To examine single nucleotide polymorphism (SNPs) in MUC16 (CA125) and MUC1 (CA15.3) in relation to ovarian cancer risk and survival.
We genotyped germline variants of MUC16 (rs2547065, rs1559168, rs12984471, rs2121133) and MUC1 (rs2070803, rs4072037, rs1045253) using samples collected from 758 ovarian cancer cases and 788 controls enrolled in the New England Case-Control Study between 2003 and 2008. We calculated age-adjusted odds ratios (OR) and 95% confidence intervals (CIs) for disease risk using unconditional and polytomous logistic regression and hazard ratios (HR) for survival using Cox proportional hazard ratios. In a subset of cases, we compared log-normalized CA125 values by genotype using generalized linear models.
Cases homozygous for the variant allele of MUC16 SNP, rs12984471, had poorer overall survival (log-rank p = 0.03) and higher CA125 levels, especially cases over age 65 (p = 0.01). For MUC1 SNP, rs4072037, women homozygous for the G variant had a non-significantly decreased risk for serous invasive types but elevated risk for serous borderline tumors, mucinous borderline and invasive tumors, and endometrioid tumors. Women with the variant allele of MUC16 SNP, rs2547065, especially those who were homozygous had an elevated risk for ovarian cancer; but this association was not confirmed in an independent dataset.
This targeted screen of seven polymorphisms of MUC16 and MUC1 genes failed to identify and confirm effects on ovarian cancer risk overall. However, there may be effects of MUC16 rs12984471 on survival and MUC1 rs4072037 on risk for histologic types of ovarian cancer other than invasive serous. Further study is warranted.
Citation: Williams KA, Terry KL, Tworoger SS, Vitonis AF, Titus LJ, Cramer DW (2014) Polymorphisms of MUC16 (CA125) and MUC1 (CA15.3) in Relation to Ovarian Cancer Risk and Survival. PLoS ONE 9(2): e88334. https://doi.org/10.1371/journal.pone.0088334
Editor: Surinder K. Batra, University of Nebraska Medical Center, United States of America
Received: September 18, 2013; Accepted: January 6, 2014; Published: February 13, 2014
Copyright: © 2014 Williams et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Work was supported by NIH grant numbers R01CA054419, P50CA105009, R01CA49449, R01CA67272, R01CA50383, UM1 CA176726, and P01CA87969; Department of Defense grant number W81XWH-10-1-0280 and the Scholars in Medicine Office at Harvard Medical School and the Department of Obstetrics and Gynecology at Brigham and Women’s Hospital. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The tethered human mucins (MUC) are a family of large, heavily glycosylated transmembrane proteins that have a diverse range of functions . CA125, or MUC16, is the largest glycoprotein of the mucin family, and is normally expressed in the epithelial lining of various tissues, especially that of the female reproductive tract . CA125 is elevated in the serum of about 82% of ovarian cancer patients and is used to predict recurrence , . CA15.3, or MUC1, also is expressed in the epithelial lining of various tissues, exhibiting strong expression in the mammary gland and the female reproductive tract during pregnancy and lactation. CA15.3 is over-expressed in a wide variety of cancers, including breast and ovarian , . Although these two mucins are best known as tumor markers, evidence suggests that they may play a role in cancer metastasis, tumor growth and survival, inhibition of immune response, and prognosis , , .
Several studies have examined genetic variation in genes involved in glycosylation of CA125 and CA15.3 and ovarian cancer risk, observing overall null associations , , , ; however, there are few studies of genetic variation specifically in MUC16 or MUC1 and their association with ovarian cancer risk or survival. Therefore, we examined the association between a targeted set of single nucleotide polymorphisms (SNPs) in MUC16 (rs2547065, rs1559168, rs12914471, rs2121133) and MUC1 (rs2070803, rs4072037, rs1045253) in relation to ovarian cancer risk and survival.
Institutional Review Boards at Brigham and Women’s Hospital, Dana Farber Cancer Institute, and Dartmouth Medical School approved the studies and all study participants signed informed consent.
Study Population and Design
Data and specimens come from the last enrollment phase of the New England Case Control Study of ovarian cancer from 2003–2008 (NECC). Details regarding case and control enrollment for this study are described elsewhere , . Briefly, of 1610 incident cases of ovarian cancer identified through hospital tumor boards and statewide cancer registries between 2003 and 2008, 897 of 1238 eligible agreed to participate. Controls were identified through town books in eastern Massachusetts and drivers’ license lists in New Hampshire. Exclusion criteria for controls included inability to be contacted, history of bilateral oophorectomy, language barriers, or relocation outside of the study area. Of 2522 controls identified, 1673 were eligible and 857 agreed to participate.
After written informed consent, demographic information, reproductive and medical history, and lifestyle factors were assessed by in-person interviews and heparinized blood samples were collected.
DNA was extracted and genotyping was performed at the Dana-Farber/Harvard Cancer Center (DF/HCC) High Throughput Polymorphism Core, an affiliate of the Partners Healthcare Center for Personalized Genetic Medicine. DNA was extracted from buffy coat samples using QIAmp (Qiagen, Chatworth, CA). Genotyping of MUC16 (rs2547065, rs1559168, rs12914471, rs2121133) and MUC1 (rs2070803, rs4072037, rs1045253) was performed using 5′ nuclease assays (Taqman®) on the Applied Biosystems Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, California). Primers, probes, and conditions for genotyping assays are available upon request. Replicates of 10% of the samples were included for quality control. Laboratory personnel were blinded to case control status and the location of quality controls.
Based on preliminary data, we sought to validate one of the SNPs in an independent dataset. We used samples from 534 cases and 1513 controls from the Nurses’ Health Study cohorts (NHS/NHSII) . The NHS includes 121,700 participants, 32,826 of whom provided blood samples in 1990 and 33,040 who gave buccal cells specimens from 2001–2004. The NHSII includes 116,430 participants, of whom 29,611 provided blood from 1996–1999 and 29,859 provided buccal cells from 2004–2006. Cases were identified after sample collection and before June 1, 2010 (NHS) or June 1, 2009 (NHSII). Demographic information on NHS and NHSII participants have been described previously . Briefly, participants in both cohorts are predominantly white (>96%), but NHS is an older cohort than NHSII which is reflected in participants’ mean age (NHS:65years, NHSII:49years), ever a child birth (95% NHS, 76% NHSII), and ever oral contraceptive use (45% NHS, 81% NHSII).
Preoperative CA125 Levels
We reviewed all medical records and computerized laboratory reports for cases who received care at Brigham and Women’s Hospital or Massachusetts General Hospital (n = 809) . CA125 values were abstracted for women whose levels had been measured prior to surgery and/or neoadjuvant chemotherapy. We were able to retrieve CA125 values on 353 of the cases genotyped in this study. Data on CA15.3 were not available.
We used chi-square tests to assess Hardy–Weinberg Equilibrium (HWE) for each SNP among controls. Unconditional logistic regression was used to calculate overall odds ratios (OR) and 95% confidence intervals (95% CI) of ovarian cancer risk adjusted for age (continuous), study center (Massachusetts or New Hampshire), and race (white or non-white). The more common allele for each SNP served as the reference group in the regression models. Co-dominant (heterozygous vs. wild type or homozygous variant vs. wild type), recessive (homozygous variant vs. heterozygous and wild type), and per allele (trend test) models were computed. Multivariate regression models were additionally adjusted for family history of ovarian or early onset breast cancer and a personal history of breast cancer.
Polytomous logistic regression was used to calculate OR (95%CI) for risk of various histological subtypes adjusted for age, study center, and race. Likelihood ratio tests were used to test for heterogeneity across histologic categories (serous borderline, serous invasive [includes high grade transitional cell and mixed serous], mucinous [borderline and invasive], endometrioid or mixed endometrioid/clear cell, clear cell, undifferentiated [includes unspecified and Brenner tumours]) comparing a model that allows the estimate of the association to vary by histologic type to a model that restricts to one estimate of the association for all histologic types.
Cox proportional hazard models (HR) were used to examine the association between each polymorphism and survival, adjusting for study center and race and in a secondary model for stage (I-IV) and histology (serous, non-serous). Co-dominant, recessive, and per allele models were used as described in supplemental methods (Methods S1). The Kaplan–Meier method was used to estimate survival curves and calculate log-rank statistics.
Geometric mean CA125 values by genotype were calculated for each MUC16 polymorphism. Statistical analyses used general linear regression, adjusted for age, race, and time between CA125 measurement and diagnosis (≤30 days, >30days, missing), using continuous log transformed CA125 levels and a variable that represents increasing variant alleles for each polymorphism (0, 1, 2). CA125 levels can vary during the menstrual cycle, and levels vary between pre and postmenopausal women , , so we stratified these analyses into three age/menopausal categories (premenopausal, “midlife” postmenopausal (age<65), and “elderly” postmenopausal (age> = 65)). All analyses were performed using SAS v 9.1 (SAS, Cary, North Carolina) and Intercooled Stata 9 (StataCorp LP, College Station, Texas).
A total of 758 women with ovarian cancer and 788 controls were included in the final analytic sample (Table 1). For both cases and controls, mean age was 54. Our study population consisted primarily of Caucasian women (>95%) and white ethnicity was more common among controls. On average, controls had higher parity, longer duration of oral contraceptive use, and a higher frequency of tubal ligation, endometriosis or painful periods, and a personal history of breast cancer. Family history of ovarian or early onset breast cancer, smoking status, and menopausal status did not differ significantly between cases and controls. Serous invasive (49.7%) was the most frequent histologic subtype among cases followed by endometrioid (17.8%).
All seven polymorphisms were in Hardy-Weinberg equilibrium and had genotyping success greater than 95% except for rs2547065 (93%). In general the minor allele frequencies (MAF) we found for our controls were comparable to that of the Caucasian European (CEU) HapMap populations (data not shown). In the NECC study, one of the four MUC16 polymorphisms was associated with ovarian cancer risk (Table 2). For polymorphism rs2547065, we observed an increase in ovarian cancer risk (per allele OR = 1.26, 95% CI: 1.09–1.47). Risk was most apparent for the homozygous variant genotype when compared to the wild type genotype (OR = 1.68, 95% CI: 1.23–2.29). However, polymorphism rs2547065 was not associated with ovarian cancer risk (per allele: OR = 1.05, 95% CI: 0.91–1.21) in an independent dataset including 534 cases and 1513 controls from the Nurses’ Health Study cohorts (NHS/NHSII). There was no significant heterogeneity by histologic type for any of the MUC16 polymorphisms.
For MUC1, none of the polymorphisms we tested were significantly associated with overall ovarian cancer risk in the per allele model. Women carrying two copies of the MUC1 polymorphism rs2070803 variant had an increased risk of serous invasive cancer of borderline statistical significance (OR = 1.35, 95% CI 0.99–1.86) in the recessive model. Although we observed no significant risk associated with MUC1 polymorphism rs4072037 overall, significant heterogeneity by histology was observed when evaluating the recessive model for this polymorphism (p-heterogeneity = 0.02). Women homozygous for the G variant of rs4072023 had a non-significantly decreased risk for invasive serous cancers but elevated risks for serous borderline tumors (OR = 1.91, 95% CI 1.08–3.36), mucinous borderline and invasive (OR = 1.68, 95% CI 1.00–2.83), and endometrioid tumors (OR = 1.54, 95% CI 1.01–2.33). Adjusting for family history of ovarian or early onset breast cancer and personal history of breast cancer did not change estimates for any of the MUC1 or MUC16 polymorphisms.
In examining the effect of mucin polymorphisms and survival, we observed poorer overall survival among women carrying the variant allele of MUC16 polymorphism rs12984471 (log-rank p = 0.03; Figure 1A) as well as an increased mortality (age-adjusted HR = 1.23 95% CI: 1.02–1.48, Table 3). These associations were strongest in women age 65 and older (log-rank p = 0.02; age-adjusted HR = 1.53 95% CI: 1.07–2.19) (Figure 1B). Mortality was 57% greater for women who were homozygous for the variant allele of rs12984471 (age-adjusted HR = 1.57 95% CI: 1.09–2.28). Adjustment for stage and histology attenuated the association (multivariate HR = 1.32 95% CI: 0.91–1.92); stage was the strongest predictor of survival. No other associations between the remaining mucin polymorphisms and survival were observed.
A. All women B. Postmenopausal women age 65 and older.
Finally, we evaluated the association between MUC16 polymorphisms and serum levels of CA125 measured preoperatively (Table 4). Among all cases, we observed no linear associations between MUC16 polymorphisms and CA125 levels; however, rs2121133 had the highest CA125 levels for heterozygotes (319.9) and lowest for homozygous variants (110.0) (p = 0.03). Among elderly postmenopausal women, increasing variant alleles of polymorphism rs12984471 were significantly associated with increasing levels of CA125 (p = 0.02).
The MUC1 gene is located on 1q21–22, which is a region frequently altered in both neoplastic and non-neoplastic disorders. MUC1 gene amplification due to increased gene copy number has been observed in ovarian, breast, papillary thyroid, and gastric cancers , , , . Neoplastic mammary cells have been shown to have a high frequency of altered DNA within the variable nucleotide repeat region (VNTR) of MUC1- the largest region of the this protein and the site of O-glycosylation , , . MUC1 has been shown to be essential for ovarian cancer tumorigenesis in mouse models and is over expressed in approximately 90–100% of serous carcinomas , , . The three MUC1 SNPs we studied were selected based on previous publications that studied associations between MUC1 polymorphisms and gastrointestinal cancers , . Polymorphism rs4072037 has been correlated with serum MUC1 levels and is known to play a role in alternative splicing , . Polymorphism rs2070803 is located upstream of the MUC1 gene in a large LD block, and polymorphism rs1045253 was previously identified as a tagSNP representative of the MUC1 region , , . None of these three polymorphisms affected risk or survival for ovarian cancer overall. Women who were homozygous for the variant G allele of rs2070803 had a 35% elevation in risk for invasive serous ovarian cancer. Although our finding was of borderline statistical significance, a Japanese study found risk for “diffuse” type gastric cancer to be increased with possession of the G allele . In tests we did for heterogeneity by histologic type of ovarian cancer, only rs4072037 varied with increased risks for types other than invasive serous including especially serous borderline, mucinous, and endometrioid. In general, all histologic types of epithelial ovarian tumors, both benign and malignant, express MUC1 on the cell surface by immunohistochemistry . However a recent study using gene expression proposed that MUC1 expression is low in one subtype of invasive serous tumors .
The MUC16 gene is located at 19p13, which is altered in a variety of cancers, especially ovarian. In ovarian carcinoma, 19p13 has been identified as the chromosome band most frequently involved in structural rearrangement , . This region has also been shown to be highly amplified in high grade serous carcinoma . Polymorphisms rs12984471, rs1559168, rs2121133 are tagSNPs and were selected for our study because they are representative of various regions of the MUC16 gene. The minor alleles of rs12984471 and rs1559168 introduce missense mutations while rs2121133 is within an intron . Polymorphism rs2547065 was selected because it was previously studied in relation to epithelial ovarian cancer  and introduces a missense mutation that could potentially contribute to a functional modification of the gene product. Among the MUC16 polymorphisms studied, we observed associations between rs2547065 and ovarian cancer risk and between rs12984471 and survival.
In a small study that examined two MUC16 variants including rs2547065, Bouanene et al. observed that the CC genotype was more frequent in cases (49%) than in controls (34%), similar in direction to what we observed but not significant in their study which included only 41 cases and 76 controls . Despite the fact that homozygous variant genotype (CC) of polymorphism rs2547065 was associated with ovarian cancer risk overall and invasive serous ovarian cancer in the NECC data, we were unable to validate this finding in independent data from the Nurses’ Health Study. With positive results from our study, supportive results from the only published study related to this SNP, but null results from the NHS, validation will be necessary and is planned within the Ovarian Cancer Association Consortium.
MUC16 polymorphism rs12984471 was the only SNP associated with survival. The variant allele, which conferred poorer survival among all cases, also was correlated with high CA125 serum levels in postmenopausal women, especially those over the age of 65. The possibility that the association between rs12984471 and survival may have a biologic basis is suggested by possible functional consequences on MUC16 protein. Polymorphism rs12984471 is located in the largely uncharacterized extracellular subunit where the C variant is responsible for a conservative missense mutation changing a glutamate to an aspartate , . Conservative amino acid changes are theorized to confer 40% likelihood of gene function modification compared to a silent mutation . Thus, it is plausible that the missense mutations caused by this polymorphism could influence the function of the MUC16 gene product as suggested by its effects on CA125 levels in cases.
In conclusion, our study examined four SNPs in MUC16 (CA125) and three SNPs in MUC1 (CA15.3) in relation to ovarian cancer risk and survival in the New England Case-Control study. One of four MUC16 SNPs, rs2547065, was associated with increased risk for ovarian cancer. A different MUC16 SNP, rs12984471, was associated with survival and also correlated with serum levels of CA125. Of the three MUC1 SNPs studied, we found one, rs4072037, which displayed significant heterogeneity by histologic type. We had the opportunity to examine one of these findings, rs2547065 with increased risk, in data from the Nurse’s Health Study. The finding was not validated raising the issue of chance in explaining our findings. However, the association of rs12984471 in MUC16 with survival has some biologic support in that it is also correlated with CA125 levels. Since our study population is composed of primarily Caucasian women, we were not able to generalize our results to other ethnicities. Our study should not be considered definitive because we targeted SNPs as opposed to conducting a comprehensive gene or genome wide investigation. Evaluation of a broader set of tagging SNPs is planned in the Ovarian Cancer Association Consortium.
We would like to thank the participants and staff of the NHS and NHSII cohorts for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. The authors assume full responsibility for analyses and interpretation of these data.
Conceived and designed the experiments: DWC KLT. Analyzed the data: KAW AFV KLT. Wrote the paper: KAW KLT SST AFV LJT DWC.
- 1. Hattrup CL, Gendler SJ (2008) Structure and function of the cell surface (tethered) mucins. Annu Rev Physiol 70: 431–457.
- 2. Bast RC Jr, Klug TL, St John E, Jenison E, Niloff JM, et al. (1983) A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer. N Engl J Med 309: 883–887.
- 3. Buller RE, Berman ML, Bloss JD, Manetta A, DiSaia PJ (1992) Serum CA125 regression in epithelial ovarian cancer: correlation with reassessment findings and survival. Gynecol Oncol 47: 87–92.
- 4. Giuntoli RL 2nd, Rodriguez GC, Whitaker RS, Dodge R, Voynow JA (1998) Mucin gene expression in ovarian cancers. Cancer Res 58: 5546–5550.
- 5. Wang L, Ma J, Liu F, Yu Q, Chu G, et al. (2007) Expression of MUC1 in primary and metastatic human epithelial ovarian cancer and its therapeutic significance. Gynecol Oncol 105: 695–702.
- 6. Theriault C, Pinard M, Comamala M, Migneault M, Beaudin J, et al. (2011) MUC16 (CA125) regulates epithelial ovarian cancer cell growth, tumorigenesis and metastasis. Gynecol Oncol 121: 434–443.
- 7. Sellers TA, Huang Y, Cunningham J, Goode EL, Sutphen R, et al. (2008) Association of single nucleotide polymorphisms in glycosylation genes with risk of epithelial ovarian cancer. Cancer Epidemiol Biomarkers Prev 17: 397–404.
- 8. Phelan CM, Tsai YY, Goode EL, Vierkant RA, Fridley BL, et al. (2010) Polymorphism in the GALNT1 gene and epithelial ovarian cancer in non-Hispanic white women: the Ovarian Cancer Association Consortium. Cancer Epidemiol Biomarkers Prev 19: 600–604.
- 9. Goode EL, Maurer MJ, Sellers TA, Phelan CM, Kalli KR, et al. (2010) Inherited determinants of ovarian cancer survival. Clin Cancer Res 16: 995–1007.
- 10. Terry KL, Vitonis AF, Hernandez D, Lurie G, Song H, et al. (2010) A polymorphism in the GALNT2 gene and ovarian cancer risk in four population based case-control studies. Int J Mol Epidemiol Genet 1: 272–277.
- 11. Harris HR, Cramer DW, Vitonis AF, DePari M, Terry KL (2012) Folate, vitamin B(6), vitamin B(12), methionine and alcohol intake in relation to ovarian cancer risk. Int J Cancer 131: E518–529.
- 12. Vitonis AF, Titus-Ernstoff L, Cramer DW (2011) Assessing ovarian cancer risk when considering elective oophorectomy at the time of hysterectomy. Obstet Gynecol 117: 1042–1050.
- 13. Tworoger SS, Gates MA, Lee IM, Buring JE, Titus-Ernstoff L, et al. (2009) Polymorphisms in the vitamin D receptor and risk of ovarian cancer in four studies. Cancer Res 69: 1885–1891.
- 14. Cramer DW, Vitonis AF, Welch WR, Terry KL, Goodman A, et al. (2010) Correlates of the preoperative level of CA125 at presentation of ovarian cancer. Gynecol Oncol 119: 462–468.
- 15. Skates SJ, Mai P, Horick NK, Piedmonte M, Drescher CW, et al. (2011) Large prospective study of ovarian cancer screening in high-risk women: CA125 cut-point defined by menopausal status. Cancer Prev Res (Phila) 4: 1401–1408.
- 16. Bon GG, Kenemans P, Dekker JJ, Hompes PG, Verstraeten RA, et al. (1999) Fluctuations in CA 125 and CA 15–3 serum concentrations during spontaneous ovulatory cycles. Hum Reprod 14: 566–570.
- 17. Takano M, Fujii K, Kita T, Kikuchi Y, Uchida K (2004) Amplicon profiling reveals cytoplasmic overexpression of MUC1 protein as an indicator of resistance to platinum-based chemotherapy in patients with ovarian cancer. Oncol Rep 12: 1177–1182.
- 18. Wreesmann VB, Sieczka EM, Socci ND, Hezel M, Belbin TJ, et al. (2004) Genome-wide profiling of papillary thyroid cancer identifies MUC1 as an independent prognostic marker. Cancer Res 64: 3780–3789.
- 19. Myllykangas S, Junnila S, Kokkola A, Autio R, Scheinin I, et al. (2008) Integrated gene copy number and expression microarray analysis of gastric cancer highlights potential target genes. Int J Cancer 123: 817–825.
- 20. Lacunza E, Baudis M, Colussi AG, Segal-Eiras A, Croce MV, et al. (2010) MUC1 oncogene amplification correlates with protein overexpression in invasive breast carcinoma cells. Cancer Genet Cytogenet 201: 102–110.
- 21. Bieche I, Lidereau R (1997) A gene dosage effect is responsible for high overexpression of the MUC1 gene observed in human breast tumors. Cancer Genet Cytogenet 98: 75–80.
- 22. Waltz MR, Pandelidis SM, Pratt W, Barnes D, Swallow DM, et al. (1998) A microsatellite within the MUC1 locus at 1q21 is altered in the neoplastic cells of breast cancer patients. Cancer Genet Cytogenet 100: 63–67.
- 23. Schroeder JA, Masri AA, Adriance MC, Tessier JC, Kotlarczyk KL, et al. (2004) MUC1 overexpression results in mammary gland tumorigenesis and prolonged alveolar differentiation. Oncogene 23: 5739–5747.
- 24. Feng H, Ghazizadeh M, Konishi H, Araki T (2002) Expression of MUC1 and MUC2 mucin gene products in human ovarian carcinomas. Jpn J Clin Oncol 32: 525–529.
- 25. Tashiro Y, Yonezawa S, Kim YS, Sato E (1994) Immunohistochemical study of mucin carbohydrates and core proteins in human ovarian tumors. Hum Pathol 25: 364–372.
- 26. Palmer AJ, Lochhead P, Hold GL, Rabkin CS, Chow WH, et al. (2012) Genetic variation in C20orf54, PLCE1 and MUC1 and the risk of upper gastrointestinal cancers in Caucasian populations. Eur J Cancer Prev 21: 541–544.
- 27. Li FX, Yang XX, He XQ, Hu NY, Wu YS, et al. (2012) Association of 10q23 with colorectal cancer in a Chinese population. Mol Biol Rep 39: 9557–9562.
- 28. Horimasu Y, Hattori N, Ishikawa N, Kawase S, Tanaka S, et al. (2012) Different MUC1 gene polymorphisms in German and Japanese ethnicities affect serum KL-6 levels. Respir Med 106: 1756–1764.
- 29. Ng W, Loh AX, Teixeira AS, Pereira SP, Swallow DM (2008) Genetic regulation of MUC1 alternative splicing in human tissues. Br J Cancer 99: 978–985.
- 30. Marin F, Bonet C, Munoz X, Garcia N, Pardo ML, et al. (2012) Genetic variation in MUC1, MUC2 and MUC6 genes and evolution of gastric cancer precursor lesions in a long-term follow-up in a high-risk area in Spain. Carcinogenesis 33: 1072–1080.
- 31. Saeki N, Saito A, Choi IJ, Matsuo K, Ohnami S, et al. (2011) A functional single nucleotide polymorphism in mucin 1, at chromosome 1q22, determines susceptibility to diffuse-type gastric cancer. Gastroenterology 140: 892–902.
- 32. Shi Y, Hu Z, Wu C, Dai J, Li H, et al. (2011) A genome-wide association study identifies new susceptibility loci for non-cardia gastric cancer at 3q13.31 and 5p13.1. Nat Genet 43: 1215–1218.
- 33. Feng H, Ghazizadeh M, Konishi H, Araki T (2002) Expression of MUC1 and MUC2 mucin gene products in human ovarian carcinomas. Japanese journal of clinical oncology 32: 525–529.
- 34. Tothill RW, Tinker AV, George J, Brown R, Fox SB, et al. (2008) Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 14: 5198–5208.
- 35. Pejovic T, Heim S, Mandahl N, Baldetorp B, Elmfors B, et al. (1992) Chromosome aberrations in 35 primary ovarian carcinomas. Genes Chromosomes Cancer 4: 58–68.
- 36. Thompson FH, Liu Y, Emerson J, Weinstein R, Makar R, et al. (1994) Simple numeric abnormalities as primary karyotype changes in ovarian carcinoma. Genes Chromosomes Cancer 10: 262–266.
- 37. Micci F, Weimer J, Haugom L, Skotheim RI, Grunewald R, et al. (2009) Reverse painting of microdissected chromosome 19 markers in ovarian carcinoma identifies a complex rearrangement map. Genes Chromosomes Cancer 48: 184–193.
- 38. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, et al. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29: 308–311.
- 39. Bouanene H, Hadj Kacem H, Ben Fatma L, Ben Limem H, Ben Ahmed S, et al. (2011) Polymorphisms in the MUC16 gene: potential implication in epithelial ovarian cancer. Pathol Oncol Res 17: 295–299.
- 40. Carraway KL, Hull SR (1989) O-glycosylation pathway for mucin-type glycoproteins. Bioessays 10: 117–121.
- 41. Tabor HK, Risch NJ, Myers RM (2002) Candidate-gene approaches for studying complex genetic traits: practical considerations. Nat Rev Genet 3: 391–397.