Association of SMAD4 Gene Polymorphisms with Human Papillomavirus Infection and Risk of Cervical Cancer: A Case-Control Study

Association of SMAD4 Gene Polymorphisms with Human Papillomavirus Infection and Risk of Cervical Cancer: A Case-Control Study | 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 SMAD4 Gene Polymorphisms with Human Papillomavirus Infection and Risk of Cervical Cancer: A Case-Control Study Yongliang Wang, Ying Deng, Ying Wei, Chunfang Wang, YuanJi Teng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7771252/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in BMC Cancer → Version 1 posted 24 You are reading this latest preprint version Abstract Objective To investigate the association between SMAD4 gene single nucleotide polymorphisms (SNPs) and the risk of human papillomavirus (HPV) infection and cervical cancer. Methods A case-control study was conducted, enrolling 342 cervical cancer patients as the case group and 342 healthy individuals from concurrent physical examinations as the control group. Genotyping of SMAD4 SNP loci rs34468925 and rs8084630 was performed using the imLDR SNP genotyping technique. HPV detection in the case group was performed via PCR-reverse dot hybridization. Statistical analysis and the online SHEsis software were employed to comprehensively evaluate the association between the rs34468925 and rs8084630 SNP loci, their haplotypes, and the risk of HPV infection and cervical cancer. Results No significant association was observed between the genotypes, alleles, genetic models (dominant and recessive), or haplotypes of the SMAD4 gene SNPs rs34468925 and rs8084630 and HPV infection risk in either group (P > 0.05). rs8084630 showed no association with cervical cancer risk (P > 0.05). However, the TT genotype, T allele, and recessive genetic model of rs34468925 may be associated with cervical cancer risk (P < 0.05). Additionally, the rs34468925 CT genotype may be associated with clinical pathological characteristics (tumor classification, distant metastasis) in cervical cancer patients (P < 0.05). Conclusion The SMAD4 gene SNP site rs34468925 shows no significant association with HPV infection but may be associated with cervical cancer risk and clinical pathological characteristics. SMAD4 Single nucleotide polymorphism Human papillomavirus Cervical cancer Figures Figure 1 1. Introduction Globally, cervical cancer ranks among the most common cancers affecting women. To date, approximately 99.7% of cervical cancer cases are caused by persistent infection with high-risk human papillomavirus (HR-HPV) ( 1 ). The SMAD4 gene (also known as DPC4) is a member of the SMAD family and serves as a central mediator of transforming growth factor beta (TGF-β). It participates in regulating the expression of numerous genes associated with cancer initiation and progression, including apoptosis, proliferation, and inflammation ( 2 , 3 ). Research has identified an association between cervical neuroendocrine carcinoma (NECC) and HR-HPV infection, with SMAD4 gene mutations occurring in approximately 20% of NECC cases. However, this study did not reveal a correlation between HPV genotype and SMAD4 gene mutation ( 4 ). Evidently, the relationship between HPV, the SMAD4 gene, and cervical cancer has attracted significant scholarly attention. Therefore, this study employs a case-control approach to investigate the association between SMAD4 single nucleotide polymorphisms (SNPs) and the risk of HPV infection and cervical cancer. 2. Subjects and Methods 2.1 Study Population A total of 342 patients diagnosed with cervical cancer at our hospital between January 2022 and June 2024 were enrolled. Their ages ranged from 23 to 79 years, with a mean age of (50.60 ± 9.473) years. Inclusion criteria for patients were: ⑴ Diagnosis of cervical cancer based on both clinical features and histopathological examination. ⑵ No prior history of tumors; no prior radiotherapy or chemotherapy. ⑶ No significant impairment of vital organ function (cardiac, cerebral, hepatic, renal). ⑷ Normal mental status; no history of immune disorders, psychiatric conditions, or familial cancer. A control group of 342 healthy women undergoing routine physical examinations during the same period was selected, aged 23–82 years, with a mean age of (49.85 ± 9.524) years. Inclusion criteria for controls were: ⑴ No blood relationship among included individuals. ⑵ No prior history of tumors; routine blood test results within normal ranges. ⑶ Negative HPV test results. ⑷ Normal mental status; no history of other major chronic diseases. All study participants provided informed consent. This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Ethics No.: YYFY-LL-2022-58). Written informed consent was obtained from all participants prior to inclusion in the study. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. 2.2 Methods 2.2.1 SNP Selection Single nucleotide polymorphism loci of the SMAD4 gene were screened from the HapMap database ( http://hapmap.ncbi.nlm.nih.gov ). The selection criteria were a minor allele frequency (MAF) > 0.05 and linkage disequilibrium coefficient (r 2 ) > 0.8. In addition, considering SNPs potentially related to disease as reported in the literature, two loci (rs34468925 and rs8084630) were ultimately chosen for analysis. 2.2.2 SNP Genotyping Genotyping of the SNP loci was performed on all 684 samples using an imLDR™ multiplex SNP genotyping kit (Shanghai Tianhao Biotechnology). One microliter of extracted DNA was subjected to 0.01% agarose gel electrophoresis for quality assessment and concentration estimation. Samples were diluted to a working concentration of 5 ng/µL based on estimated concentration. PCR amplification of target fragments was carried out using primers for SMAD4 designed with Premier 5.0 (synthesized by Shanghai Tianhao Biotechnology). The PCR reaction mixture (20 µL) contained 1× GC-I buffer (Takara), 2 U 3.0 mM Mg 2+ , 0.3 mM dNTPs, 1 U HotStar Taq DNA polymerase (Qiagen Inc.), 1 µL of template DNA, and 1 µL of multiplex PCR primer. The PCR cycling program was: 95°C for 2 min; 11 cycles of (94°C for 20 s, 66°C cycle for 40 s, 72°C for 1 min); 24 cycles of (94°C for 20 s, 60°C for 30 s, 72°C for 1 min); a final extension at 72°C for 2 min; 4°C indefinitely. Multiplex PCR products were purified by adding 5 U SAP and 2 U Exonuclease I enzyme to 20 µL of PCR product, incubating at 37°C for 60 min, and then heat-inactivating at 75°C for 15 min. Ligation reactions were prepared in a 10 µL system containing 1 µL 10× ligation buffer, 0.25 µL heat-resistant ligase, 0.4 µL 5′ ligation primer mix (1 µM), 0.4 µL 3′ ligation primer mix (2 µM), 2 µL purified PCR product, and 6 µL ddH₂O. Ligation was carried out for 38 cycles of (94°C for 1 min, 56°C for 4 min). For product detection, 1 µL of the diluted ligation product was mixed with 0.1 µL Liz500 size standard and 9 µL Hi-Di. The mixture was denatured at 95°C for 5 min and then run on an ABI 3730XL DNA sequence. Sequencing files were analyzed using Polyphred software, manually proofread, and compiled into results (gene sequencing information shown in Table 1 ). Table 1 rs34468925 and rs8084630 information and PCR primers SNP Position Primers rs34468925 48554285 Upstream 5'-CRTTGGTCTCTCCTAAGCCCCAAT-3' Downstream 5'-CACCTCTCAAGTAGTGGCAACTGTGT-3' Linking Primer: RC: 5'-TTCCGCGTTCGGACTGATATTTAGTTTCCTGATCCTTGACCTCCACG-3' RF: 5'-TCTTCTCCAGTTATTTTCTGTTTGGACTACTT-3' RT: 5'-TACGGTTATTCGGGCTCCTGTTTAGTTTCCTGATCCTTGACCTCCACA-3' rs8084630 48577091 Upstream 5'-CCACTGTCATCTTAGAAAGGTGTGCT − 3' Downstream 5'-AGGCAGTGAGTCTTCCTCCATTACC − 3' Primer connection: RA: 5'-TACGGTTATTCGGGCTCCTGTTGTGCTCTTGTTCTGGAGGGGAA-3' RC:5'- TTCCGCGTTCGGACTGATATTGTGCTCTTGTTCTGGAGGGAAG-3' RF: 5'- TTAGTAGTAGTTTAGCACCTTTTTTGTTTGTAGAATTTTTTT-3' 2.2.3 HPV Genotyping PCR-reverse dot hybridization was employed using specific primers designed by Aneng Biotechnology (Shenzhen) for the HPV genome. Potential HPV genotypes in the test samples underwent PCR amplification and biotin labeling. The PCR products were then subjected to specific molecular hybridization with 23 specific probes immobilized on the chip, with results interpreted via chemical color development. The kit used in this study detects 23 HPV genotypes, including 17 high-risk types: HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82; and 6 low-risk types: HPV6, 11, 42, 43, 81, and 83. 2.2.4 Statistical Analysis Experimental data were analyzed using SPSS 24.0 statistical software. Binary logistic regression analysis was employed to examine the distribution frequency of rs34468925 genotypes, alleles, genetic models across clinical characteristics, distribution frequencies of gene and haplotype models. The chi-square test (χ ²) was used to examine the distribution frequency of rs34468925 genotypes across clinical characteristics. A t-test analyzed age level differences among subjects. Online SHEsis software performed joint haplotype analysis for rs34468925 and rs8084630. A significance level of P < 0.05 was considered statistically significant. 3. Results 3.1 rs8084630 and rs34468925 genotypes The study results showed that rs8084630 genotypes included AA, GA, and GG. rs34468925 genotypes comprised CC, CT, and TT (Fig. 1 ). 3.2 Correlation analysis of rs34468925 and rs8084630 with HPV infection risk This study categorized cervical cancer subjects into high-risk HPV infection (HR-HPV+) and low-risk HPV infection (HR-HPV-) groups. We analyzed the distribution frequencies of genotypes and alleles at rs34468925 and rs8084630 between the two groups to explore their association with HPV infection risk. Results showed no significant differences in the genotypes, alleles, or genetic models (dominant, recessive) of rs34468925 and rs8084630 between the high-risk and low-risk HPV infection groups. The results suggest that rs34468925 and rs8084630 SNPs are not significantly associated with HPV infection risk (P > 0.05). (Table 2 ). Table 2 Correlation analysis of rs34468925 and rs8084630 with the risk of HPV infection [n (%)] SNP HR-HPV (+) HR-HPV (-) χ2 P OR (95%CI) rs34468925 CC 62(40.8) 75(44.6) - - 1.00 CT 66(43.4) 66(39.3) 0.607 0.436 0.827(0.512–1.335) TT 24 (15.8) 27(16.1) 0.049 0.825 0.930(0.488–1.772) TT + CT vs CC 90(59.2) 93(55.4) 0.484 0.487 1.171(0.751–1.825) CC + CT vs TT 128(84.2) 141(83.9) 0.005 0.945 1.021(0.561–1.860) C 190(62.5) 216(64.3) - - 1.00 T 114(37.5) 120(35.7) 0.219 0.639 0.926(0.671–1.278) rs8084630 GG 51(33.6) 62(36.9) - - 1.00 GA 70(46.0) 72(42.9) 0.437 0.508 0.846(0.515–1.389) AA 31(20.4) 34(20.2) 0.109 0.742 0.902(0.489–1.663) AA + GA vs GG 101(66.4) 106(63.1) 0.393 0.531 1.158(0.731–1.835) GG + GA vs AA 121(79.6) 134(79.8) 0.001 0.972 0.990(0.574–1.708) A 132(43.4) 140(41.7) 0.201 0.654 0.931(0.680–1.274) G 172(56.6) 196(58.3) - - 1.00 3.3 Association of rs34468925 and rs8084630 with cervical cancer risk This study analyzed the genotype and allele distribution of rs34468925 and rs8084630 in cervical cancer patients and healthy controls to explore their potential association with cervical cancer risk. The TT genotype and T allele of rs34468925 were more frequent in cervical cancer cases than in healthy controls. The recessive model CC + CT vs. TT was less common in the cervical cancer group than in controls, with both differences being statistically significant ( P < 0.05). Results indicate that the TT genotype is negatively correlated with cervical cancer risk. The lower frequency of the T allele in the control group suggests its association with reduced cervical cancer risk. In contrast, the distribution frequency of rs8084630 showed no statistically significant difference between the two groups (P > 0.05), indicating no apparent correlation with cervical cancer risk at this locus (Table 3 ). Table 3 Comparison of rs34468925 and rs8084630 genotype frequencies between cervical cancer patients and controls [n (%)] Genotypes Cervical cancer (n = 342) Control (n = 342) P OR (95%CI) P a OR (95%CI) a rs34468925 CC 143(41.8) 158(46.2) - 1.00 - 1.00 CT 146(42.7) 150(43.9) 0.657 0.930(0.674–1.282) 0.632 0.925(0.670–1.275) TT 53(15.5) 34(9.9) 0.028 0.581(0.357–0.944) 0.027 0.576(0.354–0.938) TT + CT vs CC 199(58.2) 184(53.8) 0.248 0.837(0.619–1.132) 0.233 0.832(0.615–1.126) CC + CT vs TT 289(84.5) 308(90.1) 0.030 0.602(0.380–0.953) 0.029 0.599(0.378–0.949) C 432(63.2) 466(68.1) - 1.00 - 1.00 T 252(36.8) 218(31.9) 0.053 0.802(0.641–1.003) 0.049 0.798(0.638–0.999) rs8084630 GG 118(34.5) 129(37.7) - 1.00 - 1.00 GA 154(45.0) 153(44.7) 0.576 0.909(0.650–1.271) 0.563 0.906(0.648–1.267) AA 70(20.5) 60(17.6) 0.263 0.784(0. 512 − 1.200) 0.253 0.780(0.509–1.194) GA + AA vs GG 224(65.5) 213(62.3) 0.381 0.870(0.637–1.189) 0.369 0.866(0.634–1.184) GG + GA vs AA 272(79.5) 282(82.5) 0.330 0.827(0.564–1.213) 0.322 0.824(0.562–1.029) G 390(57.0) 411(60.1) - 1.00 - 1.00 A 294(43.0) 273(39.9) 0.249 0.8881(0.710–1.093) 0.239 0.878(0.708–1.090) Note: a represents P and OR adjusted for age 2.4 Association of rs34468925 with clinical and pathological characteristics in cervical cancer patients Analysis of the data in the above table suggests a potential association between the rs34468925 locus and cervical cancer risk. Therefore, we further investigated whether it correlates with the clinical and pathological characteristics of the subjects in this study. Results showed that the CT genotype at rs34468925 was significantly more frequent in squamous cell carcinoma than in adenocarcinoma. Additionally, the CT genotype exhibited a statistically significant difference in distant metastasis occurrence (P = 0.036, OR = 3.252), indicating that carriers of the CT genotype had a 3.25-fold higher risk of distant metastasis compared to non-carriers, suggesting an increased risk of metastasis. (Table 4 ) Table 4 Correlation between rs34468925 genotypes and clinicopathological features of cervical cancer patients [n (%)] Variable Category χ2 P OR (95%CI) Tumor type Squamous Adenocarcinoma Other CC 103(38.6) 28(53.8) 12(52.2) - - 1.00 CT 121(45.3) 17(32.7) 8(34.8) 3.956 0.047 0.517(0.268–0.997) TT 43(16.1) 7(13.5) 3(13.0) 1.262 0.261 0.599(0.243–1.475) C 327(61.2) 73(70.2) 32(69.6) - - 1.00 T 207(38.8) 31(29.8) 14(30.4) 2.986 0.084 0.671(0.426–1.057) Tumor grade Ⅰ Ⅱ Ⅲ CC 35(40.7) 66(41.3) 42(43.8) - - 1.00 CT 38(44.2) 68(42.5) 40(41.7) 0.171 0.918 - TT 13(15.1) 26(16.2) 14(14.5) 0.188 0.910 - C 108(62.8) 200(62.5) 124(64.6) - - 1.00 T 64(37.2) 120(37.5) 68(35.4) 0.237 0.888 - Tumor grade Ⅰ + Ⅱ Ⅲ + Ⅳ CC 92(41.4) 51(42.5) - - 1.00 CT 100(45.1) 46(38.3) 0.560 0.454 0.830(0.509–1.353) TT 30(13.5) 23(19.2) 0.984 0.409 1.383(0.728–2.628) C 284(64.0) 148(61.7) 1.00 T 160(36.0) 92(38.3) 0.353 0.552 1.103(0.798–1.526) Lymph node metastasis Yes No CC 41(50.0) 102(39.2) - - 1.00 CT 30(36.6) 116(44.6) 2.572 0.109 1.554(0.905–2.669) TT 11(13.4) 42(16.2) 1.243 0.265 1.535(0.720–3.270) C 112(68.3) 320(51.0) - - 1.00 T 52(31.7) 200(49.0) 2.444 0.118 1.346(0.927–1.955) Distant metastasis Yes No CC 12(57.1) 131(40.8) - - 1.00 CT 4(19.1) 142(44.2) 4.413 0.036 3.252(1.023–10.334) TT 5(23.8) 48(15.0) 0.053 0.818 0.879(0.294–2.627) C 28(45.2) 404(62.9) - - 1.00 T 14(54.8) 238(37.1) 0.237 0.627 1.178(0.608–2.282) 2.5 Analysis of the association between rs34468925 and rs8084630 haplotypes and cervical cancer risk We conducted a haplotype analysis combining rs34468925 and rs8084630 to explore any synergistic effect of these loci in cervical carcinogenesis. SHEsis analysis identified four haplotypes formed by rs34468925 and rs8084630: C–A, C–G, T–A, and T–G (where the first letter represents the allele at rs34468925 and the second letter the allele at rs8084630). The C–G haplotype was the most common, accounting for 56.4% in the cervical cancer group and 59.9% in the control group (OR = 0.874). This suggests a possible protective trend for the C–G haplotype, though the difference was not statistically significant (P > 0.05). Notably, the T–A haplotype was more frequent in the cervical cancer group than in controls (36.2% vs. 31.7%). Although this difference did not reach conventional significance (P = 0.068), the OR for the T–A haplotype was 1.232, which is close to significance. This implies that the T–A haplotype might be associated with a higher risk of cervical cancer and could have potential predictive value. (Table 5 ) Table 5 Comparison of rs34468925 and rs8084630 haplotype analysis between cervical cancer group and controls [n (%)] Haplotype Cervical cancer Control χ2 P OR (95%CI) C-A 46(6.8) 56(8.2) 0.97 0.324 0.8116(0.545–1.223) C-G 386(56.4) 410(59.9) 1.51 0.220 0.874(0.704–1.084) T-A 248(36.2) 217(31.7) 3.33 0.068 1.232(0.984–1.542) T-G 4(0.6) 1(0.2) - - - 2.6 Analysis of the association between rs34468925 and rs8084630 haplotypes and HR-HPV infection risk To further investigate whether rs34468925 and rs8084630 are associated with HPV infection in a combined genetic context, this study analyzed the distribution differences of four haplotypes in the HPV-infected population. SHEsis analysis revealed that the C-G haplotypes of rs34468925 and rs8084630 were most prevalent in the high-risk HPV infection group (HR-HPV+) and low-risk HPV infection group (HR-HPV-), accounting for 55.9% and 57.7%, respectively. The distribution differences of the four haplotypes between the two groups were not statistically significant (P > 0.05), indicating that rs34468925 and rs8084630 haplotypes are not associated with HPV infection risk (Table 6 ). Table 6 Analysis of the relationship between rs34468925 and rs8084630 haplotypes and HPV infection [n (%)] Haplotype HR-HPV (+) HR-HPV (-) χ2 P OR (95%CI) C-A 20(6.6) 22(6.6) 0.01 0.983 1.007(0.539–1.881) C-G 170(55.9) 194(57.7) 0.21 0.648 0.929(0.678–1.273) T-A 112(36.8) 118(35.1) 0.21 0.646 1.079(0.780–1.492) T-G 2 (0.7) 2 (0.6) - - - 4. Discussion HPV is a small, circular, double-stranded DNA virus belonging to the Papovaviridae family, exhibiting strict genotype-specific host specificity ( 5 ). Globally, approximately 5% of all cancers are associated with HPV infection ( 6 ). Virtually all cases of cervical cancer can be attributed to persistent infection with high-risk HPV (HR-HPV) ( 7 ). Reduced SMAD4 gene expression is common in tumors, often accompanied by mutations, loss of replication, and transcriptional downregulation ( 8 , 9 ). Studies confirm that reduced SMAD4 expression in immunohistochemistry correlates with poorer survival in lung and pancreatic cancers ( 10 , 11 ). SMAD4 deficiency in animal models induces tumorigenesis, promotes oncogene activation, accelerates disease progression, and stimulates tumor metastasis ( 12 , 13 ). Increasing evidence demonstrates an association between HPV infection and SMAD4 gene regulation. This study systematically analyzed the genotypes, allele frequencies, and haplotype compositions of two SMAD4 gene single nucleotide polymorphisms (SNPs)—rs34468925 and rs8084630—in cervical cancer patients and healthy controls, further investigating their association with HPV infection and cervical cancer risk. In the association analysis with cervical cancer, the TT genotype at rs34468925 was significantly negatively correlated with cervical cancer risk, suggesting a potential protective effect of this gene. The T allele frequency was higher in the case group, showing a marginally significant negative correlation with cervical cancer risk. These findings indicate that the TT genotype or T allele at this locus may reduce cervical cancer risk to some extent, meaning individuals carrying the TT genotype have a lower risk of developing cervical cancer compared to non-carriers. In contrast, the distribution differences of rs8084630 across genotypes and genetic models did not reach statistical significance, suggesting this locus may not be a genetic factor influencing cervical cancer development in the study population. Additionally, the CT genotype at rs34468925 may be associated with distant metastasis in cervical cancer, indicating that CT genotype carriers exhibit an increased risk of distant metastasis compared to non-carriers, making it a risk factor. Therefore, the rs34468925 site in the SMAD4 gene may play a role in cervical cancer susceptibility, warranting further validation in other populations and confirmation through molecular mechanisms. In contrast, rs8084630 is likely not a major risk site. In the association analysis with HPV infection risk, although this study did not identify any correlation between the SMAD4 gene SNPs rs34468925 and rs8084630 and high-risk HPV infection (HR-HPV+) or non-high-risk HPV infection (HR-HPV -). This suggests that these two loci may not influence the risk of high- or low-risk HPV infection in the studied population. However, the association between HPV and SMAD4 has been confirmed by numerous studies. For example, one study reported that HPV can upregulate SMAD4, thereby enhancing the expression of DNA damage response (DDR) proteins, ultimately leading to viral replication and the development of head and neck cancer (HNC) ( 4 ). Chen demonstrated that high-risk HPV-E7 upregulates miR-182 expression in cervical cancer via the TGF- \(\:\beta\:\) /SMAD4 signaling pathway ( 14 ). In their study, HR-HPV E7 binds to pRb and releases E2F, which then binds to the TGF-β promoter region and increases TGF-β expression. This overexpression of TGF-β activates the Smad4 pathway, causing SMAD4 to interact with the miR-182 promoter region and inducing upregulation of miR-182 in cervical cancer cells and surrounding normal cells. In contrast, low-risk HPV E7 does not affect TGF-β or miR-182 expression. Xu demonstrated that the HPV16 E7 protein promotes immune evasion and stimulates tumor cell proliferation via the TGF-β1/SMAD signaling pathway, playing an important role in cervical carcinogenesis ( 15 ). Similarly, French confirmed that overexpression of HPV16 E5 can attenuate TGF-β/SMAD signal transduction, leading to infection of cervical squamous epithelial cells, disruption of the dynamic equilibrium of the microenvironment, and promotion of HPV-mediated cervical carcinogenesis ( 16 ). Additionally, many studies have focused on the role of SNPs in HPV infection persistence and cervical cancer development. Certain SNPs in immune response-related genes (such as IL1β, TNF, TLR family genes) have been shown to influence the persistence of HPV infection and the progression of cervical lesions ( 17 , 18 ). However, some SNPs have not shown significant associations, which may be related to various factors such as population differences, sample size, HPV subtypes, and other genetic backgrounds. Therefore, our findings also suggest that the two SMAD4 SNPs investigated (rs34468925 and rs8084630) may not serve as independent genetic susceptibility markers for HPV infection risk, further integration with functional experiments, protein expression levels, and larger-scale in-depth studies may provide a more comprehensive understanding. The mechanisms underlying gene-virus interactions require further exploration. In summary, the rs34468925 site in the SMAD4 gene was associated with cervical cancer risk and clinical-pathological characteristics in this study. However, neither rs34468925 nor rs8084630 showed an association with the risk of HR-HPV infection in our data. These results may be influenced by the limited selection of loci and the sample size in this study. The case-control approach employed here provided preliminary insights into the correlations among these multifactorial elements but did not delve into deeper investigations of the TGF-β/SMAD signaling pathway and HPV protein molecular networks. This represents a future research objective and direction for this topic. Exploring the precise mechanisms of cervical cancer from a more multidimensional and profound perspective will not only advance our understanding of its progression but also provide specific and effective strategies or biomarkers for clinical treatment and prediction. Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Ethics No.: YYFY-LL-2022-58). Written informed consent was obtained from all participants prior to inclusion in the study. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The genotyping and associated phenotype data supporting the findings of this study have been deposited in the figshare repository, available at 10.6084/m9.figshare.30320254 or https://figshare.com/s/aefc408833b7d02ae287 Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Yongliang Wang designed the study, performed statistical analyses, and drafted the initial manuscript. Ying Deng and Ying Wei contributed to sample collection, laboratory experiments, and data acquisition. Chunfang Wang assisted in experimental design, supervised laboratory procedures, and contributed to data interpretation. Yuanji Teng conceived the study, provided overall supervision, critically revised the manuscript for important intellectual content, and served as the corresponding author. All authors read and approved the final manuscript. Acknowledgements The authors thank all the participants and staff at the Affiliated Hospital of Youjiang Medical University for Nationalities for their assistance with sample collection and data processing. Authors’ information Corresponding author: Dr. Yuanji Teng Center for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China. Email: [email protected] References Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health. 2020;8(2):e191-e203. Liu L, Li Q, Yang L, Li Q, Du X. SMAD4 Feedback Activates the Canonical TGF-β Family Signaling Pathways. Int J Mol Sci. 2021;22(18). 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Liu J, Cho SN, Akkanti B, Jin N, Mao J, Long W, et al. ErbB2 Pathway Activation upon Smad4 Loss Promotes Lung Tumor Growth and Metastasis. Cell Rep. 2015;10(9):1599-613. Chen J, Deng Y, Ao L, Song Y, Xu Y, Wang CC, et al. The high-risk HPV oncogene E7 upregulates miR-182 expression through the TGF-β/Smad pathway in cervical cancer. Cancer Lett. 2019;460:75-85. Xu Q, Wang S, Xi L, Wu S, Chen G, Zhao Y, et al. Effects of human papillomavirus type 16 E7 protein on the growth of cervical carcinoma cells and immuno-escape through the TGF-beta1 signaling pathway. Gynecol Oncol. 2006;101(1):132-9. French D, Belleudi F, Mauro MV, Mazzetta F, Raffa S, Fabiano V, et al. Expression of HPV16 E5 down-modulates the TGFbeta signaling pathway. Mol Cancer. 2013;12:38. Wang SS, Gonzalez P, Yu K, Porras C, Li Q, Safaeian M, et al. Common genetic variants and risk for HPV persistence and progression to cervical cancer. PLoS One. 2010;5(1):e8667. Das AP, Chopra M, Agarwal SM. Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer. Cytokine. 2022;157:155954. Additional Declarations No competing interests reported. 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08:47:38","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109470,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7771252/v1/edb28477ebef45e9faaa1230.html"},{"id":95806566,"identity":"0382153c-0284-4011-bc6c-e5cc0a610e8d","added_by":"auto","created_at":"2025-11-13 08:47:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequencing map of rs8084630 and rs34468925: \u003c/strong\u003e\u003cem\u003eLeft panel shows the AA, GA, and GG genotypes at rs8084630; Right panel shows the CC, CT, and TT genotypes at rs34468925.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7771252/v1/855175a8719b34fb20fe47b4.png"},{"id":100614657,"identity":"2f56886d-728c-40cc-8544-217cd24b2313","added_by":"auto","created_at":"2026-01-19 17:22:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7771252/v1/e8b8e0ad-5e34-46fa-8a15-e6f1b7cff9fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of SMAD4 Gene Polymorphisms with Human Papillomavirus Infection and Risk of Cervical Cancer: A Case-Control Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, cervical cancer ranks among the most common cancers affecting women. To date, approximately 99.7% of cervical cancer cases are caused by persistent infection with high-risk human papillomavirus (HR-HPV) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The SMAD4 gene (also known as DPC4) is a member of the SMAD family and serves as a central mediator of transforming growth factor beta (TGF-β). It participates in regulating the expression of numerous genes associated with cancer initiation and progression, including apoptosis, proliferation, and inflammation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Research has identified an association between cervical neuroendocrine carcinoma (NECC) and HR-HPV infection, with SMAD4 gene mutations occurring in approximately 20% of NECC cases. However, this study did not reveal a correlation between HPV genotype and SMAD4 gene mutation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Evidently, the relationship between HPV, the SMAD4 gene, and cervical cancer has attracted significant scholarly attention. Therefore, this study employs a case-control approach to investigate the association between SMAD4 single nucleotide polymorphisms (SNPs) and the risk of HPV infection and cervical cancer.\u003c/p\u003e"},{"header":"2. Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eA total of 342 patients diagnosed with cervical cancer at our hospital between January 2022 and June 2024 were enrolled. Their ages ranged from 23 to 79 years, with a mean age of (50.60\u0026thinsp;\u0026plusmn;\u0026thinsp;9.473) years. Inclusion criteria for patients were:\u003c/p\u003e\u003cp\u003e⑴ Diagnosis of cervical cancer based on both clinical features and histopathological examination.\u003c/p\u003e\u003cp\u003e⑵ No prior history of tumors; no prior radiotherapy or chemotherapy.\u003c/p\u003e\u003cp\u003e⑶ No significant impairment of vital organ function (cardiac, cerebral, hepatic, renal).\u003c/p\u003e\u003cp\u003e⑷ Normal mental status; no history of immune disorders, psychiatric conditions, or familial cancer.\u003c/p\u003e\u003cp\u003eA control group of 342 healthy women undergoing routine physical examinations during the same period was selected, aged 23\u0026ndash;82 years, with a mean age of (49.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.524) years. Inclusion criteria for controls were:\u003c/p\u003e\u003cp\u003e⑴ No blood relationship among included individuals.\u003c/p\u003e\u003cp\u003e⑵ No prior history of tumors; routine blood test results within normal ranges.\u003c/p\u003e\u003cp\u003e⑶ Negative HPV test results.\u003c/p\u003e\u003cp\u003e⑷ Normal mental status; no history of other major chronic diseases.\u003c/p\u003e\u003cp\u003e All study participants provided informed consent. This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Ethics No.: YYFY-LL-2022-58). Written informed consent was obtained from all participants prior to inclusion in the study. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 SNP Selection\u003c/h2\u003e\u003cp\u003eSingle nucleotide polymorphism loci of the SMAD4 gene were screened from the HapMap database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hapmap.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://hapmap.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The selection criteria were a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and linkage disequilibrium coefficient (r\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0.8. In addition, considering SNPs potentially related to disease as reported in the literature, two loci (rs34468925 and rs8084630) were ultimately chosen for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 SNP Genotyping\u003c/h2\u003e\u003cp\u003eGenotyping of the SNP loci was performed on all 684 samples using an imLDR\u0026trade; multiplex SNP genotyping kit (Shanghai Tianhao Biotechnology). One microliter of extracted DNA was subjected to 0.01% agarose gel electrophoresis for quality assessment and concentration estimation. Samples were diluted to a working concentration of 5 ng/\u0026micro;L based on estimated concentration. PCR amplification of target fragments was carried out using primers for SMAD4 designed with Premier 5.0 (synthesized by Shanghai Tianhao Biotechnology). The PCR reaction mixture (20 \u0026micro;L) contained 1\u0026times; GC-I buffer (Takara), 2 U 3.0 mM Mg\u003csup\u003e2+\u003c/sup\u003e, 0.3 mM dNTPs, 1 U HotStar Taq DNA polymerase (Qiagen Inc.), 1 \u0026micro;L of template DNA, and 1 \u0026micro;L of multiplex PCR primer. The PCR cycling program was: 95\u0026deg;C for 2 min; 11 cycles of (94\u0026deg;C for 20 s, 66\u0026deg;C cycle for 40 s, 72\u0026deg;C for 1 min); 24 cycles of (94\u0026deg;C for 20 s, 60\u0026deg;C for 30 s, 72\u0026deg;C for 1 min); a final extension at 72\u0026deg;C for 2 min; 4\u0026deg;C indefinitely.\u003c/p\u003e\u003cp\u003eMultiplex PCR products were purified by adding 5 U SAP and 2 U Exonuclease I enzyme to 20 \u0026micro;L of PCR product, incubating at 37\u0026deg;C for 60 min, and then heat-inactivating at 75\u0026deg;C for 15 min. Ligation reactions were prepared in a 10 \u0026micro;L system containing 1 \u0026micro;L 10\u0026times; ligation buffer, 0.25 \u0026micro;L heat-resistant ligase, 0.4 \u0026micro;L 5\u0026prime; ligation primer mix (1 \u0026micro;M), 0.4 \u0026micro;L 3\u0026prime; ligation primer mix (2 \u0026micro;M), 2 \u0026micro;L purified PCR product, and 6 \u0026micro;L ddH₂O. Ligation was carried out for 38 cycles of (94\u0026deg;C for 1 min, 56\u0026deg;C for 4 min).\u003c/p\u003e\u003cp\u003eFor product detection, 1 \u0026micro;L of the diluted ligation product was mixed with 0.1 \u0026micro;L Liz500 size standard and 9 \u0026micro;L Hi-Di. The mixture was denatured at 95\u0026deg;C for 5 min and then run on an ABI 3730XL DNA sequence. Sequencing files were analyzed using Polyphred software, manually proofread, and compiled into results (gene sequencing information shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003ers34468925 and rs8084630 information and PCR primers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ers34468925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e48554285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUpstream 5'-CRTTGGTCTCTCCTAAGCCCCAAT-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDownstream 5'-CACCTCTCAAGTAGTGGCAACTGTGT-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLinking Primer:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRC: 5'-TTCCGCGTTCGGACTGATATTTAGTTTCCTGATCCTTGACCTCCACG-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRF: 5'-TCTTCTCCAGTTATTTTCTGTTTGGACTACTT-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRT: 5'-TACGGTTATTCGGGCTCCTGTTTAGTTTCCTGATCCTTGACCTCCACA-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ers8084630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e48577091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUpstream 5'-CCACTGTCATCTTAGAAAGGTGTGCT \u0026minus;\u0026thinsp;3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDownstream 5'-AGGCAGTGAGTCTTCCTCCATTACC \u0026minus;\u0026thinsp;3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer connection:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRA: 5'-TACGGTTATTCGGGCTCCTGTTGTGCTCTTGTTCTGGAGGGGAA-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRC:5'- TTCCGCGTTCGGACTGATATTGTGCTCTTGTTCTGGAGGGAAG-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRF: 5'- TTAGTAGTAGTTTAGCACCTTTTTTGTTTGTAGAATTTTTTT-3'\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 HPV Genotyping\u003c/h2\u003e\u003cp\u003ePCR-reverse dot hybridization was employed using specific primers designed by Aneng Biotechnology (Shenzhen) for the HPV genome. Potential HPV genotypes in the test samples underwent PCR amplification and biotin labeling. The PCR products were then subjected to specific molecular hybridization with 23 specific probes immobilized on the chip, with results interpreted via chemical color development. The kit used in this study detects 23 HPV genotypes, including 17 high-risk types: HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82; and 6 low-risk types: HPV6, 11, 42, 43, 81, and 83.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eExperimental data were analyzed using SPSS 24.0 statistical software. Binary logistic regression analysis was employed to examine the distribution frequency of rs34468925 genotypes, alleles, genetic models across clinical characteristics, distribution frequencies of gene and haplotype models. The chi-square test (χ \u0026sup2;) was used to examine the distribution frequency of rs34468925 genotypes across clinical characteristics. A t-test analyzed age level differences among subjects. Online SHEsis software performed joint haplotype analysis for rs34468925 and rs8084630. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 rs8084630 and rs34468925 genotypes\u003c/h2\u003e\u003cp\u003eThe study results showed that rs8084630 genotypes included AA, GA, and GG. rs34468925 genotypes comprised CC, CT, and TT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Correlation analysis of rs34468925 and rs8084630 with HPV infection risk\u003c/h2\u003e\u003cp\u003eThis study categorized cervical cancer subjects into high-risk HPV infection (HR-HPV+) and low-risk HPV infection (HR-HPV-) groups. We analyzed the distribution frequencies of genotypes and alleles at rs34468925 and rs8084630 between the two groups to explore their association with HPV infection risk. Results showed no significant differences in the genotypes, alleles, or genetic models (dominant, recessive) of rs34468925 and rs8084630 between the high-risk and low-risk HPV infection groups. The results suggest that rs34468925 and rs8084630 SNPs are not significantly associated with HPV infection risk (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eCorrelation analysis of rs34468925 and rs8084630 with the risk of HPV infection [n (%)]\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=\"char\" char=\".\" 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\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR-HPV (+)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR-HPV (-)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\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%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers34468925\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\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62(40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66(43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66(39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.827(0.512\u0026ndash;1.335)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.930(0.488\u0026ndash;1.772)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u0026thinsp;+\u0026thinsp;CT \u003cem\u003evs\u003c/em\u003e CC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90(59.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93(55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.171(0.751\u0026ndash;1.825)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u0026thinsp;+\u0026thinsp;CT \u003cem\u003evs\u003c/em\u003e TT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128(84.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141(83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.021(0.561\u0026ndash;1.860)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e190(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e216(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.926(0.671\u0026ndash;1.278)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers8084630\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\u003eGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51(33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62(36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70(46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72(42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846(0.515\u0026ndash;1.389)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34(20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.902(0.489\u0026ndash;1.663)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u0026thinsp;+\u0026thinsp;GA \u003cem\u003evs\u003c/em\u003e GG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101(66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106(63.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.158(0.731\u0026ndash;1.835)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;GA \u003cem\u003evs\u003c/em\u003e AA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121(79.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134(79.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.990(0.574\u0026ndash;1.708)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132(43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140(41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.931(0.680\u0026ndash;1.274)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e172(56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e196(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Association of rs34468925 and rs8084630 with cervical cancer risk\u003c/h2\u003e\u003cp\u003eThis study analyzed the genotype and allele distribution of rs34468925 and rs8084630 in cervical cancer patients and healthy controls to explore their potential association with cervical cancer risk. The TT genotype and T allele of rs34468925 were more frequent in cervical cancer cases than in healthy controls. The recessive model CC\u0026thinsp;+\u0026thinsp;CT \u003cem\u003evs.\u003c/em\u003e TT was less common in the cervical cancer group than in controls, with both differences being statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Results indicate that the TT genotype is negatively correlated with cervical cancer risk. The lower frequency of the T allele in the control group suggests its association with reduced cervical cancer risk. In contrast, the distribution frequency of rs8084630 showed no statistically significant difference between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no apparent correlation with cervical cancer risk at this locus (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of rs34468925 and rs8084630 genotype frequencies between cervical cancer patients and controls [n (%)]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCervical cancer (n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers34468925\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e143(41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158(46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146(42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150(43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.930(0.674\u0026ndash;1.282)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.925(0.670\u0026ndash;1.275)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53(15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34(9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.581(0.357\u0026ndash;0.944)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.576(0.354\u0026ndash;0.938)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u0026thinsp;+\u0026thinsp;CT \u003cem\u003evs CC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e199(58.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e184(53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.837(0.619\u0026ndash;1.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.832(0.615\u0026ndash;1.126)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u0026thinsp;+\u0026thinsp;CT \u003cem\u003evs\u003c/em\u003e TT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e289(84.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308(90.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.602(0.380\u0026ndash;0.953)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.599(0.378\u0026ndash;0.949)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e432(63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e466(68.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e252(36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218(31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.802(0.641\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.798(0.638\u0026ndash;0.999)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers8084630\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e118(34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129(37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e154(45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153(44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.909(0.650\u0026ndash;1.271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.906(0.648\u0026ndash;1.267)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60(17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.784(0. 512\u0026thinsp;\u0026minus;\u0026thinsp;1.200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.780(0.509\u0026ndash;1.194)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA \u003cem\u003evs\u003c/em\u003e GG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e224(65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e213(62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.870(0.637\u0026ndash;1.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.866(0.634\u0026ndash;1.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;GA \u003cem\u003evs\u003c/em\u003e AA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e272(79.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e282(82.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.827(0.564\u0026ndash;1.213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.824(0.562\u0026ndash;1.029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390(57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e411(60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e294(43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e273(39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8881(0.710\u0026ndash;1.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.878(0.708\u0026ndash;1.090)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e represents \u003cem\u003eP\u003c/em\u003e and \u003cem\u003eOR\u003c/em\u003e adjusted for age\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Association of rs34468925 with clinical and pathological characteristics in cervical cancer patients\u003c/h2\u003e\u003cp\u003eAnalysis of the data in the above table suggests a potential association between the rs34468925 locus and cervical cancer risk. Therefore, we further investigated whether it correlates with the clinical and pathological characteristics of the subjects in this study. Results showed that the CT genotype at rs34468925 was significantly more frequent in squamous cell carcinoma than in adenocarcinoma. Additionally, the CT genotype exhibited a statistically significant difference in distant metastasis occurrence (P\u0026thinsp;=\u0026thinsp;0.036, OR\u0026thinsp;=\u0026thinsp;3.252), indicating that carriers of the CT genotype had a 3.25-fold higher risk of distant metastasis compared to non-carriers, suggesting an increased risk of metastasis. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation between rs34468925 genotypes and clinicopathological features of cervical cancer patients [n (%)]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSquamous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103(38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e28(53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121(45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e17(32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.517(0.268\u0026ndash;0.997)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e7(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.599(0.243\u0026ndash;1.475)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e327(61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e73(70.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32(69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207(38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e31(29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.671(0.426\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e66(41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e68(42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40(41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e26(16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108(62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e200(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e124(64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e120(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68(35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eⅠ + Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eⅢ + Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e92(41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e51(42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e100(45.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e46(38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.830(0.509\u0026ndash;1.353)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e30(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e23(19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.383(0.728\u0026ndash;2.628)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e284(64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e148(61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e160(36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e92(38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.103(0.798\u0026ndash;1.526)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e41(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e102(39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e30(36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e116(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.554(0.905\u0026ndash;2.669)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e11(13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42(16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.535(0.720\u0026ndash;3.270)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e112(68.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e320(51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e52(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e200(49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.346(0.927\u0026ndash;1.955)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12(57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e131(40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4(19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e142(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.252(1.023\u0026ndash;10.334)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5(23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e48(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.879(0.294\u0026ndash;2.627)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e28(45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e404(62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e14(54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e238(37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.178(0.608\u0026ndash;2.282)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Analysis of the association between rs34468925 and rs8084630 haplotypes and cervical cancer risk\u003c/h2\u003e\u003cp\u003eWe conducted a haplotype analysis combining rs34468925 and rs8084630 to explore any synergistic effect of these loci in cervical carcinogenesis. SHEsis analysis identified four haplotypes formed by rs34468925 and rs8084630: C\u0026ndash;A, C\u0026ndash;G, T\u0026ndash;A, and T\u0026ndash;G (where the first letter represents the allele at rs34468925 and the second letter the allele at rs8084630). The C\u0026ndash;G haplotype was the most common, accounting for 56.4% in the cervical cancer group and 59.9% in the control group (OR\u0026thinsp;=\u0026thinsp;0.874). This suggests a possible protective trend for the C\u0026ndash;G haplotype, though the difference was not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, the T\u0026ndash;A haplotype was more frequent in the cervical cancer group than in controls (36.2% vs. 31.7%). Although this difference did not reach conventional significance (P\u0026thinsp;=\u0026thinsp;0.068), the OR for the T\u0026ndash;A haplotype was 1.232, which is close to significance. This implies that the T\u0026ndash;A haplotype might be associated with a \u003cem\u003ehigher\u003c/em\u003e risk of cervical cancer and could have potential predictive value. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of rs34468925 and rs8084630 haplotype analysis between cervical cancer group and controls [n (%)]\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaplotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\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%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46(6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8116(0.545\u0026ndash;1.223)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e386(56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410(59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.874(0.704\u0026ndash;1.084)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248(36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.232(0.984\u0026ndash;1.542)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analysis of the association between rs34468925 and rs8084630 haplotypes and HR-HPV infection risk\u003c/h2\u003e\u003cp\u003eTo further investigate whether rs34468925 and rs8084630 are associated with HPV infection in a combined genetic context, this study analyzed the distribution differences of four haplotypes in the HPV-infected population. SHEsis analysis revealed that the C-G haplotypes of rs34468925 and rs8084630 were most prevalent in the high-risk HPV infection group (HR-HPV+) and low-risk HPV infection group (HR-HPV-), accounting for 55.9% and 57.7%, respectively. The distribution differences of the four haplotypes between the two groups were not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that rs34468925 and rs8084630 haplotypes are not associated with HPV infection risk (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of the relationship between rs34468925 and rs8084630 haplotypes and HPV infection [n (%)]\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaplotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR-HPV (+)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR-HPV (-)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\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%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.007(0.539\u0026ndash;1.881)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170(55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194(57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.929(0.678\u0026ndash;1.273)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112(36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118(35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.079(0.780\u0026ndash;1.492)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHPV is a small, circular, double-stranded DNA virus belonging to the Papovaviridae family, exhibiting strict genotype-specific host specificity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Globally, approximately 5% of all cancers are associated with HPV infection (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Virtually all cases of cervical cancer can be attributed to persistent infection with high-risk HPV (HR-HPV) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Reduced SMAD4 gene expression is common in tumors, often accompanied by mutations, loss of replication, and transcriptional downregulation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Studies confirm that reduced SMAD4 expression in immunohistochemistry correlates with poorer survival in lung and pancreatic cancers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). SMAD4 deficiency in animal models induces tumorigenesis, promotes oncogene activation, accelerates disease progression, and stimulates tumor metastasis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Increasing evidence demonstrates an association between HPV infection and SMAD4 gene regulation.\u003c/p\u003e\u003cp\u003eThis study systematically analyzed the genotypes, allele frequencies, and haplotype compositions of two SMAD4 gene single nucleotide polymorphisms (SNPs)\u0026mdash;rs34468925 and rs8084630\u0026mdash;in cervical cancer patients and healthy controls, further investigating their association with HPV infection and cervical cancer risk. In the association analysis with cervical cancer, the TT genotype at rs34468925 was significantly negatively correlated with cervical cancer risk, suggesting a potential protective effect of this gene. The T allele frequency was higher in the case group, showing a marginally significant negative correlation with cervical cancer risk. These findings indicate that the TT genotype or T allele at this locus may reduce cervical cancer risk to some extent, meaning individuals carrying the TT genotype have a lower risk of developing cervical cancer compared to non-carriers. In contrast, the distribution differences of rs8084630 across genotypes and genetic models did not reach statistical significance, suggesting this locus may not be a genetic factor influencing cervical cancer development in the study population. Additionally, the CT genotype at rs34468925 may be associated with distant metastasis in cervical cancer, indicating that CT genotype carriers exhibit an increased risk of distant metastasis compared to non-carriers, making it a risk factor. Therefore, the rs34468925 site in the SMAD4 gene may play a role in cervical cancer susceptibility, warranting further validation in other populations and confirmation through molecular mechanisms. In contrast, rs8084630 is likely not a major risk site.\u003c/p\u003e\u003cp\u003eIn the association analysis with HPV infection risk, although this study did not identify any correlation between the SMAD4 gene SNPs rs34468925 and rs8084630 and high-risk HPV infection (HR-HPV+) or non-high-risk HPV infection (HR-HPV -). This suggests that these two loci may not influence the risk of high- or low-risk HPV infection in the studied population. However, the association between HPV and SMAD4 has been confirmed by numerous studies. For example, one study reported that HPV can upregulate SMAD4, thereby enhancing the expression of DNA damage response (DDR) proteins, ultimately leading to viral replication and the development of head and neck cancer (HNC) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Chen demonstrated that high-risk HPV-E7 upregulates miR-182 expression in cervical cancer via the TGF-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e/SMAD4 signaling pathway (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In their study, HR-HPV E7 binds to pRb and releases E2F, which then binds to the TGF-β promoter region and increases TGF-β expression. This overexpression of TGF-β activates the Smad4 pathway, causing SMAD4 to interact with the miR-182 promoter region and inducing upregulation of miR-182 in cervical cancer cells and surrounding normal cells. In contrast, low-risk HPV E7 does not affect TGF-β or miR-182 expression. Xu demonstrated that the HPV16 E7 protein promotes immune evasion and stimulates tumor cell proliferation via the TGF-β1/SMAD signaling pathway, playing an important role in cervical carcinogenesis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly, French confirmed that overexpression of HPV16 E5 can attenuate TGF-β/SMAD signal transduction, leading to infection of cervical squamous epithelial cells, disruption of the dynamic equilibrium of the microenvironment, and promotion of HPV-mediated cervical carcinogenesis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, many studies have focused on the role of SNPs in HPV infection persistence and cervical cancer development. Certain SNPs in immune response-related genes (such as IL1β, TNF, TLR family genes) have been shown to influence the persistence of HPV infection and the progression of cervical lesions (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, some SNPs have not shown significant associations, which may be related to various factors such as population differences, sample size, HPV subtypes, and other genetic backgrounds. Therefore, our findings also suggest that the two SMAD4 SNPs investigated (rs34468925 and rs8084630) may not serve as independent genetic susceptibility markers for HPV infection risk, further integration with functional experiments, protein expression levels, and larger-scale in-depth studies may provide a more comprehensive understanding. The mechanisms underlying gene-virus interactions require further exploration.\u003c/p\u003e\u003cp\u003eIn summary, the rs34468925 site in the SMAD4 gene was associated with cervical cancer risk and clinical-pathological characteristics in this study. However, neither rs34468925 nor rs8084630 showed an association with the risk of HR-HPV infection in our data. These results may be influenced by the limited selection of loci and the sample size in this study. The case-control approach employed here provided preliminary insights into the correlations among these multifactorial elements but did not delve into deeper investigations of the TGF-β/SMAD signaling pathway and HPV protein molecular networks. This represents a future research objective and direction for this topic. Exploring the precise mechanisms of cervical cancer from a more multidimensional and profound perspective will not only advance our understanding of its progression but also provide specific and effective strategies or biomarkers for clinical treatment and prediction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Ethics No.: YYFY-LL-2022-58). Written informed consent was obtained from all participants prior to inclusion in the study. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genotyping and associated phenotype data supporting the findings of this study have been deposited in the \u003cem\u003efigshare\u003c/em\u003e repository, available at 10.6084/m9.figshare.30320254 or https://figshare.com/s/aefc408833b7d02ae287\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYongliang Wang\u003c/strong\u003e designed the study, performed statistical analyses, and drafted the initial manuscript. \u003cstrong\u003eYing Deng\u003c/strong\u003e and \u003cstrong\u003eYing Wei\u003c/strong\u003e contributed to sample collection, laboratory experiments, and data acquisition. \u003cstrong\u003eChunfang Wang\u003c/strong\u003e assisted in experimental design, supervised laboratory procedures, and contributed to data interpretation. \u003cstrong\u003eYuanji Teng\u003c/strong\u003e conceived the study, provided overall supervision, critically revised the manuscript for important intellectual content, and served as the corresponding author. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants and staff at the Affiliated Hospital of Youjiang Medical University for Nationalities for their assistance with sample collection and data processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorresponding author:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Dr. Yuanji Teng\u003c/p\u003e\n\u003cp\u003eCenter for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArbyn M, Weiderpass E, Bruni L, de Sanjos\u0026eacute; S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health. 2020;8(2):e191-e203.\u003c/li\u003e\n\u003cli\u003eLiu L, Li Q, Yang L, Li Q, Du X. SMAD4 Feedback Activates the Canonical TGF-\u0026beta; Family Signaling Pathways. Int J Mol Sci. 2021;22(18).\u003c/li\u003e\n\u003cli\u003eDu X, Li Q, Yang L, Liu L, Cao Q, Li Q. SMAD4 activates Wnt signaling pathway to inhibit granulosa cell apoptosis. Cell Death Dis. 2020;11(5):373.\u003c/li\u003e\n\u003cli\u003eTakayanagi D, Hirose S, Kuno I, Asami Y, Murakami N, Matsuda M, et al. Comparative Analysis of Genetic Alterations, HPV-Status, and PD-L1 Expression in Neuroendocrine Carcinomas of the Cervix. Cancers (Basel). 2021;13(6).\u003c/li\u003e\n\u003cli\u003eWang R, Pan W, Jin L, Huang W, Li Y, Wu D, et al. Human papillomavirus vaccine against cervical cancer: Opportunity and challenge. Cancer Lett. 2020;471:88-102.\u003c/li\u003e\n\u003cli\u003eBurd EM. Human papillomavirus and cervical cancer. Clin Microbiol Rev. 2003;16(1):1-17.\u003c/li\u003e\n\u003cli\u003eCanfell K, Kim JJ, Brisson M, Keane A, Simms KT, Caruana M, et al. Mortality impact of achieving WHO cervical cancer elimination targets: a comparative modelling analysis in 78 low-income and lower-middle-income countries. Lancet. 2020;395(10224):591-603.\u003c/li\u003e\n\u003cli\u003eWang Y, Xue Q, Zheng Q, Jin Y, Shen X, Yang M, et al. SMAD4 mutation correlates with poor prognosis in non-small cell lung cancer. Lab Invest. 2021;101(4):463-76.\u003c/li\u003e\n\u003cli\u003eDhamija S, Yang CM, Seiler J, Myacheva K, Caudron-Herger M, Wieland A, et al. A pan-cancer analysis reveals nonstop extension mutations causing SMAD4 tumour suppressor degradation. Nat Cell Biol. 2020;22(8):999-1010.\u003c/li\u003e\n\u003cli\u003eHaeger SM, Thompson JJ, Kalra S, Cleaver TG, Merrick D, Wang XJ, Malkoski SP. Smad4 loss promotes lung cancer formation but increases sensitivity to DNA topoisomerase inhibitors. Oncogene. 2016;35(5):577-86.\u003c/li\u003e\n\u003cli\u003eHuang W, Navarro-Serer B, Jeong YJ, Chianchiano P, Xia L, Luchini C, et al. Pattern of Invasion in Human Pancreatic Cancer Organoids Is Associated with Loss of SMAD4 and Clinical Outcome. Cancer Res. 2020;80(13):2804-17.\u003c/li\u003e\n\u003cli\u003eZhang M, Kiyono T, Aoki K, Goshima N, Kobayashi S, Hiranuma K, et al. Development of an in vitro carcinogenesis model of human papillomavirus-induced cervical adenocarcinoma. Cancer Sci. 2022;113(3):904-15.\u003c/li\u003e\n\u003cli\u003eLiu J, Cho SN, Akkanti B, Jin N, Mao J, Long W, et al. ErbB2 Pathway Activation upon Smad4 Loss Promotes Lung Tumor Growth and Metastasis. Cell Rep. 2015;10(9):1599-613.\u003c/li\u003e\n\u003cli\u003eChen J, Deng Y, Ao L, Song Y, Xu Y, Wang CC, et al. The high-risk HPV oncogene E7 upregulates miR-182 expression through the TGF-\u0026beta;/Smad pathway in cervical cancer. Cancer Lett. 2019;460:75-85.\u003c/li\u003e\n\u003cli\u003eXu Q, Wang S, Xi L, Wu S, Chen G, Zhao Y, et al. Effects of human papillomavirus type 16 E7 protein on the growth of cervical carcinoma cells and immuno-escape through the TGF-beta1 signaling pathway. Gynecol Oncol. 2006;101(1):132-9.\u003c/li\u003e\n\u003cli\u003eFrench D, Belleudi F, Mauro MV, Mazzetta F, Raffa S, Fabiano V, et al. Expression of HPV16 E5 down-modulates the TGFbeta signaling pathway. Mol Cancer. 2013;12:38.\u003c/li\u003e\n\u003cli\u003eWang SS, Gonzalez P, Yu K, Porras C, Li Q, Safaeian M, et al. Common genetic variants and risk for HPV persistence and progression to cervical cancer. PLoS One. 2010;5(1):e8667.\u003c/li\u003e\n\u003cli\u003eDas AP, Chopra M, Agarwal SM. Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer. Cytokine. 2022;157:155954.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SMAD4, Single nucleotide polymorphism, Human papillomavirus, Cervical cancer","lastPublishedDoi":"10.21203/rs.3.rs-7771252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7771252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the association between SMAD4 gene single nucleotide polymorphisms (SNPs) and the risk of human papillomavirus (HPV) infection and cervical cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA case-control study was conducted, enrolling 342 cervical cancer patients as the case group and 342 healthy individuals from concurrent physical examinations as the control group. Genotyping of SMAD4 SNP loci rs34468925 and rs8084630 was performed using the imLDR SNP genotyping technique. HPV detection in the case group was performed via PCR-reverse dot hybridization. Statistical analysis and the online SHEsis software were employed to comprehensively evaluate the association between the rs34468925 and rs8084630 SNP loci, their haplotypes, and the risk of HPV infection and cervical cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNo significant association was observed between the genotypes, alleles, genetic models (dominant and recessive), or haplotypes of the SMAD4 gene SNPs rs34468925 and rs8084630 and HPV infection risk in either group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). rs8084630 showed no association with cervical cancer risk (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the TT genotype, T allele, and recessive genetic model of rs34468925 may be associated with cervical cancer risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the rs34468925 CT genotype may be associated with clinical pathological characteristics (tumor classification, distant metastasis) in cervical cancer patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe SMAD4 gene SNP site rs34468925 shows no significant association with HPV infection but may be associated with cervical cancer risk and clinical pathological characteristics.\u003c/p\u003e","manuscriptTitle":"Association of SMAD4 Gene Polymorphisms with Human Papillomavirus Infection and Risk of Cervical Cancer: A Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:55:11","doi":"10.21203/rs.3.rs-7771252/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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