Association of functional genetic variants in the N6-methyladenosine reader protein YTHDC1/2 with breast cancer susceptibility

Association of functional genetic variants in the N6-methyladenosine reader protein YTHDC1/2 with breast cancer susceptibility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of functional genetic variants in the N6-methyladenosine reader protein YTHDC1/2 with breast cancer susceptibility Haoqing Cheng, Pengxia Guo, Chuying Zhang, Gege Zhang, Zhilin Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6511788/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective N6-methyladenosine (m 6 A), the most abundant mRNA modification in eukaryotes, is critical in cancer development. As key m 6 A reader, YTHDC1 and YTHDC2 may affect breast cancer risk. This study investigates the association of single nucleotide polymorphisms (SNPs) of YTHDC1/2 with breast cancer susceptibility and their interactions with environmental factors. Methods Functional SNPs of YTHDC1/2 were screened through literature and public databases, yielding nine candidates. In a frequency-matched case-control study (n = 746; age ± 2 years), their association with breast cancer susceptibility was evaluated using conditional logistic regression, followed by gene-environment interactions analysis. qRT-PCR measured expression of YTHDC2 in blood across rs9552 and rs2416282 genotypes. Public databases were further used to explore the potential target genes and functional mechanisms of YTHDC2 . Results YTHDC2 rs9552G > A was associated with an increased risk of breast cancer. YTHDC2 rs2416282 C > A was associated with a reduced risk of breast cancer. YTHDC1 haplotype C rs1715080 A rs17592288 T rs2293596 T rs3813832 was associated with an increased risk of breast cancer. YTHDC2 haplotype G rs9552 G rs17135754 A rs2416282 C rs654732 G rs2303718 decreased the risk of breast cancer. rs1715080 and rs3813832 were associated with PR receptor status, Luminal type and HER-2 positive breast cancer, and rs17135754 was associated with HER-2 positive breast cancer. Negative multiplication interactions were observed between rs2293596 and family history of cancer, rs2416282 and menarche age and menopausal status. There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. qRT-PCR results showed that the relative expression of YTHDC2 was changed by rs9552 G > A and rs2416282 C > A. YTHDC2 SNPs may identify and bind to the m6A modification sites of potential target genes to regulate the target genes, and then affect the susceptibility of breast cancer. Conclusion Our findings suggested that rs9552 G > A and rs2416282 C > A might be associated with the risk of BC. Case-control m6A YTHDC1 YTHDC2 Single nucleotide polymorphisms Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Breast cancer (BC) remains a leading global malignancy in women, with rising incidence and mortality rates posing a significant threat to women's health [ 1 ] . The latest global cancer statistics show that in 2022, there were 2.297 million new cases (11.5% of cancers in women) and 666,000 deaths (6.8%) worldwide [ 2 ] . Due to the large population base and many cases in China, BC remains a significant public health threat to Chinese women [ 3 , 4 ] .In 2022, there were 357 000 new BC cases in Chinese women (accounting for 15.6% of all female cancers), ranking second only to lung cancer. And the 5-year survival rate of metastatic BC is less than 30% [ 5 ] .The occurrence of BC is a complex process involving multi-genes, multi-steps and multi-stages. Age, family history, reproductive factors, estrogen and lifestyle factors [ 6 ] , mutations of BRCA1 and BRCA2 genes and other genetic factors are closely related to the risk of BC [ 7 ] . Studies have shown that genetic variation can explain about 49% of familial BC risk, demonstrating the critical role of genetic background in disease susceptibility [ 8 , 9 ] . N6-methyladenosine (m6A) represents the most abundant and conserved RNA modification in eukaryotes, with demonstrated functional roles across diverse cell types and cancers [ 10 ] . The m 6 A modification process involves the action of a variety of enzymes, including methyltransferases, demethylases, and binding proteins (readers). YTH domain containing proteins 1(YTHDC1) and YTHDC2 are important RNA binding proteins. At present, YTHDC1/2 have been confirmed to be closely related to the occurrence and development of a variety of cancers. YTHDC1 has been shown to promote the progression of endometrial cancer, glioblastoma and Acute myeloid leukemia (AML) [ 11 ] . Yang [ 12 ] et al. found that rs2416282 A > C mutation reduced the risk of esophageal squamous cell carcinoma by about 16% in the Chinese population by changing the expression of YTHDC2 . Liu [ 13 ] et al. found that three loci in YTHDC2 (rs6594732, rs10071816 and rs2303718) were associated with the overall survival rate of liver cancer patients treated with transcatheter arterial chemoembolization. However, no study has been reported on YTHDC1/2 gene polymorphisms and BC risk. Therefore, we identified nine YTHDC1/2 SNPs by using bioinformatics and investigated their association with BC risk, interactions with reproductive factors, and underlying mechanisms. We hypothesize that single-nucleotide polymorphisms (SNPs) of YTHDC1/2 may influence BC susceptibility by altering RNA secondary structure and gene expression, thereby modulating m 6 A modification of target genes. 2 Methods 2.1 Study participants The case-control study included 746 patients with newly diagnosed BC (case group) and 746 healthy controls (control group) by age ± 2 years with frequency matching. The case group was selected from a tertiary hospital in Henan Province, aged ≥ 18 years, excluding previous history of cancer and treatment. The control group was from the Cardiovascular Disease Survey and Genome Project of Henan Province, and had no history of cancer. All participants signed informed consent, and the study protocol was approved by the Ethics Committee of Zhengzhou University. The data for the case group were extracted from the hospital electronic medical records, including demographic characteristics such as age, menstrual and reproductive history (age of menarche, menopause status, age of menopause, number of pregnancy and abortion, breast-feeding history, etc.), tumor history in first degree relatives, hormone receptor status such as progesterone receptor(PR), and estrogen receptor (ER) and human epidermal growth factor receptor-2 (HER2), and molecular type of BC such as triple-negative breast cancer (TNBC), HER-2 positive BC, and luminal BC. Control group used a standardized questionnaire to collect the same indicators, and all data were obtained through face-to-face interviews by uniformly trained investigators. 2.2 DNA extraction Venous blood samples of 5 mL were collected from all subjects under fasting conditions using EDTA anticoagulated tubes. The samples were packaged and stored in an ultra-low temperature refrigerator at -80℃. DK601-02 blood genomic DNA small amount extraction kit was used to extract DNA from blood samples. 2.3 SNP selection and genotyping We through visit NCBI Gene website ( https://www.ncbi.nlm.nih.gov/gene/ ) and Ensembl37 database ( http://grch37.ensembl.org/index.html ) to determine the YTHDC1/2 location information area Between. Then, using Ensembl37 ( http://grch37.ensembl.org/Homo_sapiens/Tools/VcftoPed ) and Haploview 4.2 software, and according to the following standard screening in YTHDC1/2 labels on SNPs: Linkage disequilibrium (LD) (r2 ≥ 0.8) and Minor allele frequency (MAF) > 0.05. Finally, through RNAfold website ( http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi ) to predict and observe whether the secondary structure of affected by SNPs, the results are shown in Figure S1 ; Using SNPinfo ( https://manticore.niehs.nih.gov/ ), MirSNP ( http://bioinfo.bjmu.edu.cn/mirsnp/search/ ) and miRNASNP - v3 ( http://bioinfo.life.hust.edu.cn/miRNASNP/#! /) to predict whether YTHDC1/2 SNPs are miRNA binding sites; Through RegulomeDB website ( https://regulomedb.org/regulome-search/ ) predict relationship between SNPs and transcription regulation. Finally, nine functional SNPs (rs1715080, rs17592288, rs2293596, rs3813832, rs9552, rs17135754, rs2416282, rs6594732, rs2303718) were included in Table S1 . The above nine SNPs were genotyped by SNPscanTM multiplex SNP typing kit. 2.4 Quantitative Real-Time Polymerase Chain Reaction (qRT–PCR) RNA was extracted from blood cells of 95 randomly selected healthy controls, and SYBR-green qRT-PCR was used to detect the relative expression of YTHDC2 in different genotypes of the selected nine SNP S . The primer sequences of YTHDC2 and GAPDH are shown in Table S2 , and the relative expression was calculated as ΔCt. 2.5 Functional prediction of the association between YTHDC2 SNPs and breast cancer susceptibility First of all, using RM2Target database ( http://rm2target.canceromics.org/#/home ) to obtain the YTHDC2 potential target genes. The GEPIA website ( http://gepia2.cancer-pku.cn/index.html ) was used to analyze the relative expression levels of the target genes in breast cancer and adjacent tissues, and the differentially expressed genes were screened. Then, using GTEx v8 database ( https://www.gtexportal.org/home/index.html ) to express quantitative trait loci (eQTL) analysis, evaluate candidate SNPs of nearby genes mRNA expression level in whole blood or breast tissue, the effect of Potential target genes of the predicted SNPs. Finally, the application of SRAMP ( http://www.cuilab.cn/sramp ) online database prediction m 6 A modification sites of potential target genes. 2.6 Statistic Analysis Continuous variables were expressed as Mean ± standard deviation (Mean ± SD), and independent sample t test was used for comparison between groups. Categorical variables were expressed as frequency (%), and differences between groups were analyzed using Pearson's chi-square test. Conditional Logistic regression model was used to analyze the association between YTHDC1/2 SNPs and BC susceptibility, hormone receptor status and molecular typing of BC patients. Based on the dominant genetic model, stratified analysis was performed according to age and reproductive factors. All the above results were expressed as odds ratio (OR) and 95% confidence interval (95%CI). Subsequently, False positive report probability (FPRP) analysis was used to verify the accuracy of positive results and critical values [ 14 ] .The SHEsis online software ( http://analysis.biox.cn/myAnalysis.php ) was used to calculate whether each SNP was in Hardy-Weinberg equilibrium in the control group. HWE) law (P > 0.05 indicates that the control group is representative of the population) and haplotype analysis; Pearson's chi-square test was used to analyze the differences in genotype and allele frequency distribution between the case group and the control group. Two independent samples t-test was used to compare the expression levels of different YTHDC1/2 genotypes. Multifactor dimensionality reduction (MDR), classification and regression tree models were used to explore the best interaction effects of gene-environment factors. Multiple Logistic regression model was used to evaluate the multiplicative interaction ( INT M ) between genes and environmental factors. Based on the forked analysis table, the ORint value calculated by the Logistic regression model was put into the formula prepared by Toms Anderson et al. [ 15 ] to evaluate the additive interaction. The evaluation index included relative excess risk of interaction ( RERI ), attributable proportion due to interaction ( AP ) and synergy index ( S ) SPSS 26.0 and R 4.3.2 software were used for data analysis, and Adobe Illustrate 2023 and GraphPad Prism 8 software were used for graphic drawing. The level of statistical significance was set at a two-sided P < 0.05. 3 Results 3.1 Subject Characteristics This case-control study enrolled 746 breast cancer patients and 746 matched healthy controls, with baseline characteristics presented in Table 1 . The mean age was 48.53 ± 10.29 years in cases and 48.96 ± 10.37 years in controls. Multivariate logistic regression analysis revealed significantly increased BC risk associated with age ≥ 45 years( OR : 1.616, 95% CI ༚1.209–2.160), two abortions( OR ༚1.752, 95% CI ༚1.195–2.568), three or more abortions ( OR ༚5.247, 95% CI ༚3.148–8.747)and family history of cancer༈ OR ༚1.475, 95% CI ༚1.098–1.984༉. Women with menarche age ≥ 14 years ( OR : 0.619, 95% CI : 0.496–0.774) and menopause ( OR : 0.722, 95% CI : 0.544–0.959) had lower risk of BC. Compared with women who had no more than one pregnancy, women who had two, three and four or more pregnancies had a 58.5%, 67.2% and 74.9% lower risk of BC, respectively, and the risk decreased with the increase of the number of pregnancies ( P trend <0.001). In addition, 445 cases (59.7%) were ER positive, 421 cases (56.4%) PR positive and 367 cases (49.2%) HER-2 positive in BC patients. Table 1 Basic characteristics of breast cancer cases and healthy controls Variables Case (%) n = 746 Control (%) n = 746 P a OR (95% CI ) d P Age (mean ± SD) 48.96 ± 10.37 48.53 ± 10.29 0.414 b Age(years) < 45 280(37.5) 268(35.9) 1 ≥ 45 466(62.5) 478(64.1) 0.519 1.616 (1.209–2.160) 0.001 Age at menarche < 14 256(34.3) 349(46.8) 1 ≥ 14 490(65.7) 397(53.2) < 0.001 0.619 (0.496–0.774) < 0.001 Number of pregnancy ≤ 1 60(8.0) 110(14.7) 1 2 225(30.2) 193(25.9) 0.415 (0.283–0.610) < 0.001 3 219(29.4) 191(25.6) < 0.001 c 0.328 (0.213–0.506) < 0.001 ≥ 4 242(32.4) 252(33.8) 0.251 (0.155–0.406) < 0.001 Number of abortion 0 351(47.1) 313(42.0) 1 1 204(27.3) 180(24.1) 1.333 (0.984–1.806) 0.063 2 151(20.2) 149(20.0) < 0.001 c 1.752 (1.195–2.568) 0.004 ≥ 3 40(5.4) 104(13.9) 5.247 (3.148–8.747) < 0.001 Breast-feeding No 29(3.9) 32(4.3) 1 Yes 717(96.1) 714(95.7) 0.695 1.058 (0.612–1.831) 0.840 Menopause statue Pre-menopause 402(53.9) 440(59.0) 1 Post-menopause 344(46.1) 306(41.0) 0.047 0.722 (0.544–0.959) 0.024 Family history No 647(86.7) 612(82.0) 1 Yes 99(13.3) 134(18.0) 0.013 1.475 (1.098–1.984) 0.010 ER receptor Negative 253(33.9) Positive 445(59.7) Missing 48(6.4) ER receptor Negative 278(37.3) Positive 421(56.4) Missing 47(6.3) Her-2 receptor Negative 133(17.8) Positive 367(49.2) Missing 246(33.0) a χ 2 test, two-sided P < 0.05 is statistically. b Student’s t test, two-sided P < 0.05 is statistically. c χ 2 trend test, two-sided P < 0.05 is statistically. d Multivariate Logistic regression model adjusted by age, age of menarche, number of pregnancy, number of abortion, breast-feeding history, menopausal status and family history of tumors. Bold values mean significant results with P < 0.05. 3.2 The associations between YTHDC1/2 SNPs and BC susceptibility The genotype and allele frequency distributions of the nine SNPs in the YTHDC1/2 gene are shown in Table S3. The results showed that the genotype (P = 0.033) and allele (P = 0.031) frequencies of rs9552 and genotypes (P = 0.017) and allele (P = 0.010) frequencies of rs2416282 were significantly different between BC cases and controls. The results of the HWE test in the control population and the association of YTHDC1/2 SNPs with BC susceptibility are shown in Figure S2 and Fig. 1 . All SNPs were consistent with HWE (all PHWE > 0.05), which confirmed that the selected controls were representative of the population. For YTHDC2 rs9552 G > A, the AA genotype increased the risk of BC by 0.771 times ( OR : 1.771, 95% CI : 1.186–2.644) and 0.673 times ( OR : 1.673, 95% CI : 1.673) in the codominant model and the recessive model, respectively. For YTHDC2 rs17135754 G > C, the CC genotype increased the risk of BC by 1.858 times( OR : 2.858, 95% CI ༚1.077–7.584)and 1.843 times༈ OR ༚2.843, 95% CI ༚1.073–7.535༉in the codominant model and the recessive model, respectively. For YTHDC2 rs2416282 C > A, in the codominant model, the CA genotype ( OR : 0.657, 95% CI : 0.487–0.886) and AA genotype ( OR : 0.601, 95% CI : 0.439–0.825) could reduce the risk of BC. In the dominant model, the CA + AA genotype could reduce the risk of BC by 36.6% ( OR : 0.634, 95% CI : 0.478–0.840). For the remaining SNPs, there was no significant association between different genotypes and breast cancer risk in the four types of models (all P > 0.05) 3.3 Stratified analysis between YTHDC1/2 SNPs and BC risk Based on the dominant model, the stratification results of the 9 SNPs and BC susceptibility showed that for rs2416282 C > A, CA + CC genotype was associated with age ≥ 45 years ( P = 0.001), age at menarche ≥ 14 years ( P = 0.001), post-menopause ( P = 0.005), age at menopause < 50 years ( P = 0.010), three pregnancies (P = 0.001), 1 abortion ( P = 0.008), and breast-feeding history (P = 0.002) P = 0.003) and no family history of cancer ( p = 0.003). The results are shown in Fig. 2 . For rs17592288 A > C, AC + CC genotype decreased the risk of BC in the subgroup with 1 miscarriage ( P = 0.010). For rs2293596 T > C, TC + CC genotype decreased the risk of BC in the subgroup of post-menopausal( P = 0.022) and family history of cancer ( P = 0.023). For rs9552 G > A, GA + AA genotype increased the risk of BC in the population with menarche age ≥ 14 years ( P = 0.011). For rs6594732 C > A, CA + AA genotype increased the risk of BC in the population with only one miscarriage ( P = 0.045). However, there was no significant association between different genotypes of rs1715080 T > C, rs3813832 T > C, rs17135754 G > C and rs2303718 G > A and BC susceptibility (all P > 0.05). The results are shown in Figures S3-S10. 3.4 Haplotype analysis The results of haplotype analysis are shown in Table S4. YTHDC1 haplotype C rs1715080 A rs17592288 T rs2293596 T rs3813832 was associated with an increased risk of BC ( OR : 1.287, 95% CI : 1.002–1.654). YTHDC2 haplotype G rs9552 G rs17135754 A rs2416282 C rs654732 G rs2303718 was associated with a reduced risk of BC ( OR : 0.824, 95% CI : 0.712–0.953). 3.5 Associations between YTHDC1/2 SNPs with receptor status and molecular type in BC The results of the association analysis of the nine YTHDC1/2 SNPs with BC hormone receptor status and molecular typing are shown in Tables S5-S6. BC patients with TC ( P = 0.009) and TC + CC genotypes ( P = 0.025) of YTHDC1 rs1715080 were 37.4% and 31.8% less likely to be PR receptor positive, respectively. BC patients with TC ( P = 0.006) and TC + CC genotypes ( P = 0.034) of YTHDC1 rs3813832 were 36.0% and 28.2% less likely to be PR receptor positive, respectively. BC patients with YTHDC2 rs17135754 CC ( P = 0.035) were 71.0% less likely to be HER-2 positive. BC patients with YTHDC1 rs1715080 TC ( P = 0.013) and TC + CC genotypes ( P = 0.016) had a 41.2% and 38.6% lower likelihood of Luminal BC, respectively. The risk of HER-2 positive breast cancer in BC patients with TC + CC genotype ( P = 0.046) increased by 0.655 times. BC patients with YTHDC1 rs3813832 TC ( P = 0.007) and TC + CC genotypes ( P = 0.012) had a 41.1% and 37.5% lower likelihood of Luminal BC, respectively. BC patients with TC genotype ( P = 0.047) had a 0.591 times higher risk of HER-2 positive BC. 3.6 False positive report probability analysis The results of the FPRP analysis are shown in Table S7. When 0.25 was used as a test probability, the FPRP values of rs17135754 co-dominant model (GG/CC) and recessive model (GG + GC/CC) were 0.518 and 0.519, respectively, and the FPRP value of GC + CC genotype was 0.572. This indicates that these results may be false positives. 3.7 Interaction analysis between YTHDC1/2 SNPs and reproductive factors The results of MDR software analysis are shown in Table 2 . The third-order model was the best, including three factors: Age at menarche, number of pregnancies and rs9552 G > A, indicating an interaction between rs9552 G > A and environmental factors. Women with menarche age ≥ 14 years, number of pregnancies ≥ 2 and rs9552 A genotype had 2.070 times the risk of BC compared with the control population ( OR : 2.070, 95% CI : 1.665–2.572). CART model analysis results showed that three explanatory variables were screened out, including: age at menarche, number of pregnancies, and rs9552 G > A, which verified the results of MDR (Figure S11). Table 2 Interaction results between YTHDC1/2 SNPs and reproduction factors by MDR Model TBA a CVC b χ 2 P OR (95% CI ) Age at menarche 0.562 10/10 21.642 < 0.001 1.683 (1.352–2.097) Age at menarche、Number of pregnancy 0.564 7/10 35.439 < 0.001 1.932 (1.554–2.403) Age at menarche、Number of pregnancy、rs9552 0.567 5/10 43.415 < 0.001 2.070(1.665–2.572) a Testing balance accuracy. b Cross-validation consistency. Tables S7-S15 show that there were negative multiplicative interactions between rs2293596 and family history of cancer, rs2416282 and age at menarche and menopausal status. There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. However, no significant additive interaction was observed between YTHDC1/2 SNPs and reproductive factors. 3.8 Relative expression of YTHDC2 in blood samples with different genotypes of SNP The results of the qRT-PCR are shown in Fig. 3 . For rs9552 (Fig. 3 A), The relative expression level of YTHDC2 in the GG genotype was lower than that in the AA genotype ( P = 0.004). For rs2416282 (Fig. 3 B). The relative expression level of YTHDC2 in CC genotype was higher than that in CA ( P = 0.018) and AA genotypes ( P = 0.009). 3.9 Functional mechanisms of YTHDC2 SNPs predicted by bioinformatics analysis In this study, a total of 5 potential target genes were Validated, including CES2 [ 16 ] , CYLD [ 17 ] , HOXA13 [ 18 ] , IGF1R [ 19 ] and SLC7A11 [ 20 ] , However, no potential target genes of YTHDC2 were found in breast cancer cell lines( Table S16). Furthermore, according to the cut-off value of P -value < 0.05, there were two possible potential target genes CES2 and IGFIR with different expression in BC and normal tissues (Figure S12). Then, according to the P -value < 10e-5, rs9552, rs17135754, rs2416282 and rs6594732 were all associated with the expression of YTHDC2 in whole blood and BC tissues (Figure S13). rs2303718 is associated with the expression of YTHDC2 in whole blood tissues. No target-genes regulated by other YTHDC2 SNPs were identified. Using the SRAMP website, it was found that CES2 (65) and IGFIR (15) had potential m 6 A modification sites (Fig. 4 ). The RNA secondary structure prediction maps of m 6 A modification sites of the two target genes with Very high confidence are shown in Figure S14. Based on the above prediction results, we hypothesized that YTHDC2 may regulate target-genes by recognizing and binding to their m6A modification sites, thereby affecting breast cancer susceptibility. 4 Discussion The results of the current case-control study showed that rs9552G > A and rs2416282C > T were associated with BC risk. qRT-PCR results showed that rs9552 G > A and rs2416282 C > T had significant effects on the expression of YTHDC2, respectively. Therefore, we hypothesized that these two SNPS may alter the expression level of YTHDC2 in blood, thereby affecting BC. Furthermore, the potential functional mechanism of YTHDC2 was predicted by bioinformatics, and it was found that YTHDC2 SNPs may identify and regulate the target gene by combining with the m 6 A modification site of the potential target gene, thereby affecting the susceptibility to BC. Our findings identify novel BC risk biomarkers, enabling targeted screening and precision prevention strategies for high-risk populations with specific genetic and reproductive profiles. Tanabe et al. [ 21 ] found that YTHDC2 promoted the malignant phenotype of BC cells and that there was a significant positive correlation between YTHDC2 expression level and BC stage. AA genotype of YTHDC2 rs9552 was significantly associated with an increased risk of BC. CA + AA of rs2416282 was associated with a reduced risk. This may be because the variations of rs9552 and rs2416282 affect the function of YTHDC2 in the process of RNA stability, splicing, transport and translation, interfere with the m 6 A modification and expression level of tumor-related genes, and then regulate the proliferation, invasion and migration of tumor cells. At present, no study has been reported on YTHDC1 gene polymorphisms and disease risk, and no significant association between YTHDC1 SNPs and BC risk was found in the present study. The classification of BC relies on immunohistochemical detection of ER, PR, HER-2, and Ki-67 expression in tumor tissue, categorizing cases into Luminal A/B, HER-2 positive and TNBC [ 22 ] . Studies have shown that ER and PR positive patients have a better prognosis after receiving hormone therapy [ 23 , 24 ] . At present, the prognosis of HER-2 positive BC has been significantly improved thanks to the application of HER-2 targeted drugs (such as trastuzumab) [ 25 ] .However, the problem of HER-2 positive BC resistance to targeted therapy still exists, and some patients may not receive optimal treatment due to treatment side effects or economic burden [ 26 ] .Luminal type BC expresses ER and/or PR and is sensitive to endocrine therapy, but Luminal B type has a higher proliferation index and poor prognosis and may require combined chemotherapy [ 27 ] . Therefore, the discovery of HER-2 positive and Luminal BC risk biomarkers is of great clinical importance. The results of our study suggest that rs1715080 and rs3813832 may be used as risk biomarkers for HER-2 positive BC, which may help to achieve early identification, diagnosis and treatment of HER-2 positive BC. The occurrence of BC is affected by many environmental factors. Studies found that increasing age at menarche reduced the risk of BC [ 28 , 29 ] . The risk of BC in pre-menopausal women was higher than that in menopausal women of the same age ( RR : 1.029, 95% CI : 1.025–1.023) [ 30 ] . Women who become pregnant for the first time before the age of 20 can reduce the risk of BC, but women who at the age of 35 May increase the risk of BC [ 31 ] . Another study found that full-term pregnancy can reduce a woman's long-term risk of BC, and no reduction was observed for pregnancies lasting 33 weeks or less [ 32 ] . Therefore, factors such as age at first pregnancy and duration of pregnancy closely affect the association between pregnancy and BC susceptibility. The results of a Chinese case-control study showed that women had a significantly increased risk of BC in those who had experienced at least one medical abortion compared with those who had never had an induced abortion [ 31 ] . Results from a cohort study of Korean women showed that after adjusting for multiple confounding factors, family history of BC was significantly associated with an increased risk of BC [ 33 ] . The above conclusions are consistent with the results of this study. Therefore, we recommend regular BC screening for women with advanced maternal age at first pregnancy or preterm delivery, and prioritized screening/education for those with abortion history or familial cancer risk, to enable early detection and risk reduction. As a complex disease, BC is induced by both genetic and environmental factors. A genome-wide gene-environment interaction study of BC in more than 90,000 women identified a crosstalk between predicted expression of the C13orf45 gene and age at first full-term pregnancy ( P GXE T) was associated with Q2 of energy intake ( P-interaction = 0.042), HIF-1α C1772T rs11549465(C > T) and Q4༈ P-interaction = 0.007༉were associated with a decreased risk of BC in overall women. Rudolph et al. [ 36 ] found an interaction between the number of full-term pregnancies and rs4808801, as well as between smoking and rs11242675. In our study, first MDR results showed that the interaction between rs9552 A genotype, age at menarche, and number of pregnancies could increase the risk of BC. The classification and regression tree model screened these three key variables and verified the MDR results. In addition, there were negative multiplicative interactions between rs2293596 and family history of cancer, and between rs2416282 and menarche age and menopausal status by multiplicative model and additive effect [ 37 ] . There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. Therefore, BC can be prevented in daily life by regulating environmental factors, such as reducing the number of abortions, thereby reducing the independent influence of environmental factors and their interaction with genetic factors. In this study, we first found that rs9552 G > A and rs2416282 C > A of YTHDC2 were associated with BC risk. Further qRT-PCR experiments were performed, and the relative expression of YTHDC2 in blood samples with rs9552 GG genotype was lower than that with AA genotype. The relative expression of rs2416282 CC genotype was higher than that of CA and AA genotype. Therefore, in this study, the potential functional mechanism of YTHDC2 was further predicted by bioinformatics. Studies have shown that YTHDC2 specifically recognizes and binds to m6A modification sites on mRNA through its YTH domain, and YTHDC2 regulates RNA function by binding to these sites [ 38 ] . In some cases, YTHDC2 may also enhance mRNA stability by inhibiting its degradation [ 39 ] .Ma et al. [ 18 ] pointed out that YTHDC2 can recognize the m6A modification site on the 3'-UTR of HOXA13 in lung adenocarcinoma cells, destroy the stability of HOXA13 , inhibit the expression of HOXA13 , and the expression level of its downstream target gene SLC3A2 is also inhibited, thereby causing ferroptosis and inhibiting the occurrence and development of lung adenocarcinoma. In this study, two potential target genes of m6A reader YTHDC2 were screened by online database, and the above target genes were predicted by the website to have potential m 6 A modification sites to stabilize the target mRNA, thereby affecting the susceptibility of BC. This study was a large case-control study. A total of 746 newly diagnosed BC cases and 746 healthy controls were included, and all the included cases were pathologically confirmed BC, which reduced the prevalence-incidence bias. Data were collected by investigators with standardized training, which reduced the information bias caused by subjective factors. This study is the first to report the association between YTHDC2 rs9552 G > A and YTHDC2 rs2416282 C > A and the risk of BC. However, the results of this study also have some limitations. Retrospective data may have reporting bias, which needs to be further verified by a large prospective cohort. The sample of this study was from the Chinese Han population, and it needs to be verified in other ethnic or regional populations. In addition, this study did not carry out experimental verification at the cell level, and only through the public platform database and bioinformatics prediction, the potential target genes and potential mechanisms of the SNPS found in this study that were related to the risk of BC were predicted. 5 Conclusion YTHDC2 rs9552 G > A was significantly associated with an increased risk of BC. YTHDC2 rs2416282 C > A was significantly associated with a reduced risk of BC. YTHDC2 SNPs may identify and bind to m6A modification sites of potential target genes to regulate target genes, and then affect BC susceptibility, but this association needs further prospective cohort studies and cell experiments to verify. Declarations Acknowledgments This work was supported by the Henan Province Science and Technology Research Project (252102311033) CRediT authorship contribution statement Haoqing Cheng: Writing – original draft, Formal analysis, Conceptualization. Pengxia Guo : Data curation. Chuying Zhang : Data curation. Gege Zhang : Data curation. Zhilin Zhang : Data curation. Shiyuan Wang : Data curation. Saba Fida : Data curation. Chunhua Song : Writing – review & editing, Conceptualization. Ethical Approval The study was approved by the appropriate ethics committee. Competing interests The authors declare that they have no competing interests. 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Wacholder S, Chanock S, Garcia-Closas M, et al. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies[J]. J Natl Cancer Inst. 2004;96(6):434–42. Andersson T, Alfredsson L, Källberg H, et al. Calculating measures of biological interaction[J]. Eur J Epidemiol. 2005;20(7):575–9. Takemoto S, Nakano M, Fukami T, et al. m(6)A modification impacts hepatic drug and lipid metabolism properties by regulating carboxylesterase 2[J]. Biochem Pharmacol. 2021;193:114766. Wang J, Tan L, Jia B, et al. Downregulation of m(6)A Reader YTHDC2 Promotes the Proliferation and Migration of Malignant Lung Cells via CYLD/NF-κB Pathway[J]. Int J Biol Sci. 2021;17(10):2633–51. Ma L, Zhang X, Yu K, et al. Targeting SLC3A2 subunit of system X(C)(-) is essential for m(6)A reader YTHDC2 to be an endogenous ferroptosis inducer in lung adenocarcinoma[J]. Free Radic Biol Med. 2021;168:25–43. He JJ, Li Z, Rong ZX, et al. m(6)A Reader YTHDC2 Promotes Radiotherapy Resistance of Nasopharyngeal Carcinoma via Activating IGF1R/AKT/S6 Signaling Axis[J]. Front Oncol. 2020;10:1166. Ma L, Chen T, Zhang X, et al. The m(6)A reader YTHDC2 inhibits lung adenocarcinoma tumorigenesis by suppressing SLC7A11-dependent antioxidant function[J]. Redox Biol. 2021;38:101801. Tanabe A, Nakayama T, Kashiyanagi J, et al. YTHDC2 Promotes Malignant Phenotypes of Breast Cancer Cells[J]. J Oncol. 2022;2022:9188920. Wolff AC, Hammond ME, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update[J]. J Clin Oncol. 2013;31(31):3997–4013. Nicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: Past, present and future[J]. Semin Cancer Biol. 2018;52(Pt 1):56–73. Davies C, Godwin J, Gray R, et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials[J]. Lancet. 2011;378(9793):771–84. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2[J]. N Engl J Med. 2001;344(11):783–92. Swain SM, Baselga J, Kim SB, et al. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer[J]. N Engl J Med. 2015;372(8):724–34. Comprehensive molecular portraits. of human breast tumours[J]. Nature. 2012;490(7418):61–70. Ritte R, Lukanova A, Tjønneland A, et al. Height, age at menarche and risk of hormone receptor-positive and -negative breast cancer: a cohort study[J]. Int J Cancer. 2013;132(11):2619–29. Zhao Z, Zhang J, Tian X. Relationship between age at menarche and breast cancer in individuals, as well as in first-degree kin and estrogen receptor status: a Mendelian randomization study[J]. Front Oncol. 2024;14:1408132. Menarche menopause. breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies[J]. Lancet Oncol. 2012;13(11):1141–51. Slepicka PF, Cyrill SL, Dos Santos CO. Pregnancy and Breast Cancer: Pathways to Understand Risk and Prevention[J]. Trends Mol Med. 2019;25(10):866–81. Husby A, Wohlfahrt J, Øyen N, et al. Pregnancy duration and breast cancer risk[J]. Nat Commun. 2018;9(1):4255. Mai Tran TX, Kim S, Song H, et al. Family history of breast cancer, mammographic breast density and breast cancer risk: Findings from a cohort study of Korean women[J]. Breast. 2022;65:180–6. Wang X, Chen H, Middha Kapoor P, et al. A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women[J]. Cancer Res Commun. 2022;2(4):211–9. Ilozumba MN, Yaghjyan L, Datta S, et al. mTOR pathway candidate genes and energy intake interaction on breast cancer risk in Black women from the Women's Circle of Health Study[J]. Eur J Nutr. 2023;62(6):2593–604. Rudolph A, Milne RL, Truong T, et al. Investigation of gene-environment interactions between 47 newly identified breast cancer susceptibility loci and environmental risk factors[J]. Int J Cancer. 2015;136(6):E685–696. Knol MJ, van der Tweel I, Grobbee DE, et al. Estimating interaction on an additive scale between continuous determinants in a logistic regression model[J]. Int J Epidemiol. 2007;36(5):1111–8. Wang X, Lu Z, Gomez A, et al. N6-methyladenosine-dependent regulation of messenger RNA stability[J]. Nature. 2014;505(7481):117–20. Shi H, Wang X, Lu Z, et al. YTHDF3 facilitates translation and decay of N(6)-methyladenosine-modified RNA[J]. Cell Res. 2017;27(3):315–28. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6511788","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449915111,"identity":"c49f7142-1521-4918-9d0b-63b78928269c","order_by":0,"name":"Haoqing Cheng","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haoqing","middleName":"","lastName":"Cheng","suffix":""},{"id":449915112,"identity":"00b93763-93bb-4775-836f-33cf9a387b81","order_by":1,"name":"Pengxia Guo","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Pengxia","middleName":"","lastName":"Guo","suffix":""},{"id":449915113,"identity":"887685d5-7b78-4fb6-b222-5081201d9da2","order_by":2,"name":"Chuying Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chuying","middleName":"","lastName":"Zhang","suffix":""},{"id":449915114,"identity":"5bceb0ce-9c1d-4651-bccd-53082c807a31","order_by":3,"name":"Gege Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Gege","middleName":"","lastName":"Zhang","suffix":""},{"id":449915115,"identity":"a98abb26-f014-42f8-974f-586374056d72","order_by":4,"name":"Zhilin Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhilin","middleName":"","lastName":"Zhang","suffix":""},{"id":449915116,"identity":"bc37e083-341f-408f-b054-2236e43c7d8d","order_by":5,"name":"Shiyuan Wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shiyuan","middleName":"","lastName":"Wang","suffix":""},{"id":449915117,"identity":"ac95bee2-71b1-4f39-85ab-ec51fa63c0f2","order_by":6,"name":"Saba Fida","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Saba","middleName":"","lastName":"Fida","suffix":""},{"id":449915118,"identity":"ed5cf9be-e7d5-4cc3-9450-a4469802a6d4","order_by":7,"name":"Chunhua Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACHoYDHyogTAlitTAenHGGRC3MhznbSNEi3372wGHGeXX2BgeYD97mYbDLI6jF4ExewuHCbWzMBgfYkq15GJKLCWuR4DE4PHMbD5vBAR4zaWBQJDYQdNgMoBbeOUCNB/i/EaeF4QZIS4OBBNAWNuK0GJzJMTg441iCgeRhNmPLOQbJRDis/Yzxhw81dfZ8x5sf3nhTYUeEw+CAGWwp8epHwSgYBaNgFOABAK58N14hzvsUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6028-5923","institution":"Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-04-23 10:41:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6511788/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6511788/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82351607,"identity":"4a4420ea-bbbf-4c64-8eb5-c4e54add3d14","added_by":"auto","created_at":"2025-05-09 10:58:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1425979,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation analysis of \u003cem\u003eYTHDC1\u003c/em\u003e SNPs genotype and breast cancer susceptibility. \u003csup\u003ea \u003c/sup\u003eP value of Hardy-Weinberg equilibrium in controls. \u003csup\u003eb \u003c/sup\u003eMultivariate Logistic regression model adjusted by age, age of menarche, number of pregnancy, number of abortion, breast-feeding history, menopausal status and family history of tumors. Bold values mean significant results with \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/31f5ee84024a95a2eef2648f.png"},{"id":82349995,"identity":"9b00fc1e-3a0a-44c2-8e0f-c8dfd741c35d","added_by":"auto","created_at":"2025-05-09 10:50:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":855474,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of\u003cem\u003e YTHDC2\u003c/em\u003e SNP rs2416282 and breast cancer risk in dominant model.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/2616625b86b6f23a9a114ede.png"},{"id":82348531,"identity":"f706b44c-9b9c-4b42-85a7-1e9459f4a06e","added_by":"auto","created_at":"2025-05-09 10:42:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353387,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of \u003cem\u003eYTHDC2 \u003c/em\u003ein different genotypes. (A)rs9552; (B)rs2416282.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/c00a8708886d0a73b166cbe4.png"},{"id":82348534,"identity":"7bb26a34-6c3a-4c8a-9751-39119f4eb569","added_by":"auto","created_at":"2025-05-09 10:42:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554642,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of m\u003csup\u003e6\u003c/sup\u003eA modification sites in target genes using the SRAMP database .(A: \u003cem\u003eCES2\u003c/em\u003e; B: \u003cem\u003eBIGFIR\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/009a08c43587f9351f5a157c.png"},{"id":87533926,"identity":"919c49b2-67c0-40f3-8f8c-a38340ea1a40","added_by":"auto","created_at":"2025-07-25 01:02:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4522136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/4fc68d6c-32cd-4462-8a37-61fedeb1e2f1.pdf"},{"id":82348547,"identity":"4f821f76-77ef-41dc-9845-c8faf6fbb42d","added_by":"auto","created_at":"2025-05-09 10:42:00","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7639387,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/b83549123277f5d6d917d641.docx"},{"id":82348535,"identity":"6735ccbc-153e-4caf-8dc8-8dff36ebbcf3","added_by":"auto","created_at":"2025-05-09 10:42:00","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":125255,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6511788/v1/f6e7a598e8833dbce1b4f3b8.docx"}],"financialInterests":"","formattedTitle":"Association of functional genetic variants in the N6-methyladenosine reader protein YTHDC1/2 with breast cancer susceptibility","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer (BC) remains a leading global malignancy in women, with rising incidence and mortality rates posing a significant threat to women's health\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The latest global cancer statistics show that in 2022, there were 2.297\u0026nbsp;million new cases (11.5% of cancers in women) and 666,000 deaths (6.8%) worldwide\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Due to the large population base and many cases in China, BC remains a significant public health threat to Chinese women \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.In 2022, there were 357 000 new BC cases in Chinese women (accounting for 15.6% of all female cancers), ranking second only to lung cancer. And the 5-year survival rate of metastatic BC is less than 30%\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.The occurrence of BC is a complex process involving multi-genes, multi-steps and multi-stages. Age, family history, reproductive factors, estrogen and lifestyle factors\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, mutations of \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes and other genetic factors are closely related to the risk of BC\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that genetic variation can explain about 49% of familial BC risk, demonstrating the critical role of genetic background in disease susceptibility \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eN6-methyladenosine (m6A) represents the most abundant and conserved RNA modification in eukaryotes, with demonstrated functional roles across diverse cell types and cancers\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The m\u003csup\u003e6\u003c/sup\u003eA modification process involves the action of a variety of enzymes, including methyltransferases, demethylases, and binding proteins (readers). YTH domain containing proteins 1(YTHDC1) and \u003cem\u003eYTHDC2\u003c/em\u003e are important RNA binding proteins. At present, \u003cem\u003eYTHDC1/2\u003c/em\u003e have been confirmed to be closely related to the occurrence and development of a variety of cancers. \u003cem\u003eYTHDC1\u003c/em\u003e has been shown to promote the progression of endometrial cancer, glioblastoma and Acute myeloid leukemia (AML)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Yang \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e et al. found that rs2416282 A\u0026thinsp;\u0026gt;\u0026thinsp;C mutation reduced the risk of esophageal squamous cell carcinoma by about 16% in the Chinese population by changing the expression of \u003cem\u003eYTHDC2\u003c/em\u003e. Liu\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e et al. found that three loci in \u003cem\u003eYTHDC2\u003c/em\u003e (rs6594732, rs10071816 and rs2303718) were associated with the overall survival rate of liver cancer patients treated with transcatheter arterial chemoembolization. However, no study has been reported on \u003cem\u003eYTHDC1/2\u003c/em\u003e gene polymorphisms and BC risk.\u003c/p\u003e \u003cp\u003eTherefore, we identified nine \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs by using bioinformatics and investigated their association with BC risk, interactions with reproductive factors, and underlying mechanisms. We hypothesize that single-nucleotide polymorphisms (SNPs) of \u003cem\u003eYTHDC1/2\u003c/em\u003e may influence BC susceptibility by altering RNA secondary structure and gene expression, thereby modulating m\u003csup\u003e6\u003c/sup\u003eA modification of target genes.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study participants\u003c/h2\u003e \u003cp\u003eThe case-control study included 746 patients with newly diagnosed BC (case group) and 746 healthy controls (control group) by age\u0026thinsp;\u0026plusmn;\u0026thinsp;2 years with frequency matching. The case group was selected from a tertiary hospital in Henan Province, aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, excluding previous history of cancer and treatment. The control group was from the Cardiovascular Disease Survey and Genome Project of Henan Province, and had no history of cancer. All participants signed informed consent, and the study protocol was approved by the Ethics Committee of Zhengzhou University.\u003c/p\u003e \u003cp\u003eThe data for the case group were extracted from the hospital electronic medical records, including demographic characteristics such as age, menstrual and reproductive history (age of menarche, menopause status, age of menopause, number of pregnancy and abortion, breast-feeding history, etc.), tumor history in first degree relatives, hormone receptor status such as progesterone receptor(PR), and estrogen receptor (ER) and human epidermal growth factor receptor-2 (HER2), and molecular type of BC such as triple-negative breast cancer (TNBC), HER-2 positive BC, and luminal BC. Control group used a standardized questionnaire to collect the same indicators, and all data were obtained through face-to-face interviews by uniformly trained investigators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 DNA extraction\u003c/h2\u003e \u003cp\u003eVenous blood samples of 5 mL were collected from all subjects under fasting conditions using EDTA anticoagulated tubes. The samples were packaged and stored in an ultra-low temperature refrigerator at -80℃. DK601-02 blood genomic DNA small amount extraction kit was used to extract DNA from blood samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 SNP selection and genotyping\u003c/h2\u003e \u003cp\u003eWe through visit NCBI Gene website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Ensembl37 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://grch37.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"http://grch37.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to determine the YTHDC1/2 location information area Between. Then, using Ensembl37 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://grch37.ensembl.org/Homo_sapiens/Tools/VcftoPed\u003c/span\u003e\u003cspan address=\"http://grch37.ensembl.org/Homo_sapiens/Tools/VcftoPed\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Haploview 4.2 software, and according to the following standard screening in YTHDC1/2 labels on SNPs: Linkage disequilibrium (LD) (r2\u0026thinsp;\u0026ge;\u0026thinsp;0.8) and Minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Finally, through RNAfold website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi\u003c/span\u003e\u003cspan address=\"http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict and observe whether the secondary structure of affected by SNPs, the results are shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Using SNPinfo (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://manticore.niehs.nih.gov/\u003c/span\u003e\u003cspan address=\"https://manticore.niehs.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), MirSNP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.bjmu.edu.cn/mirsnp/search/\u003c/span\u003e\u003cspan address=\"http://bioinfo.bjmu.edu.cn/mirsnp/search/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and miRNASNP - v3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/miRNASNP/#!\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/miRNASNP/#!\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e /) to predict whether YTHDC1/2 SNPs are miRNA binding sites; Through RegulomeDB website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://regulomedb.org/regulome-search/\u003c/span\u003e\u003cspan address=\"https://regulomedb.org/regulome-search/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) predict relationship between SNPs and transcription regulation. Finally, nine functional SNPs (rs1715080, rs17592288, rs2293596, rs3813832, rs9552, rs17135754, rs2416282, rs6594732, rs2303718) were included in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The above nine SNPs were genotyped by SNPscanTM multiplex SNP typing kit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quantitative Real-Time Polymerase Chain Reaction (qRT\u0026ndash;PCR)\u003c/h2\u003e \u003cp\u003eRNA was extracted from blood cells of 95 randomly selected healthy controls, and SYBR-green qRT-PCR was used to detect the relative expression of \u003cem\u003eYTHDC2\u003c/em\u003e in different genotypes of the selected nine SNP\u003csub\u003eS\u003c/sub\u003e. The primer sequences of \u003cem\u003eYTHDC2\u003c/em\u003e and GAPDH are shown in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, and the relative expression was calculated as ΔCt.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Functional prediction of the association between YTHDC2 SNPs and breast cancer susceptibility\u003c/h2\u003e \u003cp\u003eFirst of all, using RM2Target database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rm2target.canceromics.org/#/home\u003c/span\u003e\u003cspan address=\"http://rm2target.canceromics.org/#/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the \u003cem\u003eYTHDC2\u003c/em\u003e potential target genes. The GEPIA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/index.html\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze the relative expression levels of the target genes in breast cancer and adjacent tissues, and the differentially expressed genes were screened. Then, using GTEx v8 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/index.html\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to express quantitative trait loci (eQTL) analysis, evaluate candidate SNPs of nearby genes mRNA expression level in whole blood or breast tissue, the effect of Potential target genes of the predicted SNPs. Finally, the application of SRAMP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cuilab.cn/sramp\u003c/span\u003e\u003cspan address=\"http://www.cuilab.cn/sramp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online database prediction m\u003csup\u003e6\u003c/sup\u003eA modification sites of potential target genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistic Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and independent sample t test was used for comparison between groups. Categorical variables were expressed as frequency (%), and differences between groups were analyzed using Pearson's chi-square test. Conditional Logistic regression model was used to analyze the association between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and BC susceptibility, hormone receptor status and molecular typing of BC patients. Based on the dominant genetic model, stratified analysis was performed according to age and reproductive factors. All the above results were expressed as odds ratio (OR) and 95% confidence interval (95%CI). Subsequently, False positive report probability (FPRP) analysis was used to verify the accuracy of positive results and critical values\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.The SHEsis online software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://analysis.biox.cn/myAnalysis.php\u003c/span\u003e\u003cspan address=\"http://analysis.biox.cn/myAnalysis.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate whether each SNP was in Hardy-Weinberg equilibrium in the control group. HWE) law (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates that the control group is representative of the population) and haplotype analysis; Pearson's chi-square test was used to analyze the differences in genotype and allele frequency distribution between the case group and the control group. Two independent samples t-test was used to compare the expression levels of different \u003cem\u003eYTHDC1/2\u003c/em\u003e genotypes. Multifactor dimensionality reduction (MDR), classification and regression tree models were used to explore the best interaction effects of gene-environment factors. Multiple Logistic regression model was used to evaluate the multiplicative interaction (\u003cem\u003eINT\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e) between genes and environmental factors. Based on the forked analysis table, the ORint value calculated by the Logistic regression model was put into the formula prepared by Toms Anderson et al.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e to evaluate the additive interaction. The evaluation index included relative excess risk of interaction (\u003cem\u003eRERI\u003c/em\u003e), attributable proportion due to interaction (\u003cem\u003eAP\u003c/em\u003e) and synergy index (\u003cem\u003eS\u003c/em\u003e)\u003c/p\u003e \u003cp\u003eSPSS 26.0 and R 4.3.2 software were used for data analysis, and Adobe Illustrate 2023 and GraphPad Prism 8 software were used for graphic drawing. The level of statistical significance was set at a two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Subject Characteristics\u003c/h2\u003e \u003cp\u003eThis case-control study enrolled 746 breast cancer patients and 746 matched healthy controls, with baseline characteristics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age was 48.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29 years in cases and 48.96\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37 years in controls. Multivariate logistic regression analysis revealed significantly increased BC risk associated with age\u0026thinsp;\u0026ge;\u0026thinsp;45 years(\u003cem\u003eOR\u003c/em\u003e: 1.616, 95%\u003cem\u003eCI\u003c/em\u003e༚1.209\u0026ndash;2.160), two abortions(\u003cem\u003eOR\u003c/em\u003e༚1.752, 95%\u003cem\u003eCI\u003c/em\u003e༚1.195\u0026ndash;2.568), three or more abortions (\u003cem\u003eOR\u003c/em\u003e༚5.247, 95%\u003cem\u003eCI\u003c/em\u003e༚3.148\u0026ndash;8.747)and family history of cancer༈\u003cem\u003eOR\u003c/em\u003e༚1.475, 95%\u003cem\u003eCI\u003c/em\u003e༚1.098\u0026ndash;1.984༉. Women with menarche age\u0026thinsp;\u0026ge;\u0026thinsp;14 years (\u003cem\u003eOR\u003c/em\u003e: 0.619, 95%\u003cem\u003eCI\u003c/em\u003e: 0.496\u0026ndash;0.774) and menopause (\u003cem\u003eOR\u003c/em\u003e: 0.722, 95%\u003cem\u003eCI\u003c/em\u003e: 0.544\u0026ndash;0.959) had lower risk of BC. Compared with women who had no more than one pregnancy, women who had two, three and four or more pregnancies had a 58.5%, 67.2% and 74.9% lower risk of BC, respectively, and the risk decreased with the increase of the number of pregnancies (\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e\u0026lt;0.001). In addition, 445 cases (59.7%) were ER positive, 421 cases (56.4%) PR positive and 367 cases (49.2%) HER-2 positive in BC patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of breast cancer cases and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase (%) n\u0026thinsp;=\u0026thinsp;746\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (%) n\u0026thinsp;=\u0026thinsp;746\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e) \u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.96\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e466(62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478(64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.616 (1.209\u0026ndash;2.160)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256(34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.619 (0.496\u0026ndash;0.774)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.415 (0.283\u0026ndash;0.610)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.328 (0.213\u0026ndash;0.506)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.251 (0.155\u0026ndash;0.406)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313(42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.333 (0.984\u0026ndash;1.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.752 (1.195\u0026ndash;2.568)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.247 (3.148\u0026ndash;8.747)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast-feeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e717(96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e714(95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.058 (0.612\u0026ndash;1.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause statue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402(53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440(59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344(46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306(41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.722 (0.544\u0026ndash;0.959)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e647(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e612(82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.475 (1.098\u0026ndash;1.984)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445(59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421(56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer-2 receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e χ\u003csup\u003e2\u003c/sup\u003e test, two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is statistically.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test, two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is statistically.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e \u003csub\u003etrend\u003c/sub\u003e test, two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is statistically.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ed\u003c/sup\u003e Multivariate Logistic regression model adjusted by age, age of menarche, number of pregnancy, number of abortion, breast-feeding history, menopausal status and family history of tumors. Bold values mean significant results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The associations between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and BC susceptibility\u003c/h2\u003e \u003cp\u003eThe genotype and allele frequency distributions of the nine SNPs in the \u003cem\u003eYTHDC1/2\u003c/em\u003e gene are shown in Table S3. The results showed that the genotype \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) and allele (P\u0026thinsp;=\u0026thinsp;0.031) frequencies of rs9552 and genotypes (P\u0026thinsp;=\u0026thinsp;0.017) and allele (P\u0026thinsp;=\u0026thinsp;0.010) frequencies of rs2416282 were significantly different between BC cases and controls.\u003c/p\u003e \u003cp\u003eThe results of the HWE test in the control population and the association of \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs with BC susceptibility are shown in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All SNPs were consistent with HWE (all PHWE\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which confirmed that the selected controls were representative of the population. For \u003cem\u003eYTHDC2\u003c/em\u003e rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A, the AA genotype increased the risk of BC by 0.771 times (\u003cem\u003eOR\u003c/em\u003e: 1.771, 95%\u003cem\u003eCI\u003c/em\u003e: 1.186\u0026ndash;2.644) and 0.673 times (\u003cem\u003eOR\u003c/em\u003e: 1.673, 95%\u003cem\u003eCI\u003c/em\u003e: 1.673) in the codominant model and the recessive model, respectively. For \u003cem\u003eYTHDC2\u003c/em\u003e rs17135754 G\u0026thinsp;\u0026gt;\u0026thinsp;C, the CC genotype increased the risk of BC by 1.858 times(\u003cem\u003eOR\u003c/em\u003e: 2.858, 95%\u003cem\u003eCI\u003c/em\u003e༚1.077\u0026ndash;7.584)and 1.843 times༈\u003cem\u003eOR\u003c/em\u003e༚2.843, 95%\u003cem\u003eCI\u003c/em\u003e༚1.073\u0026ndash;7.535༉in the codominant model and the recessive model, respectively. For \u003cem\u003eYTHDC2\u003c/em\u003e rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A, in the codominant model, the CA genotype (\u003cem\u003eOR\u003c/em\u003e: 0.657, 95%\u003cem\u003eCI\u003c/em\u003e: 0.487\u0026ndash;0.886) and AA genotype (\u003cem\u003eOR\u003c/em\u003e: 0.601, 95%\u003cem\u003eCI\u003c/em\u003e: 0.439\u0026ndash;0.825) could reduce the risk of BC. In the dominant model, the CA\u0026thinsp;+\u0026thinsp;AA genotype could reduce the risk of BC by 36.6% (\u003cem\u003eOR\u003c/em\u003e: 0.634, 95%\u003cem\u003eCI\u003c/em\u003e: 0.478\u0026ndash;0.840). For the remaining SNPs, there was no significant association between different genotypes and breast cancer risk in the four types of models (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stratified analysis between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and BC risk\u003c/h2\u003e \u003cp\u003eBased on the dominant model, the stratification results of the 9 SNPs and BC susceptibility showed that for rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A, CA\u0026thinsp;+\u0026thinsp;CC genotype was associated with age\u0026thinsp;\u0026ge;\u0026thinsp;45 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), age at menarche\u0026thinsp;\u0026ge;\u0026thinsp;14 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), post-menopause (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), age at menopause\u0026thinsp;\u0026lt;\u0026thinsp;50 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), three pregnancies (P\u0026thinsp;=\u0026thinsp;0.001), 1 abortion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and breast-feeding history (P\u0026thinsp;=\u0026thinsp;0.002) P\u0026thinsp;=\u0026thinsp;0.003) and no family history of cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor rs17592288 A\u0026thinsp;\u0026gt;\u0026thinsp;C, AC\u0026thinsp;+\u0026thinsp;CC genotype decreased the risk of BC in the subgroup with 1 miscarriage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). For rs2293596 T\u0026thinsp;\u0026gt;\u0026thinsp;C, TC\u0026thinsp;+\u0026thinsp;CC genotype decreased the risk of BC in the subgroup of post-menopausal(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and family history of cancer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). For rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A, GA\u0026thinsp;+\u0026thinsp;AA genotype increased the risk of BC in the population with menarche age\u0026thinsp;\u0026ge;\u0026thinsp;14 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). For rs6594732 C\u0026thinsp;\u0026gt;\u0026thinsp;A, CA\u0026thinsp;+\u0026thinsp;AA genotype increased the risk of BC in the population with only one miscarriage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). However, there was no significant association between different genotypes of rs1715080 T\u0026thinsp;\u0026gt;\u0026thinsp;C, rs3813832 T\u0026thinsp;\u0026gt;\u0026thinsp;C, rs17135754 G\u0026thinsp;\u0026gt;\u0026thinsp;C and rs2303718 G\u0026thinsp;\u0026gt;\u0026thinsp;A and BC susceptibility (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The results are shown in Figures S3-S10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Haplotype analysis\u003c/h2\u003e \u003cp\u003eThe results of haplotype analysis are shown in Table S4. YTHDC1 haplotype C \u003csub\u003ers1715080\u003c/sub\u003e A \u003csub\u003ers17592288\u003c/sub\u003e T \u003csub\u003ers2293596\u003c/sub\u003e T \u003csub\u003ers3813832\u003c/sub\u003e was associated with an increased risk of BC (\u003cem\u003eOR\u003c/em\u003e: 1.287, 95%\u003cem\u003eCI\u003c/em\u003e: 1.002\u0026ndash;1.654). \u003cem\u003eYTHDC2\u003c/em\u003e haplotype G \u003csub\u003ers9552\u003c/sub\u003e G \u003csub\u003ers17135754\u003c/sub\u003e A \u003csub\u003ers2416282\u003c/sub\u003e C \u003csub\u003ers654732\u003c/sub\u003eG \u003csub\u003ers2303718\u003c/sub\u003e was associated with a reduced risk of BC (\u003cem\u003eOR\u003c/em\u003e: 0.824, 95%\u003cem\u003eCI\u003c/em\u003e: 0.712\u0026ndash;0.953).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Associations between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs with receptor status and molecular type in BC\u003c/h2\u003e \u003cp\u003eThe results of the association analysis of the nine \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs with BC hormone receptor status and molecular typing are shown in Tables S5-S6. BC patients with TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and TC\u0026thinsp;+\u0026thinsp;CC genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) of \u003cem\u003eYTHDC1\u003c/em\u003e rs1715080 were 37.4% and 31.8% less likely to be PR receptor positive, respectively. BC patients with TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and TC\u0026thinsp;+\u0026thinsp;CC genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) of \u003cem\u003eYTHDC1\u003c/em\u003e rs3813832 were 36.0% and 28.2% less likely to be PR receptor positive, respectively. BC patients with \u003cem\u003eYTHDC2\u003c/em\u003e rs17135754 CC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) were 71.0% less likely to be HER-2 positive.\u003c/p\u003e \u003cp\u003eBC patients with \u003cem\u003eYTHDC1\u003c/em\u003e rs1715080 TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and TC\u0026thinsp;+\u0026thinsp;CC genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) had a 41.2% and 38.6% lower likelihood of Luminal BC, respectively. The risk of HER-2 positive breast cancer in BC patients with TC\u0026thinsp;+\u0026thinsp;CC genotype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) increased by 0.655 times. BC patients with YTHDC1 rs3813832 TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and TC\u0026thinsp;+\u0026thinsp;CC genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) had a 41.1% and 37.5% lower likelihood of Luminal BC, respectively. BC patients with TC genotype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) had a 0.591 times higher risk of HER-2 positive BC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 False positive report probability analysis\u003c/h2\u003e \u003cp\u003eThe results of the FPRP analysis are shown in Table S7. When 0.25 was used as a test probability, the FPRP values of rs17135754 co-dominant model (GG/CC) and recessive model (GG\u0026thinsp;+\u0026thinsp;GC/CC) were 0.518 and 0.519, respectively, and the FPRP value of GC\u0026thinsp;+\u0026thinsp;CC genotype was 0.572. This indicates that these results may be false positives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Interaction analysis between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and reproductive factors\u003c/h2\u003e \u003cp\u003eThe results of MDR software analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The third-order model was the best, including three factors: Age at menarche, number of pregnancies and rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A, indicating an interaction between rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and environmental factors. Women with menarche age\u0026thinsp;\u0026ge;\u0026thinsp;14 years, number of pregnancies\u0026thinsp;\u0026ge;\u0026thinsp;2 and rs9552 A genotype had 2.070 times the risk of BC compared with the control population (\u003cem\u003eOR\u003c/em\u003e: 2.070, 95%\u003cem\u003eCI\u003c/em\u003e: 1.665\u0026ndash;2.572). CART model analysis results showed that three explanatory variables were screened out, including: age at menarche, number of pregnancies, and rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A, which verified the results of MDR (Figure S11).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction results between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and reproduction factors by MDR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBA\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCVC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.683 (1.352\u0026ndash;2.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche、Number of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.932 (1.554\u0026ndash;2.403)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche、Number of pregnancy、rs9552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.070(1.665\u0026ndash;2.572)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Testing balance accuracy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Cross-validation consistency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables S7-S15 show that there were negative multiplicative interactions between rs2293596 and family history of cancer, rs2416282 and age at menarche and menopausal status. There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. However, no significant additive interaction was observed between \u003cem\u003eYTHDC1/2\u003c/em\u003e SNPs and reproductive factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Relative expression of \u003cem\u003eYTHDC2\u003c/em\u003e in blood samples with different genotypes of SNP\u003c/h2\u003e \u003cp\u003eThe results of the qRT-PCR are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For rs9552 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), The relative expression level of \u003cem\u003eYTHDC2\u003c/em\u003e in the GG genotype was lower than that in the AA genotype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). For rs2416282 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The relative expression level of \u003cem\u003eYTHDC2\u003c/em\u003e in CC genotype was higher than that in CA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and AA genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Functional mechanisms of \u003cem\u003eYTHDC2\u003c/em\u003e SNPs predicted by bioinformatics analysis\u003c/h2\u003e \u003cp\u003eIn this study, a total of 5 potential target genes were Validated, including\u003cem\u003eCES2\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eCYLD\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eHOXA13\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eIGF1R\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eand \u003cem\u003eSLC7A11\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, However, no potential target genes of \u003cem\u003eYTHDC2\u003c/em\u003e were found in breast cancer cell lines( Table S16). Furthermore, according to the cut-off value of \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, there were two possible potential target genes \u003cem\u003eCES2\u003c/em\u003e and \u003cem\u003eIGFIR\u003c/em\u003e with different expression in BC and normal tissues (Figure S12). Then, according to the \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;10e-5, rs9552, rs17135754, rs2416282 and rs6594732 were all associated with the expression of \u003cem\u003eYTHDC2\u003c/em\u003e in whole blood and BC tissues (Figure S13). rs2303718 is associated with the expression of \u003cem\u003eYTHDC2\u003c/em\u003e in whole blood tissues. No target-genes regulated by other \u003cem\u003eYTHDC2\u003c/em\u003e SNPs were identified.\u003c/p\u003e \u003cp\u003eUsing the SRAMP website, it was found that \u003cem\u003eCES2\u003c/em\u003e (65) and \u003cem\u003eIGFIR\u003c/em\u003e (15) had potential m\u003csup\u003e6\u003c/sup\u003eA modification sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The RNA secondary structure prediction maps of m\u003csup\u003e6\u003c/sup\u003eA modification sites of the two target genes with Very high confidence are shown in Figure S14. Based on the above prediction results, we hypothesized that \u003cem\u003eYTHDC2\u003c/em\u003e may regulate target-genes by recognizing and binding to their m6A modification sites, thereby affecting breast cancer susceptibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results of the current case-control study showed that rs9552G\u0026thinsp;\u0026gt;\u0026thinsp;A and rs2416282C\u0026thinsp;\u0026gt;\u0026thinsp;T were associated with BC risk. qRT-PCR results showed that rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;T had significant effects on the expression of YTHDC2, respectively. Therefore, we hypothesized that these two SNPS may alter the expression level of \u003cem\u003eYTHDC2\u003c/em\u003e in blood, thereby affecting BC. Furthermore, the potential functional mechanism of \u003cem\u003eYTHDC2\u003c/em\u003e was predicted by bioinformatics, and it was found that \u003cem\u003eYTHDC2\u003c/em\u003e SNPs may identify and regulate the target gene by combining with the m\u003csup\u003e6\u003c/sup\u003eA modification site of the potential target gene, thereby affecting the susceptibility to BC. Our findings identify novel BC risk biomarkers, enabling targeted screening and precision prevention strategies for high-risk populations with specific genetic and reproductive profiles.\u003c/p\u003e \u003cp\u003eTanabe et al. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e found that \u003cem\u003eYTHDC2\u003c/em\u003e promoted the malignant phenotype of BC cells and that there was a significant positive correlation between \u003cem\u003eYTHDC2\u003c/em\u003e expression level and BC stage. AA genotype of \u003cem\u003eYTHDC2\u003c/em\u003e rs9552 was significantly associated with an increased risk of BC. CA\u0026thinsp;+\u0026thinsp;AA of rs2416282 was associated with a reduced risk. This may be because the variations of rs9552 and rs2416282 affect the function of \u003cem\u003eYTHDC2\u003c/em\u003e in the process of RNA stability, splicing, transport and translation, interfere with the m\u003csup\u003e6\u003c/sup\u003eA modification and expression level of tumor-related genes, and then regulate the proliferation, invasion and migration of tumor cells. At present, no study has been reported on \u003cem\u003eYTHDC1\u003c/em\u003e gene polymorphisms and disease risk, and no significant association between \u003cem\u003eYTHDC1\u003c/em\u003e SNPs and BC risk was found in the present study.\u003c/p\u003e \u003cp\u003eThe classification of BC relies on immunohistochemical detection of ER, PR, HER-2, and Ki-67 expression in tumor tissue, categorizing cases into Luminal A/B, HER-2 positive and TNBC\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that ER and PR positive patients have a better prognosis after receiving hormone therapy\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. At present, the prognosis of HER-2 positive BC has been significantly improved thanks to the application of HER-2 targeted drugs (such as trastuzumab) \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.However, the problem of HER-2 positive BC resistance to targeted therapy still exists, and some patients may not receive optimal treatment due to treatment side effects or economic burden\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.Luminal type BC expresses ER and/or PR and is sensitive to endocrine therapy, but Luminal B type has a higher proliferation index and poor prognosis and may require combined chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Therefore, the discovery of HER-2 positive and Luminal BC risk biomarkers is of great clinical importance. The results of our study suggest that rs1715080 and rs3813832 may be used as risk biomarkers for HER-2 positive BC, which may help to achieve early identification, diagnosis and treatment of HER-2 positive BC.\u003c/p\u003e \u003cp\u003eThe occurrence of BC is affected by many environmental factors. Studies found that increasing age at menarche reduced the risk of BC \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The risk of BC in pre-menopausal women was higher than that in menopausal women of the same age (\u003cem\u003eRR\u003c/em\u003e: 1.029, 95%\u003cem\u003eCI\u003c/em\u003e: 1.025\u0026ndash;1.023) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Women who become pregnant for the first time before the age of 20 can reduce the risk of BC, but women who at the age of 35 May increase the risk of BC \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Another study found that full-term pregnancy can reduce a woman's long-term risk of BC, and no reduction was observed for pregnancies lasting 33 weeks or less \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Therefore, factors such as age at first pregnancy and duration of pregnancy closely affect the association between pregnancy and BC susceptibility. The results of a Chinese case-control study showed that women had a significantly increased risk of BC in those who had experienced at least one medical abortion compared with those who had never had an induced abortion\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Results from a cohort study of Korean women showed that after adjusting for multiple confounding factors, family history of BC was significantly associated with an increased risk of BC\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The above conclusions are consistent with the results of this study. Therefore, we recommend regular BC screening for women with advanced maternal age at first pregnancy or preterm delivery, and prioritized screening/education for those with abortion history or familial cancer risk, to enable early detection and risk reduction.\u003c/p\u003e \u003cp\u003eAs a complex disease, BC is induced by both genetic and environmental factors. A genome-wide gene-environment interaction study of BC in more than 90,000 women identified a crosstalk between predicted expression of the \u003cem\u003eC13orf45\u003c/em\u003e gene and age at first full-term pregnancy (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eGXE\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.001)\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The results of a case-control study by Mmadili et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e showed that AKT1 rs10138227 (C\u0026thinsp;\u0026gt;\u0026thinsp;T) was associated with Q2 of energy intake (\u003cem\u003eP-interaction\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), \u003cem\u003eHIF-1α C1772T\u003c/em\u003e rs11549465(C\u0026thinsp;\u0026gt;\u0026thinsp;T) and Q4༈ \u003cem\u003eP-interaction\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007༉were associated with a decreased risk of BC in overall women. Rudolph et al.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e found an interaction between the number of full-term pregnancies and rs4808801, as well as between smoking and rs11242675. In our study, first MDR results showed that the interaction between rs9552 A genotype, age at menarche, and number of pregnancies could increase the risk of BC. The classification and regression tree model screened these three key variables and verified the MDR results. In addition, there were negative multiplicative interactions between rs2293596 and family history of cancer, and between rs2416282 and menarche age and menopausal status by multiplicative model and additive effect\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. Therefore, BC can be prevented in daily life by regulating environmental factors, such as reducing the number of abortions, thereby reducing the independent influence of environmental factors and their interaction with genetic factors.\u003c/p\u003e \u003cp\u003eIn this study, we first found that rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A of \u003cem\u003eYTHDC2\u003c/em\u003e were associated with BC risk. Further qRT-PCR experiments were performed, and the relative expression of \u003cem\u003eYTHDC2\u003c/em\u003e in blood samples with rs9552 GG genotype was lower than that with AA genotype. The relative expression of rs2416282 CC genotype was higher than that of CA and AA genotype. Therefore, in this study, the potential functional mechanism of \u003cem\u003eYTHDC2\u003c/em\u003e was further predicted by bioinformatics. Studies have shown that \u003cem\u003eYTHDC2\u003c/em\u003e specifically recognizes and binds to m6A modification sites on mRNA through its YTH domain, and \u003cem\u003eYTHDC2\u003c/em\u003e regulates RNA function by binding to these sites \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In some cases, \u003cem\u003eYTHDC2\u003c/em\u003e may also enhance mRNA stability by inhibiting its degradation\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.Ma et al.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e pointed out that \u003cem\u003eYTHDC2\u003c/em\u003e can recognize the m6A modification site on the 3'-UTR of \u003cem\u003eHOXA13\u003c/em\u003e in lung adenocarcinoma cells, destroy the stability of \u003cem\u003eHOXA13\u003c/em\u003e, inhibit the expression of \u003cem\u003eHOXA13\u003c/em\u003e, and the expression level of its downstream target gene \u003cem\u003eSLC3A2\u003c/em\u003e is also inhibited, thereby causing ferroptosis and inhibiting the occurrence and development of lung adenocarcinoma. In this study, two potential target genes of m6A reader \u003cem\u003eYTHDC2\u003c/em\u003e were screened by online database, and the above target genes were predicted by the website to have potential m\u003csup\u003e6\u003c/sup\u003eA modification sites to stabilize the target mRNA, thereby affecting the susceptibility of BC.\u003c/p\u003e \u003cp\u003eThis study was a large case-control study. A total of 746 newly diagnosed BC cases and 746 healthy controls were included, and all the included cases were pathologically confirmed BC, which reduced the prevalence-incidence bias. Data were collected by investigators with standardized training, which reduced the information bias caused by subjective factors. This study is the first to report the association between \u003cem\u003eYTHDC2\u003c/em\u003e rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and \u003cem\u003eYTHDC2\u003c/em\u003e rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A and the risk of BC. However, the results of this study also have some limitations. Retrospective data may have reporting bias, which needs to be further verified by a large prospective cohort. The sample of this study was from the Chinese Han population, and it needs to be verified in other ethnic or regional populations. In addition, this study did not carry out experimental verification at the cell level, and only through the public platform database and bioinformatics prediction, the potential target genes and potential mechanisms of the SNPS found in this study that were related to the risk of BC were predicted.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e \u003cem\u003eYTHDC2\u003c/em\u003e rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A was significantly associated with an increased risk of BC. \u003cem\u003eYTHDC2\u003c/em\u003e rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A was significantly associated with a reduced risk of BC. \u003cem\u003eYTHDC2\u003c/em\u003e SNPs may identify and bind to m6A modification sites of potential target genes to regulate target genes, and then affect BC susceptibility, but this association needs further prospective cohort studies and cell experiments to verify.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Henan Province Science and Technology Research Project (252102311033)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaoqing Cheng:\u003c/strong\u003e Writing \u0026ndash; original draft, Formal analysis, Conceptualization. \u003cstrong\u003ePengxia Guo\u003c/strong\u003e: Data curation. \u003cstrong\u003eChuying Zhang\u003c/strong\u003e: Data curation.\u0026nbsp;\u003cstrong\u003eGege Zhang\u003c/strong\u003e: Data curation. \u003cstrong\u003eZhilin Zhang\u003c/strong\u003e: Data curation.\u003cstrong\u003e\u0026nbsp;Shiyuan Wang\u003c/strong\u003e:\u0026nbsp;Data curation. \u003cstrong\u003eSaba Fida\u003c/strong\u003e:\u0026nbsp;Data curation.\u0026nbsp;\u003cstrong\u003eChunhua Song\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the appropriate ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLoibl S, Poortmans P, Morrow M, et al. 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J Oncol. 2022;2022:9188920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff AC, Hammond ME, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update[J]. J Clin Oncol. 2013;31(31):3997\u0026ndash;4013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: Past, present and future[J]. Semin Cancer Biol. 2018;52(Pt 1):56\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies C, Godwin J, Gray R, et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials[J]. 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Cell Res. 2017;27(3):315\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Case-control, m6A, YTHDC1, YTHDC2, Single nucleotide polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-6511788/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6511788/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eN6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA), the most abundant mRNA modification in eukaryotes, is critical in cancer development. As key m\u003csup\u003e6\u003c/sup\u003eA reader, \u003cem\u003eYTHDC1\u003c/em\u003e and \u003cem\u003eYTHDC2\u003c/em\u003e may affect breast cancer risk. This study investigates the association of single nucleotide polymorphisms (SNPs) of \u003cem\u003eYTHDC1/2\u003c/em\u003e with breast cancer susceptibility and their interactions with environmental factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFunctional SNPs of \u003cem\u003eYTHDC1/2\u003c/em\u003e were screened through literature and public databases, yielding nine candidates. In a frequency-matched case-control study (n\u0026thinsp;=\u0026thinsp;746; age\u0026thinsp;\u0026plusmn;\u0026thinsp;2 years), their association with breast cancer susceptibility was evaluated using conditional logistic regression, followed by gene-environment interactions analysis. qRT-PCR measured expression of \u003cem\u003eYTHDC2\u003c/em\u003e in blood across rs9552 and rs2416282 genotypes. Public databases were further used to explore the potential target genes and functional mechanisms of \u003cem\u003eYTHDC2\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eYTHDC2\u003c/em\u003e rs9552G\u0026thinsp;\u0026gt;\u0026thinsp;A was associated with an increased risk of breast cancer. \u003cem\u003eYTHDC2\u003c/em\u003e rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A was associated with a reduced risk of breast cancer. \u003cem\u003eYTHDC1\u003c/em\u003e haplotype C \u003csub\u003ers1715080\u003c/sub\u003e A \u003csub\u003ers17592288\u003c/sub\u003e T \u003csub\u003ers2293596\u003c/sub\u003e T \u003csub\u003ers3813832\u003c/sub\u003e was associated with an increased risk of breast cancer. \u003cem\u003eYTHDC2\u003c/em\u003e haplotype G \u003csub\u003ers9552\u003c/sub\u003e G \u003csub\u003ers17135754\u003c/sub\u003e A \u003csub\u003ers2416282\u003c/sub\u003e C \u003csub\u003ers654732\u003c/sub\u003eG \u003csub\u003ers2303718\u003c/sub\u003e decreased the risk of breast cancer. rs1715080 and rs3813832 were associated with PR receptor status, Luminal type and HER-2 positive breast cancer, and rs17135754 was associated with HER-2 positive breast cancer. Negative multiplication interactions were observed between rs2293596 and family history of cancer, rs2416282 and menarche age and menopausal status. There were positive multiplication interactions between rs9552 and age at menarche, rs2416282 and family history of cancer. qRT-PCR results showed that the relative expression of \u003cem\u003eYTHDC2\u003c/em\u003e was changed by rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A. \u003cem\u003eYTHDC2\u003c/em\u003e SNPs may identify and bind to the m6A modification sites of potential target genes to regulate the target genes, and then affect the susceptibility of breast cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings suggested that rs9552 G\u0026thinsp;\u0026gt;\u0026thinsp;A and rs2416282 C\u0026thinsp;\u0026gt;\u0026thinsp;A might be associated with the risk of BC.\u003c/p\u003e","manuscriptTitle":"Association of functional genetic variants in the N6-methyladenosine reader protein YTHDC1/2 with breast cancer susceptibility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 10:41:55","doi":"10.21203/rs.3.rs-6511788/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d6c9f7e-b6df-4e3c-b6f8-2b3703ac8377","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-25T00:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 10:41:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6511788","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6511788","identity":"rs-6511788","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}