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Interactive association between dietary fat and sex on CDH13 cg02263260 methylation

BMC Medical Genomics volume 14, Article number: 13 (2021) Cite this article

Abstract

Background

DNA methylation of Cadherin 13 (CDH13), a tumor suppressor gene is associated with gene repression and carcinogenesis. We determined the relation of dietary fat and sex with CDH13 cg02263260 methylation in Taiwanese adults.

Methods

Data of 870 eligible participants (430 men and 440 women) between 30 and 70 years were obtained from the Taiwan Biobank (TWB) database. The association of dietary fat and sex with CDH13 cg02263260 methylation was determined using multiple linear regression.

Results

The association between sex and cg02263260 methylation was significant: beta-coefficient (β) = 0.00532; 95% confidence interval (CI) = 0.00195–0.00868. Moreover, the interaction between sex and dietary fat on cg02263260 methylation was significant (P-value = 0.0145). After stratification by sex, the association of dietary fat with cg02263260 methylation was significant only in women. Specifically, high dietary fat was positively associated with cg02263260 methylation in women (β = 0.00597; 95% CI = 0.00061–0.01133) and the test for trend was significant (P-value = 0.0283).

Conclusion

High fat intake was significantly associated with higher cg02263260 methylation in women and the test for trend was significant. These findings suggest that the association of fat intake and CDH13 cg02263260 might vary by sex and CDH13 cg02263260 methylation levels in women might increase as fat intake increases.

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Background

Dietary habits are among the major modifiable factors for non-communicable diseases (NCDs) like cancer and cardiovascular diseases (CVDs), to mention just a few [1,2,3]. Unhealthy dietary habits exacerbate the risk of chronic diseases by inducing inflammation and enhancing the production of reactive oxygen species (ROS), which are a driving force for oxidative stress and DNA damage [4,5,6,7,8]. Saturated fats (like cheese, butter, fatty meat, cream, and lard) and unhealthy cooking methods (e.g., frying) increase the risk of colorectal cancer [9,10,11]. Cancer-causing lifestyle factors influence epigenetic modifications especially DNA methylation [12].

DNA methylation is a well-recognized epigenetic phenomenon characterized by the addition or removal of a methyl group (CH3) predominantly at the fifth position of cytosine in a CpG dinucleotide, forming 5-methylcytosine [13,14,15,16]. It mediates external effects on gene expressions and plays crucial roles in cellular development, differentiation, and pathogenesis [13,14,15,16,17,18]. DNA methylation marks possess diagnostic, prognostic, and therapeutic properties that make them potential pathological biomarkers [15]. Promoter methylation, in particular, is regarded as a molecular marker for most human malignancies because different genes exhibit distinct aberrant promoter methylation patterns in several malignancies [19,20,21,22].

Cadherin 13 (CDH13), also known as T-cadherin is a tumor suppressor gene located on chromosome 16 [23,24,25,26]. Altered methylation profiles and expression of CDH13 could influence oncogenesis [27]. For instance, abnormal CDH13 methylation profiles have been observed in lung [28,29,30,31,32,33], breast [31, 34,35,36], cervical [37, 38], colorectal [25, 39,40,41,42], pancreatic [43], gastric [44], liver [45], bladder [46], endometrial [46], prostate [47], and nasopharyngeal cancer [39]. CDH13 repression due to aberrant CDH13 promoter methylation has been associated with colorectal [39, 48], NSCLC [32], and pancreatic cancer [43]. However, re-expression of the gene has been associated with suppressed oncogenic processes like proliferation, invasiveness, and angiogenesis [26, 27].

Because CDH13 promoter methylation is common in many human tumors, it has been suggested as an early detection and prognostic marker for human malignancies [27, 46, 49], particularly colorectal [40, 41], breast [34], and non-small cell lung cancer (NSCLC) [29, 32, 33]. The methylation site, cg02263260, located in the promoter region of CDH13 might be important in assessing breast cancer risk and prognosis [49]. CDH13 is a receptor for high-molecular-weight adiponectin [23]. It should be noted that adiponectin is formed and secreted primarily by adipocytes (fat cells) in the adipose tissue [50].

More is yet to be explored in the domain of CDH13 methylation in relation to dietary fat. Moreover, the relationship between CDH13 promoter methylation and sex remains disputable. For instance, in NSCLC, CDH13 promoter methylation and sex were significantly associated [51]. On the contrary, in colorectal [39, 52] and pancreatic cancer [43], they were not significantly associated. Considering that serum adiponectin levels are associated with dietary habits [53], sex [54], and CDH13 [24], we presumed that fat intake and sex may be associated with CDH13 promoter methylation. Therefore, we aimed to determine the association of CDH13 cg02263260 methylation with dietary fat and sex in Taiwanese adults.

Methods

Study population and data source

Data, including CDH13 cg02263260 methylation, dietary fat, age, body mass index (BMI), body fat, waist-hip ratio (WHR), exercise, cigarette smoking, alcohol/coffee/tea intake, vegetarian diet, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) were retrieved from the Taiwan Biobank database. The TWB was established in 2005 to ameliorate chronic diseases through the identification of genetic, environmental, and lifestyle disease-causing factors plus the underlying mechanisms [55, 56]. Before enrolment into the TWB project, all volunteers met the eligibility criterion—Taiwanese between 30 and 70 years without a personal history of cancer [57]. All participants signed an informed consent form. Data were collected through questionnaires and physical/biochemical examinations [57]. The TWB dataset contained DNA methylation data of 1142 participants. However, our final sample size was 870 (430 men and 440 women) because we excluded participants who did not complete the questionnaires (n = 3) and those who had missing data (n = 183).

Dietary fat assessment

Dietary fat was assessed and categorized based on six questions relating to the frequency of consuming fat-rich food over the past month. The questions were: (1) Do you eat meat together with the skin? (2) Do you cook meat/fish with oil? (3) Do you fry vegetables before eating? (4) Do you eat noodles/rice with lard or fried vegetables? (5) Do you fry bean products (tofu, bean curd) before eating? (6) Do you spread cream, butter, or mayonnaise on bread before eating? The responses to these questions were presented on a five-point scale (1 = never, 2 = seldom, 3 = sometimes, 4 = frequently, and 5 = always). We derived the fat-intake score (ranging between 6 and 30) of each participant by summing the responses to all the six questions. Scores were grouped into three: ≤ 12, 13–18, and ≥ 18 corresponding to low, moderate, and high dietary fat, respectively.

DNA methylation profiling

DNA methylation was determined using pure DNA (i.e., DNA with optical density (OD) 260/280 ratio of 1.6–2.0) that were extracted from whole blood. The EZ DNA Methylation Kit (Zymo Research, CA, USA) was used for bisulfite treatment and conversion of DNA for methylation analysis. DNA methylation profiling was done using the Illumina Infinium MethylationEPIC BeadChip which covers the methylation status of over 850 thousand CpG sites [58]. Quality control of methylation data was performed as previously described [59, 60]. Methylation levels were quantified using beta-values (0–1) and estimated using the formula: M/(M + U), with M and U denoting methylated and unmethylated intensity, respectively.

Covariate assessment

Covariates (age, BMI, body fat, waist-hip ratio, exercise, cigarette smoking, alcohol drinking, coffee/tea intake, vegetarian diet, HDL-C, LDL-C, TG, and TC) were assessed as previously described [59,60,61]. Smoking and drinking habits were categorized into three groups (never, former and current). Exercise, coffee/tea intake, and vegetarian diet were grouped into two (yes and no). Body fat and waist-hip ratio were categorized as normal or abnormal using cutoff values: 25 and 30% for body fat of men and women, respectively and 0.9 and 0.85 for WHR of men and women, respectively [61].

Statistical analysis

Data were analyzed with the SAS 9.4 software (SAS Institute, Cary, NC, USA). Differences in basic characteristics of male and female participants were determined with chi-squared (χ2) test (for categorical variables e.g., dietary fat, exercise, tea intake, etc.) and t-test (for continuous variables e.g. cg02263260 methylation, age, BMI, etc.). The relationship of dietary fat and sex with CDH13 cg02263260 methylation and the interaction between dietary fat and sex on CDH13 cg02263260 methylation were determined through multiple linear regression analysis and adjustments were made for covariates (age, BMI, exercise, smoking, etc.). The regression results were reported as beta coefficients at 95% CIs. To improve statistical power and reduce type one error, we adjusted for cell-type heterogeneity using the method called Reference-Free Adjustment for Cell-Type composition (ReFACTor) whose details are described elsewhere [62].

Results

The demographic features of the study participants (n = 870) were stratified by sex as shown in Table 1. The male and female participants constituted 49.43 and 50.57%, respectively. The mean (± SE) ages were 49.36 ± 0.51 years for women and 49.76 ± 0.55 years for men and the methylation levels (beta-values) in men and women were 0.7325 ± 0.0013 and 0.7287 ± 0.0012, respectively. A majority of male and female participants fell in the moderate dietary fat category: 202 men (46.98%) and 205 women (46.59%). The CDH13 cg02263260 methylation levels, dietary fat, body mass index, body fat, cigarette smoking, alcohol/tea intake, vegetarian diet, high-density lipoprotein cholesterol, and triglycerides in men and women were significantly different at P < 0.05 (Table 1).

Table 1 Demographic characteristics of participants stratified by sex
Full size table

Table 2 presents the results of multiple linear regression showing the association between dietary fat and CDH13 cg02263260 methylation in all participants. CDH13 cg02263260 methylation was not significantly associated with dietary fat (regardless of the category). Nonetheless, a significant association existed between sex and cg02263260 methylation; with the female sex as the reference group, the male sex was significantly associated with higher CDH13 cg02263260 methylation levels (β = 0.00532, 95% CI = 0.00195–0.00868). Moreover, we found a significant interaction between dietary fat and sex on CDH13 cg02263260 methylation: P-value = 0.0145 (Table 3).

Table 2 Multiple linear regression showing the association between dietary fat and cg02263260 methylation in Taiwanese adults
Full size table
Table 3 Multiple linear regression showing the association between dietary fat and cg02263260 methylation in Taiwanese adults stratified by sex
Full size table

In women, moderate dietary fat and CDH13 cg02263260 methylation were not significantly associated: β = 0.00285; 95% CI =  − 0.00130–0.00700. On the contrary, high dietary fat was significantly associated with higher CDH13 cg02263260 methylation levels (β = 0.00597, 95% CI = 0.00061–0.01133). Furthermore, a significant trend (P-value = 0.0283) for fat intake was observed (Table 3). On the contrary, there was no significant association between dietary fat and CDH13 cg02263260 methylation in men (irrespective of dietary fat category). The β; 95% CI was − 0.00213; − 0.00734–0.00307 for moderate and − 0.00419; − 0.01011–0.00172 for high dietary fat. The test for trend was not also significant (Table 3).

After stratification by menopausal status, high dietary fat was positively associated with cg02263260 methylation in non-menopausal women (β = 0.00724; 95% CI = 0.00035–0.01413) and the trend test was significant: P-value = 0.0376 (Additional file 1: Table 1). After adjusting for adiponectin (ADIPOQ) cg16126291 methylation, the association between dietary fat and CDH13 cg02263260 methylation in men and women remained the same (Additional file 1: Tables 2, 3).

Discussion

In this study on Taiwan Biobank participants, CDH13 cg02263260 methylation was significantly associated with sex but not dietary fat. CDH13 promoter methylation was associated with sex in a study on NSCLC [51]. Conversely, it was not significantly associated with sex in studies on colorectal [39, 52] and pancreatic cancer [43]. Even though CDH13 cg02263260 methylation was not significantly associated with sex in the current study, the interactive association of sex and dietary fat on CDH13 cg02263260 methylation was significant. Stratified analyses yielded significant results only in female participants whose dietary fat was high. That is, compared to low dietary fat, high but not moderate dietary fat was significantly associated with higher levels of CDH13 cg02263260 methylation in women. Notwithstanding, a significant trend for fat intake was observed, inferring that CDH13 cg02263260 methylation levels in women might increase as fat intake increases.

To our knowledge, research on the association between fat intake and CDH13 is lacking and so, there is no available literature to directly compare our findings with. We cannot clearly state why CDH13 cg02263260 methylation and dietary fat intake were not significantly associated in the primary analysis (when partcipants were not stratified). However, the presence of significant results in only women after we stratified participants by sex suggests that sex might play a crucial role in fat-related CDH13 methylation. It is believed that after a high-fat meal, women store relatively high portions of fat [63]. High-fat intake increases the levels of endotoxins in the intestinal mucosa and disrupts the gut microbiota thereby inducing inflammation [8]. Endotoxin-induced proinflammatory responses were found to be greater in women than men and the levels of plasma IL-6 and TNF-alpha in women were significantly higher than in men [64]. The molecular mechanisms underlying the sex differences observed in the current study remain indescribable. It should be noted that after further stratification of the female participants by menopausal status, the association between dietary fat and CDH13 methylation was significant in only non-menopausal women with high fat intake. This implies that the role of hormones especially estrogen cannot be completely disregarded. In osteosarcoma cells, CDH13 expression was modulated by estradiol and progesterone [65]. Moreover, in breast cancer, CDH13 methylation was highly correlated with the down-regulation of estrogen and progesterone receptors [66]. Furthermore, circulating plasma levels of cortisol were significantly increased in women compared to men suggesting more proinflammatory responses to endotoxins in women [64].

CDH13 interacts with adiponectin in the smooth muscle and endothelial cells [24, 67] and high dietary fat reduces adiponectin levels [68]. Low expression of adiponectin is associated with an increased risk of colorectal cancer [69]. Besides, a high intake of fats, especially saturated fats increases the risk of colorectal cancer [9,10,11]. In our study, we adjusted for ADIPOQ cg16126291, a methylation site in the promoter region of the adiponectin gene which has been validated as being significantly associated with adipogenesis [70]. The observed relationships between fat intake and CDH13 in both men and women were not affected after such adjustments.

Since CDH13 is a tumor suppressor gene, its hypermethylation might result in gene repression as previously shown [27]. However, it was beyond the scope of this study to evaluate the association between CDH13 cg02263260 methylation and gene expression. This is a limitation of our study. Nonetheless, CDH13 down-regulation by aberrant methylation in the CpG island of its promoter is associated with colorectal [39, 48], NSCLC [32], and pancreatic cancer [43]. On the other hand, re-expression of CDH13 in most tumor cell lines inhibits tumor growth through enhanced susceptibility to apoptosis and subdued carcinogenic processes, including invasiveness, proliferation, and angiogenesis [26, 27]. Moreover, the up-regulation of CDH13 brings about an anti-apoptotic effect on the vascular endothelial cells [67] and enhances their migration and proliferation thereby elevating their survival [27]. Due to this, CDH13 is a potential therapeutic target for some cancers [27].

Conclusions

High fat intake was significantly associated with higher cg02263260 methylation in women and the test for trend was significant. These findings suggest that the association of fat intake with CDH13 cg02263260 might vary by sex and CDH13 cg02263260 methylation levels in women might increase as fat intake increases. As a tumor suppressor gene, CDH13 might repress gene expression when hypermethylated, possibly triggering tumorigenesis.

Availability of data and materials

The data that support the findings of this study are available from Taiwan Biobank but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from Professor Yung Po Liaw (Email address: liawyp@csmu.edu.tw, Tel: + 886424730022 ext. 12102; fax: + 886423248179) upon reasonable request and with permission of Taiwan Biobank.

Abbreviations

CDH13:

cadherin 13

β:

beta-coefficient

CI:

confidence interval

DNA:

deoxyribonucleic acid

NCDs:

non-communicable diseases

CVDs:

cardiovascular diseases

ROS:

reactive oxygen species

CH3 :

methyl group

CpG:

cytosine–phosphate–guanine

NSCLC:

non-small cell lung cancer

SE:

standard error

BMI:

body mass index

HDL-C:

high-density lipoprotein cholesterol

LDL-C:

low-density lipoprotein cholesterol

TG:

triglyceride

TC:

total cholesterol

TWB:

Taiwan Biobank

References

  1. Bollati V, Favero C, Albetti B, Tarantini L, Moroni A, Byun H-M, et al. Nutrients intake is associated with DNA methylation of candidate inflammatory genes in a population of obese subjects. Nutrients. 2014;6(10):4625–39.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. . WHO. World Health Organization healthy diet fact sheet 2018.

  3. Micha R, Shulkin ML, Penalvo JL, Khatibzadeh S, Singh GM, Rao M, et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE. 2017;12(4):e0175149.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. Cencioni C, Spallotta F, Martelli F, Valente S, Mai A, Zeiher AM, et al. Oxidative stress and epigenetic regulation in ageing and age-related diseases. Int J Mol Sci. 2013;14(9):17643–63.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. Lobo V, Patil A, Phatak A, Chandra N. Free radicals, antioxidants and functional foods: impact on human health. Pharmacogn Rev. 2010;4(8):118.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Vigna L, Novembrino C, De Giuseppe R, De Liso F, Sommaruga D, Agnelli G, et al. Nutritional and oxidative status in occupational obese subjects. Mediterr J Nutr Metab. 2011;4(1):69–74.

    Article  Google Scholar 

  7. Vassalle C, Vigna L, Bianchi S, Maffei S, Novembrino C, De Giuseppe R, et al. A biomarker of oxidative stress as a nontraditional risk factor in obese subjects. Biomark Med. 2013;7(4):633–9.

    CAS  PubMed  Article  Google Scholar 

  8. Kim K-A, Gu W, Lee I-A, Joh E-H, Kim D-H. High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice via the TLR4 signaling pathway. PLoS ONE. 2012;7(10):e47713.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Jarosz M, Sekuła W, Rychlik E. Trends in dietary patterns, alcohol intake, tobacco smoking, and colorectal cancer in Polish population in 1960–2008. BioMed Res Int. 2013;2013.

  10. Chan AT, Giovannucci EL. Primary prevention of colorectal cancer. Gastroenterology. 2010;138(6):2029-43.e10.

    CAS  PubMed  Article  Google Scholar 

  11. Joshi AD, Kim A, Lewinger JP, Ulrich CM, Potter JD, Cotterchio M, et al. Meat intake, cooking methods, dietary carcinogens, and colorectal cancer risk: findings from the Colorectal Cancer Family Registry. Cancer Med. 2015;4(6):936–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Yuasa Y. Epigenetics in molecular epidemiology of cancer: a new scope. Advances in genetics. 71: Elsevier; 2010. p. 211–35.

  13. Law JA, Jacobsen SE. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet. 2010;11(3):204–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38(1):23–38.

    CAS  PubMed  Article  Google Scholar 

  15. Yuan B-F. 5-methylcytosine and its derivatives. Advances in clinical chemistry. 67: Elsevier; 2014. p. 151–87.

  16. Jang HS, Shin WJ, Lee JE, Do JT. CpG and non-CpG methylation in epigenetic gene regulation and brain function. Genes. 2017;8(6):148.

    PubMed Central  Article  CAS  Google Scholar 

  17. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33(3):245–54.

    CAS  PubMed  Article  Google Scholar 

  18. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14(3):204–20.

    CAS  PubMed  Article  Google Scholar 

  19. Sidransky D. Emerging molecular markers of cancer. Nat Rev Cancer. 2002;2(3):210–9.

    CAS  PubMed  Article  Google Scholar 

  20. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002;3(6):415–28.

    CAS  Article  PubMed  Google Scholar 

  21. Rountree MR, Bachman KE, Herman JG, Baylin SB. DNA methylation, chromatin inheritance, and cancer. Oncogene. 2001;20(24):3156–65.

    CAS  PubMed  Article  Google Scholar 

  22. Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene. 2002;21(35):5427–40.

    CAS  Article  PubMed  Google Scholar 

  23. Hug C, Wang J, Ahmad NS, Bogan JS, Tsao T-S, Lodish HF. T-cadherin is a receptor for hexameric and high-molecular-weight forms of Acrp30/adiponectin. Proc Natl Acad Sci. 2004;101(28):10308–13.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  24. Jee SH, Sull JW, Lee J-E, Shin C, Park J, Kimm H, et al. Adiponectin concentrations: a genome-wide association study. Am J Hum Genet. 2010;87(4):545–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Park J, Kim I, Jung KJ, Kim S, Jee SH, Yoon SK. Gene–gene interaction analysis identifies a new genetic risk factor for colorectal cancer. J Biomed Sci. 2015;22(1):73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Berx G, Van Roy F. Involvement of members of the cadherin superfamily in cancer. Cold Spring Harbor Perspect Bio. 2009;1(6):a003129.

    Google Scholar 

  27. Andreeva AV, Kutuzov MA. Cadherin 13 in cancer. Genes Chromosom Cancer. 2010;49(9):775–90.

    CAS  PubMed  Google Scholar 

  28. Zhong Y-H, Peng H, Cheng H-Z, Wang P. Quantitative assessment of the diagnostic role of CDH13 promoter methylation in lung cancer. Asian Pac J Cancer Prev. 2015;16(3):1139–43.

    PubMed  Article  Google Scholar 

  29. Kim H, Kwon YM, Kim JS, Lee H, Park J-H, Shim YM, et al. Tumor-specific methylation in bronchial lavage for the early detection of non-small-cell lung cancer. J Clin Oncol. 2004;22(12):2363–70.

    CAS  PubMed  Article  Google Scholar 

  30. Hanabata T, Tsukuda K, Toyooka S, Yano M, Aoe M, Nagahiro I, et al. DNA methylation of multiple genes and clinicopathological relationship of non-small cell lung cancers. Oncol Rep. 2004;12(1):177–80.

    CAS  PubMed  Google Scholar 

  31. Toyooka KO, Toyooka S, Virmani AK, Sathyanarayana UG, Euhus DM, Gilcrease M, et al. Loss of expression and aberrant methylation of the CDH13 (H-cadherin) gene in breast and lung carcinomas. Can Res. 2001;61(11):4556–60.

    CAS  Google Scholar 

  32. Ulivi P, Zoli W, Calistri D, Fabbri F, Tesei A, Rosetti M, et al. p16INK4A and CDH13 hypermethylation in tumor and serum of non-small cell lung cancer patients. J Cell Physiol. 2006;206(3):611–5.

    CAS  PubMed  Article  Google Scholar 

  33. Zhang Y, Wang R, Song H, Huang G, Yi J, Zheng Y, et al. Methylation of multiple genes as a candidate biomarker in non-small cell lung cancer. Cancer Lett. 2011;303(1):21–8.

    CAS  PubMed  Article  Google Scholar 

  34. Yang J, Li L, Wang W, Chen Y, Wang S, Zhou Y. T-cadherin association with clinicopathological features and prognosis in axillary lymph node-positive breast cancer. Breast Cancer Res Treat. 2015;150(1):119–26.

    PubMed  Article  CAS  Google Scholar 

  35. Xu J, Shetty PB, Feng W, Chenault C, Bast RC, Issa J-PJ, et al. Methylation of HIN-1, RASSF1A, RIL and CDH13 in breast cancer is associated with clinical characteristics, but only RASSF1A methylation is associated with outcome. BMC Cancer. 2012;12(1):243.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Wang S, Dorsey TH, Terunuma A, Kittles RA, Ambs S, Kwabi-Addo B. Relationship between tumor DNA methylation status and patient characteristics in African-American and European-American women with breast cancer. PLoS ONE. 2012;7(5):e37928.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Widschwendter A, Ivarsson L, Blassnig A, Müller HM, Fiegl H, Wiedemair A, et al. CDH1 and CDH13 methylation in serum is an independent prognostic marker in cervical cancer patients. Int J Cancer. 2004;109(2):163–6.

    CAS  PubMed  Article  Google Scholar 

  38. Henken FE, Wilting SM, Overmeer RM, van Rietschoten JG, Nygren AO, Errami A, et al. Sequential gene promoter methylation during HPV-induced cervical carcinogenesis. Br J Cancer. 2007;97(10):1457–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Hibi K, Nakayama H, Kodera Y, Ito K, Akiyama S, Nakao A. CDH13 promoter region is specifically methylated in poorly differentiated colorectal cancer. Br J Cancer. 2004;90(5):1030–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Toyooka S, Toyooka KO, Harada K, Miyajima K, Makarla P, Sathyanarayana UG, et al. Aberrant methylation of the CDH13 (H-cadherin) promoter region in colorectal cancers and adenomas. Can Res. 2002;62(12):3382–6.

    CAS  Google Scholar 

  41. Duan B-S, Xie L-F, Wang Y. Aberrant methylation of T-cadherin can be a diagnostic biomarker for colorectal cancer. Cancer Genomics Proteomics. 2017;14(4):277–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Mahon G, Metzger B, Dicato M-A. Distribution of CDH13, CDH1, MGMT and PTEN promoter methylation and KRAS, BRAF and EGFR mutation in colorectal cancer. American Society of Clinical Oncology; 2016.

  43. Sakai M, Hibi K, Koshikawa K, Inoue S, Takeda S, Kaneko T, et al. Frequent promoter methylation and gene silencing of CDH13 in pancreatic cancer. Cancer Sci. 2004;95(7):588–91.

    CAS  PubMed  Article  Google Scholar 

  44. Wei B, Shi H, Lu X, Shi A, Cheng Y, Dong L. Association between the expression of T-cadherin and vascular endothelial growth factor and the prognosis of patients with gastric cancer. Mol Med Rep. 2015;12(2):2075–81.

    CAS  PubMed  Article  Google Scholar 

  45. Chan DW, Lee JM, Chan PC, Ng IO. Genetic and epigenetic inactivation of T-cadherin in human hepatocellular carcinoma cells. Int J Cancer. 2008;123(5):1043–52.

    CAS  PubMed  Article  Google Scholar 

  46. Chen F, Huang T, Ren Y, Wei J, Lou Z, Wang X, et al. Clinical significance of CDH13 promoter methylation as a biomarker for bladder cancer: a meta-analysis. BMC Urol. 2016;16(1):52.

    PubMed  PubMed Central  Article  Google Scholar 

  47. Maruyama R, Toyooka S, Toyooka KO, Virmani AK, Zöchbauer-Müller S, Farinas AJ, et al. Aberrant promoter methylation profile of prostate cancers and its relationship to clinicopathological features. Clin Cancer Res. 2002;8(2):514–9.

    CAS  PubMed  Google Scholar 

  48. Lofton-Day C, Model F, DeVos T, Tetzner R, Distler J, Schuster M, et al. DNA methylation biomarkers for blood-based colorectal cancer screening. Clin Chem. 2008;54(2):414–23.

    CAS  PubMed  Article  Google Scholar 

  49. Yang J, Niu H, Huang Y, Yang K. A systematic analysis of the relationship of CDH13 promoter methylation and breast cancer risk and prognosis. PLoS ONE. 2016;11(5):e0149185.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. Straub LG, Scherer PE. Metabolic messengers: adiponectin. Nat Metab. 2019;1(3):334–9.

    PubMed  PubMed Central  Article  Google Scholar 

  51. Kontic M, Stojsic J, Jovanovic D, Bunjevacki V, Ognjanovic S, Kuriger J, et al. Aberrant promoter methylation of CDH13 and MGMT Genes is associated with clinicopathologic characteristics of primary non-small-cell lung carcinoma. Clin Lung Cancer. 2012;13(4):297–303.

    CAS  PubMed  Article  Google Scholar 

  52. Ye M, Huang T, Li J, Zhou C, Yang P, Ni C, et al. Role of CDH13 promoter methylation in the carcinogenesis, progression, and prognosis of colorectal cancer: A systematic meta-analysis under PRISMA guidelines. Medicine. 2017;96(4):e5956.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Ostrowska L, Fiedorczuk J, Adamska E. Effect of diet and other factors on serum adiponectin concentrations in patients with type 2 diabetes. Roczniki Państwowego Zakładu Higieny. 2013;64(1):61–6.

    CAS  PubMed  Google Scholar 

  54. Tomono Y, Hiraishi C, Yoshida H. Age and sex differences in serum adiponectin and its association with lipoprotein fractions. Ann Clin Biochem. 2018;55(1):165–71.

    CAS  PubMed  Article  Google Scholar 

  55. Fan C-T, Lin J-C, Lee C-H. Taiwan Biobank: a project aiming to aid Taiwan’s transition into a biomedical island. Pharmacogenomics. 2008;9(2):235–46.

    PubMed  Article  Google Scholar 

  56. Biobank T. Purpose Of Taiwan Biobank 2015. https://www.twbiobank.org.tw/new_web_en/index.php.

  57. Biobank T. Participation program 2015. https://www.twbiobank.org.tw/new_web_en/join-flow-before.php.

  58. Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8(3):389–99.

    CAS  PubMed  Article  Google Scholar 

  59. Tantoh DM, Wu M-F, Ho C-C, Lung C-C, Lee K-J, Nfor ON, et al. SOX2 promoter hypermethylation in non-smoking Taiwanese adults residing in air pollution areas. Clin Epigenet. 2019;11(1):46.

    Article  Google Scholar 

  60. Tantoh DM, Lee K-J, Nfor ON, Liaw Y-C, Lin C, Chu H-W, et al. Methylation at cg05575921 of a smoking-related gene (AHRR) in non-smoking Taiwanese adults residing in areas with different PM 2.5 concentrations. Clin Epigenet. 2019;11(1):69.

    Article  CAS  Google Scholar 

  61. Hsu T-W, Tantoh DM, Lee K-J, Ndi ON, Lin L-Y, Chou M-C, et al. Genetic and non-genetic factor-adjusted association between coffee drinking and high-density lipoprotein cholesterol in Taiwanese adults: stratification by sex. Nutrients. 2019;11(5):1102.

    CAS  PubMed Central  Article  Google Scholar 

  62. Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods. 2016;13(5):443.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Votruba SB, Jensen MD. Sex-specific differences in leg fat uptake are revealed with a high-fat meal. Am J Physiol Endocrinol Metab. 2006;291(5):E1115–23.

    CAS  PubMed  Article  Google Scholar 

  64. Wegner A, Benson S, Rebernik L, Spreitzer I, Jäger M, Schedlowski M, et al. Sex differences in the pro-inflammatory cytokine response to endotoxin unfold in vivo but not ex vivo in healthy humans. Innate Immunity. 2017;23(5):432–9.

    CAS  PubMed  Article  Google Scholar 

  65. Bromhead C, Miller JH, McDonald FJ. Regulation of T-cadherin by hormones, glucocorticoid and EGF. Gene. 2006;374:58–67.

    CAS  PubMed  Article  Google Scholar 

  66. Feng W, Shen L, Wen S, Rosen DG, Jelinek J, Hu X, et al. Correlation between CpG methylation profiles and hormone receptor status in breast cancers. Breast Cancer Res. 2007;9(4):R57.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. Takeuchi T, Adachi Y, Ohtsuki Y, Furihata M. Adiponectin receptors, with special focus on the role of the third receptor, T-cadherin, in vascular disease. Med Mol Morphol. 2007;40(3):115–20.

    CAS  PubMed  Article  Google Scholar 

  68. Esposito K, Nappo F, Giugliano F, Di Palo C, Ciotola M, Barbieri M, et al. Meal modulation of circulating interleukin 18 and adiponectin concentrations in healthy subjects and in patients with type 2 diabetes mellitus. Am J Clin Nutr. 2003;78(6):1135–40.

    CAS  PubMed  Article  Google Scholar 

  69. Wei EK, Giovannucci E, Fuchs CS, Willett WC, Mantzoros CS. Low plasma adiponectin levels and risk of colorectal cancer in men: a prospective study. J Natl Cancer Inst. 2005;97(22):1688–94.

    CAS  PubMed  Article  Google Scholar 

  70. Broholm C, Olsson AH, Perfilyev A, Gillberg L, Hansen NS, Ali A, et al. Human adipogenesis is associated with genome-wide DNA methylation and gene-expression changes. Epigenomics. 2016;8(12):1601–17.

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

We appreciate the Ministry of Science and Technology (MOST), Taiwan for partially funding this work.

Funding

The Ministry of Science and Technology (MOST), Taiwan partially funded this work (MOST 107-2627-M-040-002, 108-2621-M-040-001, 106-EPA-F-016-001, and 107-EPA-F-017-002).

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Authors and Affiliations

  1. Institute of Medicine, Chung Shan Medical University, Taichung City, 40201, Taiwan

    Bei-Hao Shiu & Ming-Chih Chou

  2. Division of Colon-Rectal Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung City, 40201, Taiwan

    Bei-Hao Shiu & Chi-Chou Huang

  3. Department of Public Health and Institute of Public Health, Chung Shan Medical University, No. 110 Sec. 1 Jianguo N. Road, Taichung City, 40201, Taiwan

    Wen-Yu Lu, Disline Manli Tantoh, Oswald Ndi Nfor & Yung-Po Liaw

  4. Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, 40201, Taiwan

    Disline Manli Tantoh & Yung-Po Liaw

  5. School of Medicine, Chung Shan Medical University, No. 110 Sec. 1 Jianguo N. Road, Taichung City, 40201, Taiwan

    Chi-Chou Huang

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  1. Bei-Hao Shiu
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  2. Wen-Yu Lu
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Contributions

Conceptualization, B-HS, W-YL, DMT, M-CC, ONN, C-CH and Y-PL; Formal analysis, W-YL and Y-PL; Methodology, B-HS, W-YL, DMT, M-CC, ONN, C-CH and Y-PL; Supervision, C-CH and Y-PL; Validation, B-HS, W-YL, DMT, M-CC, ONN, C-CH and Y-PL; Writing—original draft, B-HS and DMT; Writing—review and editing, B-HS, W-YL, DMT, M-CC, ONN, C-CH and Y-PL. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Chi-Chou Huang or Yung-Po Liaw.

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Ethics approval and consent to participate

All participants signed an informed consent form. The Chung Shan Medical University Institutional Review Board (CS2-17070) granted ethical approval for this study.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

Association between dietary fat and cg02263260 methylation in Taiwanese adults based on menopausal status and adjustments for ADIPOQ cg16126291 methylation.

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Shiu, BH., Lu, WY., Tantoh, D.M. et al. Interactive association between dietary fat and sex on CDH13 cg02263260 methylation. BMC Med Genomics 14, 13 (2021). https://doi.org/10.1186/s12920-020-00858-y

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Keywords

  • Epigenetics
  • DNA methylation
  • Cadherin
  • cg02263260
  • Sex
  • Fat intake

BMC Medical Genomics

ISSN: 1755-8794

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