 | 3 |
| EPHX1 | 3 |
ESR1: estrogen receptor 1; PTEN: phosphatase and tensin homolog; AKT1: serine/threonine kinase 1; PIK3R1: phosphoinositide-3-kinase regulatory subunit 1; CDKN2A: cyclin-dependent kinase inhibitor 2A; EP300: E1A binding protein p300; CHEK2: checkpoint kinase 2; RYR2: ryanodine receptor 2; CASP8: caspase 8; EPHX1: epoxide hydrolase 1
In this study we constructed six obesity-related disease gene networks. Table 3 shows the average degrees of the six disease gene sets. With thresholds of betweenness centrality (BC) >0.05 and degrees above the average value of each network (P<0.05), a total of 31 genes were identified as hub genes for six diseases, of which 17 were included in the obesity gene set, such as ESR1, coagulation factor II (F2), nuclear factor κB subunit 1 (NFKB1), prostaglandin-endoperoxide synthase 2 (PTGS2), interleukin 6 (IL6), peroxisome proliferator activated receptor α (PPARA), tumor protein p53 (TP53), Jun proto-oncogene (JUN), AKT1, insulin (INS), EP300, breast cancer 1 (BRCA1), vascular endothelial growth factor A (VEGFA), insulin like growth factor 1 (IGF1), transforming growth factor β 1 (TGFB1), proopiomelanocortin (POMC), and PTEN. Genes ESR1, SRC proto-oncogene (SRC), IL6, TP53, JUN, INS, and AKT1 were hub genes for more than one disease. Because most hub genes are included in the obesity gene set, obesity may affect the occurrence and development of these obesity-related diseases.
Table 3.
Hub genes in six obesity-related disease/system networks
| Network name | Seed node average degree | Hub gene |
| Coronary artery disease (CAD) | 11.03 | ESR1, F2, NFKB1, SRC, PTGS2, IL6, PPARA, TP53 |
| Diabetes | 13.17 | TP53, JUN, AKT1, INS |
| Hypertension | 17.71 | SRC, JUN, AKT1 |
| Breast cancer | 4.28 | RUNX1, PIK3CA, EP300, BRCA1, SMARCD1, SMAD4, AKT1, TP53 |
| Polycystic ovary syndrome (PCOS) | 8.65 | CYP17A1, AR, BMP2, VEGFA, IGF1, IL6, TGFB1, ESR1, AKT1, POMC, JUN, INS |
| Kidney cancer | 1.01 | TSC2, MTOR, PTEN, TP53, SMARCB1, ERCC2, KDM6A |
To explore the relationship between obesity and obesity-related diseases, the nodes of the six diseases and obesity were compared. The diabetes gene set includes 325 members of the obesity gene set, the hypertension gene set includes 283, the CAD gene set includes 234, the breast cancer gene set includes 15, the PCOS gene set includes 5, and the kidney cancer gene set includes 4 (Table S1).
GO enrichment analyzed using DAVID software revealed that genes overlapping both obesity and diabetes were significantly enriched in the biological processes of response to organic substances (GO: 0010033), response to hormonal stimuli (GO:0009725), response to endogenous stimuli (GO:0009719), homeostatic processes (GO:0042592), and chemical homeostasis (GO:0048878) (Table S2). The cellular component and molecular function cluster of the genes that overlap both obesity and diabetes are shown in Table S2. The biological process cluster, cellular component cluster, and molecular function cluster of the genes that overlap both obesity and hypertension are shown in Table S3. The biological process cluster, cellular component cluster, and molecular function cluster of the genes that overlap both obesity and CAD are shown in Table S4. The biological process cluster and cellular component cluster of the genes that overlap both obesity and breast cancer are shown in Table S5.
Obesity-related diseases are influenced by genes and the environment, so these pathway clusters could indicate specific potential targets for disease research. As shown in Tables S2,S3,S4, cellular component clusters showed that the genes overlapping the diabetes, hypertension or CAD diseases and the obesity gene set are located in extracellular spaces and regions, whereas those overlapping both breast cancer and the obesity gene set are located in the nucleoplasm and cytosol. The biological processes (S2,S3,S4,S5) provide candidate targets for research into disease mechanisms, such as processes related to responses to organic substances, hormonal stimuli, and endogenous stimuli. Moreover, clusters enriched in molecular functions, such as hormone activity, identical protein binding, and cytokine activity, provide potential drug targets (Tables S2,S3,S4,S5). These results imply that some obesity genes affect different diseases in the common GO pathway.
In the present study, we investigated the relationship between genes associated with obesity and obesity-related diseases. We identified hub genes included in diseases. These hubs may play important regulatory roles in these obesity-related diseases. For example, ESR1, an essential member of the nuclear hormone receptor super family, plays significant roles in vascular wall physiology and function (Gao et al., 2014). ESR1 is also involved in pathological processes including breast cancer, endometrial cancer, and diabetes (Jiang et al., 2015). Single-nucleotide polymorphisms in the ESR1 gene were related to hypertension, CAD, and diabetes (Jiang et al., 2015). Previous studies showed that ESR1 polymorphisms PvuII (rs2234693T>C) and XbaI (rs9340799A>G) predicted the risk of CVD (Jiang et al., 2015). In hypogonadal men, the rs2207396 variant in ESR1 gene was strongly correlated with diabetes susceptibility (Linnér et al., 2013). Numerous studies have shown that PvuII polymorphism in ESR1 gene was a risk factor for breast cancer (Zhang et al., 2015). AKT1 has been functionally regulated by many pathologies such as cancer and diabetes. AKT1 regulates insulin-induced glucose uptake by two phosphorylation events at threonine 308 (T308) and serine 473 (S473) (Kumar et al., 2007). Recent reports have also showed that overexpression of microRNA-409-3p induced downregulation of AKT1 protein to inhibit breast cancer cell proliferation (Zhang et al., 2016).
In conclusion, this research identified genes associated with obesity and obesity-related diseases, and then used them to construct protein–protein interaction networks. This research identified hub genes including most obesity genes. A total of 31 hub genes were identified from six networks as potential key genes, of which 17 were included in the obesity gene set. Genes identified as common between gene sets included 325 between obesity and diabetes, 283 between obesity and hypertension, 234 between obesity and CAD, 15 between obesity and breast cancer, 5 between obesity and PCOS, and 4 between obesity and kidney cancer. This research also analyzed GO in those genes overlapping both obesity and disease. The common genes were enriched in some of the same GO functional clusters, which indicates that different obesity-related diseases share some common GO pathway clusters that may be influenced by the environment of obesity. The analysis of these pathways could identify the mechanisms by which obesity induces disease, but additional studies are required to confirm these results in humans.
Materials and methods
Gene sets of obesity-related disease
Genes from databases of obesity-related diseases were obtained and used for network analyses. Genes from the CAD gene database (Liu et al., 2011) were selected to form gene sets for CAD. Genes from the T-HOD database (Dai et al., 2013) were selected to form gene sets for diabetes and hypertension. Genes from the NCG5.0 database (An et al., 2016) were selected to form gene sets for breast cancer, PCOS, and kidney cancer. Human obesity genes in the T-HOD (Dai et al., 2013) database were selected to form a gene set for fatty metabolism. Overlaps were compared among the gene sets of obesity-related diseases.
Protein–protein interaction networks
The STRING 10.0 (Snel et al., 2000) database lists known and predicted protein–protein interactions based on their confidence scores. Genes in obesity-related disease gene sets or obesity datasets were used to construct interaction networks using STRING 10.0 with the highest confidence (score >0.9) and the genes found in the interaction networks were considered to be seed nodes. All networks were visualized by Cytoscape v3.4.0 (Kohl et al., 2011) software and calculated using the Network Analyzer tool (Assenov et al., 2008) based on parameters of BC and node degree. BC indicates the number of the shortest paths that pass one node. Degree reflects the number of interactions of one node. Nodes with thresholds of BC >0.05 and degrees above the average value of each network were considered to be hub genes.
Common gene ontology functional clusters
To examine whether biological processes are common between different obesity-related diseases and obesity, the DAVID (Sherman et al., 2007) gene annotation tool was used to analyze GO enrichment in the genes that overlap both obesity and obesity-related diseases. Values of P<0.05 and false discovery rate <0.05 were used as cutoff criteria.
List of electronic supplementary materials
Overlapped genes between obesity and obesity-related diseases
GO pathway clusters associated with common genes among obesity gene set and diabetes
GO pathway clusters associated with common genes among obesity gene set and hypertension
GO pathway clusters associated with common genes among obesity gene set and CAD
GO pathway clusters associated with common genes among obesity gene set and breast cancer
Footnotes
Project supported by the Key Project of Hebei North University (No. 120177) and the Key Project of Medical Science Research in Hebei Province (No. 20150061), China
Electronic supplementary materials: The online version of this article (http://dx.doi.org/10.1631/jzus.B1600454) contains supplementary materials, which are available to authorized users
Compliance with ethics guidelines: Li-ning SU, Yan-bing WANG, Chun-guang WNAG, and Hui-ping WEI declare that they have no conflict of interest.
This article does not contain any studies with human or animal subjects performed by any of the authors.
References
- 1.An O, Dall'Olio GM, Mourikis TP, et al. NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings. Nucleic Acids Res. 2016;44(D1):D992–D999. doi: 10.1093/nar/gkv1123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Assenov Y, Ramírez F, Schelhorn SE, et al. Computing topological parameters of biological networks. Bioinformatics. 2008;24(2):282–284. doi: 10.1093/bioinformatics/btm554. [DOI] [PubMed] [Google Scholar]
- 3.Blackburn GL, Wang KA. Dietary fat reduction and breast cancer outcome: results from the Women’s Intervention Nutrition Study (WINS) Am J Clin Nutr. 2007;86(3):878S–881S. doi: 10.1093/ajcn/86.3.878S. [DOI] [PubMed] [Google Scholar]
- 4.Boffetta P, Hashibe M, la Vecchia C, et al. The burden of cancer attributable to alcohol drinking. Int J Cancer. 2006;119(4):884–887. doi: 10.1002/ijc.21903. [DOI] [PubMed] [Google Scholar]
- 5.Dai HJ, Wu JC, Tsai RT, et al. T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database. 2013;2013:bas061. doi: 10.1093/database/bas061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Eckel N, Meidtner K, Kalle-Uhlmann T, et al. Metabolically healthy obesity and cardiovascular events: a systematic review and meta-analysis. Eur J Prev Cardiol. 2016;23(9):956–966. doi: 10.1177/2047487315623884. [DOI] [PubMed] [Google Scholar]
- 7.Gao HH, Gao LB, Wen JM. Genetic polymorphisms in the ESR1 gene and cerebral infarction risk: a meta-analysis. DNA Cell Biol. 2014;33(9):605–615. doi: 10.1089/dna.2013.2270. [DOI] [PubMed] [Google Scholar]
- 8.Gao Y, Huang Y, Song F, et al. Urban-rural disparity of overweight/obesity distribution and its potential trend with breast cancer among Chinese women. Oncotarget. 2016;7(35):56608–56618. doi: 10.18632/oncotarget.10968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gateva AT, Kamenov ZA. Markers of visceral obesity and cardiovascular risk in patients with polycystic ovarian syndrome. Eur J Obstet Gynecol Reprod Biol. 2012;164(2):161–166. doi: 10.1016/j.ejogrb.2012.05.037. [DOI] [PubMed] [Google Scholar]
- 10.Gateva A, Kamenov Z, Tsakova A. NT-proBNP levels in premenopausal women with polycystic ovarian syndrome and/or obesity. J Obstet Gynaecol. 2013;33(8):868–872. doi: 10.3109/01443615.2013.813912. [DOI] [PubMed] [Google Scholar]
- 11.Hancock SB, Georgiades CS. Kidney cancer. Cancer J. 2016;22(6):387–392. doi: 10.1097/PPO.0000000000000225. [DOI] [PubMed] [Google Scholar]
- 12.Hwang JY, Lee HJ, Go MJ, et al. An integrative study identifies KCNC2 as a novel predisposing factor for childhood obesity and the risk of diabetes in the Korean population. Sci Rep. 2016;6:33043. doi: 10.1038/srep33043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jiang N, Yang G, Peng CL. ESR1 gene polymorphisms PvuII (rs2234693T>C) and XbaI (rs9340799A>G) may not be directly correlated with cardiovascular disease risk. Genet Mol Res. 2015;14(4):13932–13944. doi: 10.4238/2015.October.29.14. [DOI] [PubMed] [Google Scholar]
- 14.Johnson KC, Miller AB, Collishaw NE, et al. Active smoking and secondhand smoke increase breast cancer risk: the report of the Canadian Expert Panel on Tobacco Smoke and Breast Cancer Risk (2009) Tob Control. 2011;20(1):e2. doi: 10.1136/tc.2010.035931. [DOI] [PubMed] [Google Scholar]
- 15.Kaiser J. Cholesterol forges link between obesity and breast cancer. Science. 2013;342(6162):1028. doi: 10.1126/science.342.6162.1028. [DOI] [PubMed] [Google Scholar]
- 16.Kivimäki PM, Nyberg ST, Batty GD, et al. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet. 2012;380(9852):1491–1497. doi: 10.1016/S0140-6736(12)60994-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. In: Hamacher M, Eisenacher M, Stephan C, editors. Data Mining in Proteomics.Methods in Molecular Biology (Methods and Protocols), Vol. 696. Humana Press; 2011. pp. 291–303. [DOI] [PubMed] [Google Scholar]
- 18.Kumar N, Afeyan R, Sheppard S, et al. Quantitative analysis of Akt phosphorylation and activity in response to EGF and insulin treatment. Biochem Biophys Res Commun. 2007;354(1):14–20. doi: 10.1016/j.bbrc.2006.12.188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lackland DT, Weber MA. Global burden of cardiovascular disease and stroke: hypertension at the core. Can J Cardiol. 2015;31(5):569–571. doi: 10.1016/j.cjca.2015.01.009. [DOI] [PubMed] [Google Scholar]
- 20.Linnér C, Svartberg J, Giwercman A, et al. Estrogen receptor alpha single nucleotide polymorphism as predictor of diabetes type 2 risk in hypogonadal men. Aging Male. 2013;16(2):52–57. doi: 10.3109/13685538.2013.772134. [DOI] [PubMed] [Google Scholar]
- 21.Liu H, Liu W, Liao Y, et al. CADgene: a comprehensive database for coronary artery disease genes. Nucleic Acids Res. 2011;39(suppl_1):D991–D996. doi: 10.1093/nar/gkq1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Mehta PK, Wei J, Wenger NK. Ischemic heart disease in women: a focus on risk factors. Trends Cardiovasc Med. 2015;25(2):140–151. doi: 10.1016/j.tcm.2014.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Moran LJ, Ko H, Misso M, et al. Dietary composition in the treatment of polycystic ovary syndrome: a systematic review to inform evidence-based guidelines. Hum Reprod Update. 2013;19(5):432. doi: 10.1093/humupd/dmt015. [DOI] [PubMed] [Google Scholar]
- 24.Nafiye Y, Sevtap K, Muammer D, et al. The effect of serum and intrafollicular insulin resistance parameters and homocysteine levels of nonobese, nonhyperandrogenemic polycystic ovary syndrome patients on in vitro fertilization outcome. Fertil Steril. 2010;93(6):1864–1869. doi: 10.1016/j.fertnstert.2008.12.024. [DOI] [PubMed] [Google Scholar]
- 25.Nagrani R, Mhatre S, Rajaraman P, et al. Central obesity increases risk of breast cancer irrespective of menopausal and hormonal receptor status in women of South Asian Ethnicity. Eur J Cancer. 2016;66:153–161. doi: 10.1016/j.ejca.2016.07.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Poulter NR, Prabhakaran D, Caulfield M. Hypertension. Lancet. 2015;386(9995):801–812. doi: 10.1016/s0140-6736(14)61468-9. [DOI] [PubMed] [Google Scholar]
- 27.Rogge BP, Cramariuc D, Lønnebakken MT, et al. Effect of overweight and obesity on cardiovascular events in asymptomatic aortic stenosis: a SEAS substudy (Simvastatin Ezetimibe in Aortic Stenosis) J Am Coll Cardiol. 2013;62(18):1683–1690. doi: 10.1016/j.jacc.2013.04.081. [DOI] [PubMed] [Google Scholar]
- 28.Sarwar N, Gao P, Kondapally Seshasai SR, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–2222. doi: 10.1016/S0140-6736(10)60484-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sherman BT, Huang DW, Tan Q, et al. DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics. 2007;8:426. doi: 10.1186/1471-2105-8-426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Snel B, Lehmann G, Bork P, et al. STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 2000;28(18):3442–3444. doi: 10.1093/nar/28.18.3442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Vilarrasa N, Rubio MA, Miñambres I, et al. Long-term outcomes in patients with morbid obesity and type 1 diabetes undergoing bariatric surgery. Obes Surg. 2017;27(4):856–863. doi: 10.1007/s11695-016-2390-y. [DOI] [PubMed] [Google Scholar]
- 32.Wilson KM, Cho E. Obesity and kidney cancer. In: Pischon T, Nimptsch K, editors. Obesity and Cancer. Recent Results in Cancer Research, Vol. 208. Springer, Cham; 2016. pp. 81–93. [DOI] [PubMed] [Google Scholar]
- 33.Zhang G, Liu Z, Xu H, et al. miR-409-3p suppresses breast cancer cell growth and invasion by targeting Akt1. Biochem Biophys Res Commun. 2016;469(2):189–195. doi: 10.1016/j.bbrc.2015.11.099. [DOI] [PubMed] [Google Scholar]
- 34.Zhang Y, Zhang M, Yuan X, et al. Association between ESR1 PvuII, XbaI, and P325P polymorphisms and breast cancer susceptibility: a meta-analysis. Med Sci Monit. 2015;21:2986–2996. doi: 10.12659/MSM.894010. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Overlapped genes between obesity and obesity-related diseases
GO pathway clusters associated with common genes among obesity gene set and diabetes
GO pathway clusters associated with common genes among obesity gene set and hypertension
GO pathway clusters associated with common genes among obesity gene set and CAD
GO pathway clusters associated with common genes among obesity gene set and breast cancer