==== Front Biomed Res Int Biomed Res Int BMRI BioMed Research International 2314-6133 2314-6141 Hindawi 10.1155/2020/2329196 Research Article The Genetic Polymorphisms in the MIR17HG Gene Are Associated with the Risk of Head and Neck Squamous Cell Carcinoma in the Chinese Han Population https://orcid.org/0000-0002-8803-9211Xu Chongwen https://orcid.org/0000-0003-4115-4439Han Peng https://orcid.org/0000-0001-8190-4244Ren Wanli https://orcid.org/0000-0002-7679-6709Dai Hao https://orcid.org/0000-0002-4892-0813Bai Yanxia https://orcid.org/0000-0002-4451-5116Shen Zhen https://orcid.org/0000-0002-2988-6728Li Baiya lby0929@163.com https://orcid.org/0000-0003-1665-2313Shao Yuan yuanshao201701@163.com Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, 710061 Shaanxi, China Academic Editor: Sercan Erg n 2020 24 11 2020 2020 232919622 6 2020 5 10 2020 20 10 2020 Copyright © 2020 Chongwen Xu et al.2020This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Purpose Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumors in the world. Genetic variants have an important role in HNSCC progression. Our study is aimed at exploring the relationship between MIR17HG polymorphisms and HNSCC risk in the Chinese Han population. Methods We recruited 537 HNSCC cases and 533 healthy subjects to detect the correlation of six polymorphisms in MIR17HG with HNSCC susceptibility. The associations were evaluated by computing odds ratios (ORs) and 95% confidence intervals (CIs) using logistic regression analysis. Results Our study revealed that rs7336610 (OR 1.77, 95%CI = 1.09‐2.86, and p = 0.021) and rs1428 (OR 1.73, 95%CI = 1.07‐2.81, and p = 0.025) are strongly associated with increased susceptibility to HNSCC in men. Besides, rs17735387 played a crucial protective role in stage III/IV HNSCC patients (OR 0.34, 95%CI = 0.12‐0.95, and p = 0.040) compared with stage I/II. Conclusion Our study firstly indicated that MIR17HG polymorphisms are significantly associated with HNSCC susceptibility, which suggests that MIR17HG has a potential role in the occurrence of HNSCC. Key Research and Development Projects of Shaanxi Province2020SF-0202018SF-009Basic Research Program of Natural Science of Shaanxi Province2018JM7027 ==== Body 1. Introduction Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumors, which includes tumors from the oral cavity, hypopharynx, pharynx, and larynx. According to epidemiological statistics, HNSCC ranks sixth among malignant tumors in the world [1]. In China, nasopharyngeal carcinoma, laryngeal carcinoma, oral cancer, and thyroid cancer are common HNSCC. Most patients with HNSCC were diagnosed at middle and advanced stages, resulting in markedly high morbidity and mortality [2]. HNSCC patients usually have typical risk factors, such as smoking, drinking, and or human papillomavirus (HPV) infection [3, 4]. However, people without known risk factors also developed HNSCC. And increased studies revealed that genetic factors have a crucial effect on the occurrence of HNSCC. Long noncoding RNA (lncRNA) is a noncoding RNA longer than 200 nucleotides, which can regulate the occurrence and development of human cancers. Now, some studies indicated that lncRNAs participate in the progression and development of HNSCC by regulating the behavior of HNSCC cells [5–7]. Besides, numerous studies suggested that polymorphisms of some lncRNAs included in PTENP1, and HOTAIR are significantly associated with HNSCC susceptibility [8, 9]. Taken together, it suggests that genetic variants of lncRNAs may have a crucial role in HNSCC progression. miR-17-92a-1 cluster host gene (MIR17HG), located on the 13q31.3 of the human chromosome, is a member of lncRNAs that contributes to the occurrence of many human tumors including colorectal cancer, breast cancer, and multiple myeloma through regulating cell survival, differentiation, and proliferation [10–12]. Besides, the human gene database showed that MIR17HG was significantly expressed in the head and neck (https://www.genecards.org/cgibin/carddisp.pl?gene=MIR17HG&keywords=MIR17HG). A recent study indicated that MIR17HG could affect the abnormal expression of miR-17-92 gene cluster miR-17 and therefore contributes to the development of human tumors [13]. Wang et al. found that the silencing of miR-17 can promote cell apoptosis and inhibit cell proliferation in laryngeal squamous cell carcinoma [14]. MIR17HG acted as a tumor suppressor lncRNA in HPV-positive HNSCC tumors compared to HPV-negative tumors, which plays a distinct role in HPV-related HNSCC [15]. Genetic variants within genes can affect the expression or structure of the genes, which may result in the progression of cancers. Moreover, previous studies have revealed that the polymorphisms of MIR17HG are markedly related to the occurrence of cancers [13, 16, 17]. Taken together, we speculate that the MIR17HG genetic variant may have a potential role in the HNSCC progression. To our knowledge, there is no study on the association between the MIR17HG polymorphisms and HNSCC susceptibility. To better know the effect of MIR17HG genetic variant on the risk of HNSCC in the Chinese population. In this case-control study, we selected six (single nucleotide polymorphisms) SNPs (rs75267932, rs17735387, rs7336610, rs72640334, rs7318578, and rs1428) in MIR17HG from 1000 Genomes Project with minor allele frequencies > 5%, r2 < 0.8, and Hardy − Weinberg equilibrium > 0.05. MassARRAY platform was performed to detect the SNP genotyping. We then studied the association of MIR17HG SNPs with the susceptibility of HNSCC. Finally, we evaluated the relationship of MIR17HG variants with the risk of HNSCC stratified by age, gender, and pathological grade. Our present work will give new scientific evidence for the molecular mechanism of HNSCC development in the Chinese population. 2. Materials and Methods 2.1. Study Population A total of 1070 participants included in 537 unrelated Chinese HNSCC patients (43 laryngeal SCC, 77 nasopharyngeal SCC, 398 thyroid SCC, and 19 parotid SCC) and 533 age-sex matched healthy controls were recruited from the First Affiliated Hospital of Xi'an Jiaotong University in this case-control study. All patients were newly diagnosed by clinical manifestations and confirmed to be HNSCC based on histopathological examination. The controls were selected from healthy individuals with a physical examination in the same hospital. All participants with other types of cancers and familial history of any cancers included HNSCC must be excluded. The basic characteristic of each individual was obtained from the medical records included in age, gender, lymph node metastasis status, clinical stage, BMI (body mass index), and smoking/drinking status. Each participant was told the research purpose, and informed consent was obtained from them. Our study was approved by the ethics committee of the First Affiliated Hospital of Xi'an Jiaotong University. All experiments were carried out based on the guideline of Helsinki's declaration. 2.2. SNP Selection and Genotyping In our study, six SNPs (rs75267932, rs7336610, rs72640334, rs17735387, rs7318578, and rs1428) of the MIR17HG gene were selected by 1000 Genomes Project with MAF > 5% and r2 (the measure value of linkage disequilibrium (LD)) < 0.8 and for further genotyping. The genomic DNA from each peripheral blood sample was extracted by a whole-blood genomic DNA extraction kit (GoldMag, Xi'an, China). The NanoDrop 2000C spectrophotometer (Thermo Scientific, Waltham, USA) was performed to test the concentration and purity of the genomic DNA. PCR primers used for genotyping were designed by the Agena Bioscience Assay Design Suite software (V2.0, https://agenacx.com/online-tools/). We further identified the SNP genotyping via the Agena MassARRAY iPLEX version 4.0 platform, and the data was organized and analyzed by the Agena Bioscience TYPER version 4.0 software. 2.3. Statistical Analysis All variables were examined for normal distributions using the Kolmogorov-Smirnov test. Comparisons of age and clinical characteristics between the cases and controls were, respectively, analyzed by the t-test. The difference of gender between the cases and controls was analyzed by the χ2 test. A chi-squared test was used to evaluate the Hardy-Weinberg equilibrium (HWE) of each SNP in the control group. Distributions of allele and genotype of SNPs in the cases and controls were analyzed by the χ2 test or exact test. The association between the MIR17HG gene and HNSCC susceptibility was detected by calculating ORs and 95% CIs under five inheritance models using logistic regression analysis. In addition, we investigated the correlation of the SNPs with HNSCC risk under subgroups such as age, gender, clinical stage, and HNSCC types. What is more, we also carried out a false-positive report probability (FPRP) analysis to further detect whether the significant findings were just chance or noteworthy observations [18]. Statistical analyses in this study were performed using the SPSS version 17.0 software. All statistical tests were two-tailed and p value <0.05 indicates statistically significant. 3. Results 3.1. Basic Characteristics of Study Participants The basic characteristics of all participants were summarized in Table 1. This study consisted of 537 cases (207 men and 330 women) and 533 controls (204 men and 329 women). The average ages were 46.62 ± 13.67 years in controls and 46.87 ± 15.05 years in cases. There were no significant differences in age and gender between the case and control participants (p = 0.782; p = 0.950, respectively). 3.2. Association Analysis between MIR17HG Genetic Variants and HNSCC Susceptibility The basic information of the candidate SNPs in this study was presented in Table 2. A total of six SNPs were successfully genotyped in our study. The distributions of the genotype of all SNPs in controls were in accordance with HWE (p > 0.05). We then investigate the association of SNPs in the MIR17HG gene with the risk of HNSCC under allele, codominant, dominant, recessive, and log-additive models (Table 3). It was shown that significant associations were not observed in SNPs. 3.3. Correlation of SNPs with HNSCC Risk Stratified by Demographic and Clinical Characteristics We further carried out stratification analyses by age, gender, and pathological grade. When stratified by age, we found that there is no strong significant association with the risk of HNSCC (Table 4). After stratifying by gender, our result indicated that rs7336610 (TC vs. TT, OR 1.77, 95% CI = 1.09‐2.86, and p = 0.021; TC-CC vs. TT, OR 1.64, 95%CI = 1.04‐2.57, and p = 0.030) and rs1428 (AC vs. AA, OR 1.73, 95%CI = 1.07‐2.81, and p = 0.025; AC-CC vs. AA, OR 1.63, 95%CI = 1.04‐2.56, and p = 0.035) polymorphisms are strongly associated with an increased risk of HNSCC in men (Table 4). We further evaluated the relationship of the MIR17HG genetic variants with pathological grade of HNSCC (Table 5). rs17735387 SNP played a crucial protective role in stage III/IV HNSCC patients (GA vs. GG, OR 0.34, 95%CI = 0.12‐0.95, and p = 0.040; GA-AA vs. GG, OR 0.38, 95%CI = 0.15‐0.97, and p = 0.042) compared with stage I/II. We finally detected the impacts of MIR17HG SNPs on nasopharyngeal SCC and thyroid SCC susceptibilities (Table 6). No significant associations were found between the SNPs and nasopharyngeal SCC and thyroid SCC susceptibilities. 3.4. FPRP Analysis FPRP and statistical power were calculated for the positive findings for the samples. As was shown in Table 7, the association of the MIR17HG rs17735387 polymorphism (GA vs. GG) with the risk of stage III/IV HNSCC remained noteworthy (FPRP = 0.192), while the association of rs17735387 (GA-AA vs. GG) was not noteworthy at the prior probability level of 0.25 and FPRP threshold of 0.2 (FPRP = 0.205). Moreover, the associations of rs7336610 polymorphisms (TC vs. TT and TC-CC vs. TT) and rs1248 (CA vs. AA and AC-CC vs. AA) with HNSCC susceptibility in men were also positive at the prior probability level of 0.25 and FPRP threshold of 0.2 (FPRP = 0.106, FPRP = 0.156, FPRP = 0.138, and FPRP = 0.169, respectively). 4. Discussion In this study, we assessed the association of the MIR17HG genetic variants (rs75267932, rs7318578, rs72640334, rs17735387, rs7336610, and rs1428) with HNSCC risk in a Chinese population. We observed that MIR17HG SNPs are strongly associated with HNSCC susceptibility, especially rs7336610, rs17735387, and rs1428. To our knowledge, our study is the first to investigate the correlation between MIR17HG variants and HNSCC risk, which suggests that MIR17HG genetic variants have a potential role in HNSCC progression. MIR17HG is a member of lncRNAs located in a region of human chromosome 13q31, which was shown to play an important role in the development and progression of several human cancers through regulating tumor growth and apoptosis [19–21]. Jiang et al. showed that the higher expression level of the MIR17HG gene can inhibit the growth and metastasis of colon tumors [22]. The overexpression of MIR17HG resulted in the evasion of apoptosis in Burkitt lymphoma cells [23]. Another study found that overexpression of MIR17HG was involved in a negatively regulating proapoptotic gene in the occurrence of lung cancer [21]. MIR17HG could affect the abnormal expression of the miR-17-92 gene. The silencing of miR-17 has a crucial role in laryngeal squamous cell carcinoma progression. MIR17HG plays a distinct role in HPV-related HNSCC [15]. In addition, we observed that the expression level of the MIR17HG gene in tumors is much higher than in normal tissues based on the UALCAN database (http://ualcan.path.uab.edu/cgibin/TCGAExResultNew2.pl?genenam=MIR17HG&ctype=HNSC) (Figure 1) [24]. We guess that the abnormal expression of MIR17HG also plays a vital role in the progression of HNSCC. As we all know, SNPs can affect the expression of genes. Thus, the study of the association between MIR17HG SNPs and HNSCC may help to understand whether they have a potential molecular role in the development of HNSCC. rs7336610 and rs1428 have been identified in the correlation with human cancers at previous researches. The study of Chen et al. showed that there is a strongly increased association between rs7336610 and rs1428 and colorectal cancer susceptibility in the Chinese population in men [16]. Our study also exhibited the same association in HNSCC risk. However, Chacon-Cortes et al. found that rs7336610 is related to breast cancer susceptibility in females of Northern European [17]. In addition, it was shown that rs17735387 SNP played a crucial protective role in stage III/IV HNSCC patients compared with stage I/II. A recent study indicated that the mutation of SNP can influence the stability of lncRNA by changing its folding structure [25]. We speculate that SNPs in the MIR17HG gene contribute to HNSCC progression through influencing the stability. Our study has some limitations. First, we have not detected the association of SNPs with HNSCC stratified by smoking and drinking status due to the very limited information from the medical records of participants. Next, we will collect more basic characteristics to study the associations. Second, whether the polymorphisms in the MIR17HG gene involved in the progression of HNSCC through affecting its functions, which is needed to explore in the subsequent work. Despite the limitations, our study supplied some scientific evidence for finding a new biomarker in the diagnosis and management of HNSCC. 5. Conclusions In summary, our study showed that there is a strong association between MIR17HG genetic variants and HNSCC susceptibility in a Chinese population, which will provide available information for the molecular mechanism of HNSCC in the Chinese population. Acknowledgments The authors thank all the participants and volunteers in this study. We also thank the First Affiliated Hospital of Xi'an Jiaotong University for their help with sample collections. This study was supported by the Key Research and Development Projects of Shaanxi Province in social development field (2020SF-020 and 2018SF-009) and the Basic Research Program of Natural Science of Shaanxi Province (2018JM7027). Data Availability Participant informed consent statements did not seek consent for data to be made publicly available; however, data will be made available to individual researchers upon reasonable request. Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the First Affiliated Hospital of Xi'an Jiaotong University and the 1964 Helsinki declaration. Conflicts of Interest All authors declare that they have no competing interests. Authors' Contributions Yuan Shao and Baiya Li designed the study. Yuan Shao revised the manuscript. Chongwen Xu performed the data and wrote the manuscript. Wanli Ren, Hao Dai, Yanxia Bai, and Zhen Shen recruited and collected study samples. Peng Han analyzed the data. Chongwen Xu and Peng Han contributed equally to this work. Figure 1 The expression of the MIR17HG gene between HNSCC and normal tissues from the UALCAN database. HNSCC: head and neck squamous cell carcinoma. Table 1 Characteristic of HNSCC and healthy controls. Characteristics Cases (n = 537) Controls (n = 533) p Age, years (mean ± SD)a 46.87 ± 15.05 46.62 ± 13.67 0.782  >46 299 (56.0%) 282 (53.0%)  ≤46 238 (44.0%) 251 (47.0%) Genderb 0.950  Male 207 (39.0%) 204 (38.0%)  Female 330 (61.0%) 329 (62.0%) LN metastasis Node positive 103 (19.0%) Node negative 82 (15.0%) Missing 352 (66%) Clinical stage  III/IV 38 (7%)  I/II 140 (26%)  Missing 359 (67%) Nasopharyngeal carcinoma 77 (14%) Thyroid cancer 398 (74%) Laryngeal carcinoma 43 (8%) Parotid gland carcinoma 19 (4%) BMI, kg/m2 (mean ± SD)a  ≤24 12 (6%) 247 (46%)  >24 1 (0.2%) 158 (30%)  Missing 515 (93.8%) 128 (24%) Smoking 90 (17%) 365 (69%) Drinking 46 (9%) 344 (65%) aStudent's t-test is used. bPearson's χ2 test is used. p < 0.05 indicates statistical significance. HNSCC: head and neck squamous cell carcinoma; LN: lymph node; BMI: body mass index. Table 2 The distribution of allele frequencies of MIR17HG SNPs in case and control. SNP ID Chromosome position Function Alleles (minor/major) MAF O (HET) E (HET) p a HWE OR (95% CI) p b Case Control rs75267932 chr13: 91351812 Exon G/A 0.104 0.115 0.205 0.204 1.000 0.89 (0.68-1.17) 0.412 rs72640334 chr13: 91352674 Intron A/C 0.100 0.086 0.164 0.156 0.411 1.19 (0.89-1.60) 0.238 rs7336610 chr13: 91352883 Intron C/T 0.493 0.489 0.485 0.500 0.489 1.02 (0.86-1.21) 0.826 rs7318578 chr13: 91353215 Intron C/A 0.289 0.277 0.400 0.401 1.000 1.06 (0.88-1.28) 0.559 rs17735387 chr13: 91353800 Intron A/G 0.201 0.197 0.311 0.316 0.683 1.03 (0.83-1.27) 0.811 rs1428 chr13: 91354516 Exon C/A 0.490 0.488 0.484 0.500 0.488 1.01 (0.85-1.19) 0.929 HNSCC: head and neck squamous cell carcinoma; SNP: single nucleotide polymorphisms; MAF: minor allele frequency; HWE: Hardy-Weinberg equilibrium. pa values were calculated by exact test, and pa < 0.05 are excluded; pb values were calculated by two-sided χ2, and pb < 0.05 indicates statistical significance. Table 3 Association of MIR17HG polymorphism with HNSCC risk. SNP ID Model Allele/genotype Case n Control n With adjusted OR (95% CI) p rs75267932 Allele A 962 943 1 G 112 123 0.89 (0.68-1.17) 0.412 Codominant AA 433 417 1 GA 96 109 0.85 (0.63-1.15) 0.297 GG 8 7 1.10 (0.39-3.06) 0.859 Dominant AA 433 417 1 AG-GG 104 116 0.86 (0.64-1.16) 0.338 Recessive AA-AG 529 526 1 GG 8 7 1.13 (0.41-3.15) 0.812 Log-additive – – – 0.90 (0.68-1.17) 0.421 rs72640334 Allele C 959 973 1 A 107 91 1.19 (0.89-1.60) 0.238 Codominant CC 432 443 1 CA 95 87 1.12 (0.81-1.54) 0.490 AA 6 2 3.09 (0.62-15.44) 0.169 Dominant CC 432 443 1 CA-AA 101 89 1.16 (0.85-1.59) 0.344 Recessive CC-CA 527 530 1 AA 0 2 3.04 (0.61-15.14) 0.175 Log-additive – – – 1.20 (0.89-1.61) 0.235 rs7336610 Allele T 544 544 1 C 530 520 1.02 (0.86-1.21) 0.826 Codominant TT 133 143 1 TC 278 258 1.16 (0.87-1.55) 0.316 CC 126 131 1.03 (0.73-1.45) 0.856 Dominant TT 133 143 1 TC-CC 404 389 1.12 (0.85-1.47) 0.429 Recessive TT-TC 411 401 1 CC 126 131 0.94 (0.71-1.24) 0.644 Log-additive – – – 1.02 (0.86-1.21) 0.833 rs7318578 Allele A 764 769 1 C 310 295 1.06 (0.88-1.28) 0.559 Codominant AA 267 278 1 AC 230 213 1.12 (0.87-1.45) 0.360 CC 40 41 1.02 (0.64-1.62) 0.945 Dominant AA 267 278 1 AC-CC 270 254 1.11 (0.87-1.41) 0.407 Recessive AA-AC 497 491 1 CC 40 41 0.96 (0.61-1.52) 0.875 Log-additive – – – 1.03 (0.88-1.28) 0.554 rs17735387 Allele G 858 856 1 A 216 210 1.03 (0.83-1.27) 0.811 Codominant GG 344 345 1 GA 170 166 1.03 (0.79-1.33) 0.842 AA 23 22 1.04 (0.57-1.91) 0.893 Dominant GG 344 345 1 AG-AA 193 188 1.03 (0.80-1.32) 0.825 Recessive GG-AG 514 511 1 AA 23 22 1.03 (0.57-1.88) 0.914 Log-additive – – – 1.02 (0.83-1.27) 0.822 rs1428 Allele A 547 546 1 C 525 520 1.01 (0.85-1.19) 0.929 Codominant AA 137 144 1 AC 273 258 1.11 (0.83-1.49) 0.464 CC 126 131 1.01 (0.72-1.42) 0.957 Dominant AA 137 144 1 AC-CC 399 389 1.08 (0.82-1.42) 0.586 Recessive AA-AC 410 402 1 CC 126 131 0.94 (0.71-1.25) 0.670 Log-additive – – – 1.01 (0.85-1.19) 0.935 HNSCC: head and neck squamous cell carcinoma; CI: confidence interval; OR: odds ratio; SNP: single nucleotide polymorphism. p values were calculated by unconditional logistic regression analysis with adjustment for age and gender. p < 0.05 indicates statistical significance. Highlighted in bold indicates the significant association between SNPs and HNSCC risk. Table 4 The relationship of MIR17HG variants with HNSCC stratified by age and gender. SNP ID Allele/genotype >46 years ≤46 years Men Women OR (95% CI) p OR (95% CI) p OR (95% CI) p OR (95% CI) p rs75267932 A 1 1 1 1 G 1.05 (0.72-1.53) 0.810 0.76 (0.51-1.12) 0.166 0.93 (0.60-1.46) 0.765 0.87 (0.62-1.22) 0.422 AA 1 1 1 1 GA 0.98 (0.64-1.50) 0.937 0.71 (0.45-1.10) 0.125 1.24 (0.75-2.04) 0.407 0.68 (0.46-1.00) 0.050 GG 1.52 (0.36-6.47) 0.570 0.81 (0.18-3.71) 0.790 / / 3.77 (0.79-17.98) 0.096 AG-GG 1.01 (0.67-1.53) 0.950 0.71 (0.46-1.10) 0.125 1.08 (0.66-1.75) 0.761 0.76 (0.52-1.11) 0.149 rs72640334 C 1 1 1 1 A 1.07 (0.72-1.59) 0.724 1.36 (0.87-2.11) 0.174 0.91 (0.57-1.46) 0.705 1.43 (0.97-2.09) 0.068 CC 1 1 1 1 CA 1.14 (0.75-1.75) 0.536 1.06 (0.65-1.73) 0.814 0.96 (0.58-1.57) 0.858 1.25 (0.82-1.90) 0.295 AA 0.47 (0.04-5.23) 0.539 / / / / 6.37 (0.76-53.39) 0.088 CA-AA 1.12 (0.73-1.70) 0.608 1.20 (0.75-1.93) 0.450 0.93 (0.57-1.53) 0.780 1.35 (0.90-2.04) 0.147 rs7336610 T 1 1 1 1 C 1.07 (0.85-1.35) 0.559 1.13 (0.88-1.45) 0.352 1.19 (0.91-1.57) 0.210 1.15 (0.93-1.43) 0.204 TT 1 1 1 1 TC 1.41 (0.95-2.10) 0.091 1.14 (0.74-1.76) 0.564 1.77 (1.09-2.86) 0.021 1.12 (0.77-1.62) 0.550 CC 1.14 (0.73-1.78) 0.568 1.33 (0.78-2.26) 0.295 1.43 (0.83-2.46) 0.203 1.33 (0.86-2.07) 0.199 TC-CC 1.30 (0.90-1.89) 0.161 1.19 (0.78-1.80) 0.419 1.64 (1.04-2.57) 0.032 1.18 (0.83-1.68) 0.348 rs7318578 A 1 1 1 1 C 1.05 (0.81-1.35) 0.711 1.07 (0.81-1.41) 0.655 0.93 (0.69-1.25) 0.626 1.15 (0.90-1.46) 0.256 AA 1 1 1 1 CA 1.11 (0.79-1.56) 0.561 1.14 (0.79-1.66) 0.487 0.94 (0.63-1.42) 0.776 1.25 (0.91-1.73) 0.166 CC 1.02 (0.54-1.94) 0.944 0.95 (0.48-1.90) 0.894 0.84 (0.40-1.73) 0.634 1.16 (0.63-2.13) 0.642 AC-CC 1.09 (0.79-1.52) 0.591 1.11 (0.78-1.59) 0.568 0.92 (0.63-1.36) 0.692 1.24 (0.91-1.68) 0.171 rs17735387 G 1 1 1 1 A 0.98 (0.74-1.31) 0.894 1.08 (0.79-1.48) 0.622 0.92 (0.65-1.30) 0.649 1.10 (0.84-1.43) 0.510 GG 1 1 1 1 GA 0.94 (0.66-1.34) 0.740 1.17 (0.80-1.73) 0.422 1.00 (0.65-1.53) 0.999 1.04 (0.75-1.45) 0.799 AA 1.04 (0.47-2.33) 0.916 1.09 (0.43-2.78) 0.857 0.68 (0.25-1.84) 0.446 1.36 (0.62-2.96) 0.440 GA-AA 0.95 (0.68-1.34) 0.785 1.16 (0.80-1.69) 0.428 0.96 (0.64-1.44) 0.827 1.08 (0.78-1.48) 0.651 rs1428 A 1 1 1 1 C 1.08 (0.86-1.36) 0.523 1.11 (0.86-1.42) 0.424 1.20 (0.92-1.58) 0.185 1.14 (0.92-1.41) 0.246 AA 1 1 1 1 CA 1.38 (0.93-2.06) 0.112 1.07 (0.70-1.65) 0.750 1.73 (1.07-2.81) 0.025 1.07 (0.74-1.55) 0.713 CC 1.15 (0.74-1.80) 0.533 1.28 (0.75-2.16) 0.367 1.45 (0.84-2.50) 0.180 1.30 (0.84-2.01) 0.239 AC-CC 1.29 (0.89-1.87) 0.177 1.13 (0.75-1.70) 0.573 1.63 (1.04-2.56) 0.035 1.14 (0.81-1.61) 0.463 HNSCC: head and neck squamous cell carcinoma. p values were calculated by unconditional logistic regression analysis with adjustment for age and gender. p < 0.05 indicates statistical significance. Highlighted in bold indicates the significant association between SNPs and HNSCC risk. Table 5 The relationship of MIR17HG polymorphisms with HNSCC stratified by pathological grade. SNP ID Allele/genotype III-IV (n) I-II (n) OR (95% CI) p rs75267932 A 70 253 1 G 6 27 0.80 (0.32-2.02) 0.641 AA 32 113 1 GA 6 27 / / GG 0 0 / / AG-GG 6 27 0.65 (0.23-1.86) 0.420 rs72640334 C 66 251 1 A 10 29 1.31 (0.61-2.83) 0.488 CC 28 112 1 CA 10 27 1.90 (0.76-4.71) 0.169 AA 0 1 / / CA-AA 10 28 1.84 (0.74-4.55) 0.189 rs7336610 T 33 144 1 C 43 136 1.38 (0.83-2.30) 0.216 TT 8 36 1 TC 17 72 0.98 (0.37-2.60) 0.963 CC 13 32 1.67 (0.58-4.81) 0.342 TC-CC 30 104 1.19 (0.48-2.97) 0.706 rs7318578 A 53 195 1 C 23 85 1.00 (0.57-1.73) 0.987 AA 20 65 1 CA 13 65 0.84 (0.36-1.93) 0.676 CC 5 10 2.25 (0.62-8.16) 0.218 AC-CC 18 75 1.01 (0.47-2.20) 0.973 rs17735387 G 66 223 1 A 10 57 0.59 (0.29-1.23) 0.154 GG 30 92 1 GA 6 39 0.34 (0.12-0.95) 0.040 AA 2 9 0.60 (0.11-3.16) 0.549 GA-AA 8 48 0.38 (0.15-0.97) 0.042 rs1428 A 33 143 1 C 43 135 1.38 (0.83-2.30) 0.215 AA 8 36 1 CA 17 71 0.99 (0.37-2.64) 0.980 CC 13 32 1.69 (0.59-4.87) 0.330 AC-CC 30 103 1.21 (0.48-3.02) 0.684 p values were calculated by unconditional logistic regression analysis with adjustment for age and gender. p < 0.05 indicates statistical significance. Highlighted in bold indicates the significant association between SNPs and HNSCC risk. Table 6 The relationship of MIR17HG variants with nasopharyngeal SCC and thyroid SCC risk. SNP ID Allele/genotype Nasopharyngeal SCC Thyroid SCC Case Control OR (95% CI) p Case Control OR (95% CI) p rs75267932 A 135 943 1 714 943 1 G 19 123 1.08 (0.64-1.81) 0.773 82 123 0.88 (0.66-1.18) 0.399 AA 59 417 1 323 417 1 GA 17 109 1.24 (0.68-2.24) 0.485 68 109 0.79 (0.56-1.11) 0.173 GG 1 7 0.87 (0.10-7.39) 0.898 7 7 1.37 (0.47-3.98) 0.562 AG-GG 18 116 1.21 (0.68-2.16) 0.524 75 116 0.82 (0.59-1.14) 0.247 rs72640334 C 132 973 1 713 973 1 A 18 91 1.46 (0.85-2.50) 0.167 81 91 1.22 (0.89-1.66) 0.225 CC 58 443 1 321 443 1 CA 16 87 1.29 (0.70-2.37) 0.419 71 87 1.15 (0.81-1.63) 0.435 AA 1 2 3.65 (0.29-46.40) 0.318 5 2 3.09 (0.58-16.38) 0.184 CA-AA 17 89 1.34 (0.73-2.43) 0.342 76 89 1.20 (0.85-1.68) 0.306 rs7336610 T 70 544 1 408 544 1 C 84 520 1.26 (0.89-1.76) 0.188 388 520 0.99 (0.83-1.20) 0.956 TT 14 143 1 102 143 1 TC 42 258 1.72 (0.90-3.29) 0.102 204 258 1.09 (0.79-1.50) 0.594 CC 21 131 1.45 (0.70-3.01) 0.319 92 131 1.00 (0.69-1.46) 0.986 TC-CC 63 389 1.62 (0.87-3.01) 0.127 296 389 1.06 (0.79-1.43) 0.697 rs7318578 A 102 769 1 573 769 1 C 52 295 1.33 (0.93-1.91) 0.121 223 295 1.02 (0.83-1.25) 0.890 AA 32 278 1 202 278 1 CA 38 213 1.46 (0.88-2.44) 0.147 169 213 1.10 (0.84-1.45) 0.494 CC 7 41 1.36 (0.55-3.35) 0.503 27 41 0.89 (0.53-1.50) 0.664 AC-CC 45 254 1.45 (0.88-2.37) 0.144 196 254 1.07 (0.82-1.39) 0.632 rs17735387 G 122 856 1 634 856 1 A 32 210 1.07 (0.70-1.62) 0.754 162 210 1.04 (0.83-1.31) 0.728 GG 48 345 1 253 345 1 GA 26 166 1.14 (0.68-1.93) 0.621 128 166 1.06 (0.79-1.40) 0.708 AA 3 22 0.88 (0.25-3.10) 0.843 17 22 1.11 (0.57-2.14) 0.762 GA-AA 29 188 1.11 (0.67-1.83) 0.692 145 188 1.06 (0.81-1.40) 0.667 rs1428 A 70 546 1 412 546 1 C 84 520 1.26 (0.90-1.77) 0.181 384 520 0.98 (0.81-1.18) 0.818 AA 14 144 1 106 144 1 CA 42 258 1.73 (0.90-3.31) 0.099 200 258 1.03 (0.75-1.42) 0.838 CC 21 131 1.46 (0.70-3.02) 0.314 92 131 0.97 (0.67-1.41) 0.878 AC-CC 63 389 1.63 (0.88-3.02) 0.123 292 389 1.01 (0.75-1.36) 0.933 Nasopharyngeal SCC: nasopharyngeal squamous cell carcinoma; thyroid SCC: thyroid squamous cell carcinoma. p values were calculated by unconditional logistic regression analysis with adjustment for age and gender. p < 0.05 indicates statistical significance. Highlighted in bold indicates the significant association between SNPs and HNSCC risk. Table 7 False-positive report probability analysis for the significant findings between MIR17HG variants and HNSCC risk. Genotype and variables OR (95% CI) p valuea Statistical powerb Prior probability 0.25 0.1 0.01 0.001 0.0001 III-IV  rs17735387 G>A   GA vs. GG 0.34 (0.12-0.95) 0.040 0.308 0.192c 0.416 0.887 0.988 0.999   GA-AA vs. GG 0.38 (0.15-0.97) 0.042 0.329 0.205 0.436 0.895 0.988 0.999 Men  rs7336610 T>C   TC vs. TT 1.77 (1.09-2.86) 0.021 0.487 0.106c 0.262 0.796 0.975 0.997   TC-CC vs. TT 1.64 (1.04-2.57) 0.032 0.031 0.156c 0.357 0.859 0.984 0.998  rs1428 A>C   CA vs. AA 1.73 (1.07-2.81) 0.025 0.410 0.138c 0.325 0.841 0.982 0.998   AC-CC vs. AA 1.63 (1.04-2.56) 0.035 0.034 0.169c 0.379 0.870 0.985 0.999 HNSCC: head and neck squamous cell carcinoma. p valuea was calculated by unconditional logistic regression analysis with adjustment for age and gender. Statistical powerb was calculated using the number of observations in the subgroup and the OR and p values in this table. cThe level of false-positive report probability threshold was set at 0.2, and noteworthy findings are presented. ==== Refs 1 Siegel R. Naishadham D. Jemal A. Cancer statistics, 2012 CA: A Cancer Journal for Clinicians 2012 62 1 10 29 10.3322/caac.20138 2-s2.0-84855792427 22237781 2 Kim L. King T. Agulnik M. Head and neck cancer: changing epidemiology and public health implications Oncology 2010 24 10 915 9, 924 21138172 3 Ragin C. C. R. Modugno F. Gollin S. M. The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus Journal of Dental Research 2007 86 2 104 114 10.1177/154405910708600202 2-s2.0-33847066782 17251508 4 Goldenberg D. Lee J. Koch W. M. Habitual Risk Factors for Head and Neck Cancer Otolaryngology–Head and Neck Surgery 2016 131 6 986 993 10.1016/j.otohns.2004.02.035 2-s2.0-9644284453 5 Wu K. Jiang Y. Zhou W. Long noncoding RNA RC3H2 facilitates cell proliferation and invasion by targeting microRNA-101-3p/EZH2 axis in OSCC Molecular Therapy-Nucleic acids 2020 20 97 110 10.1016/j.omtn.2020.02.006 32163895 6 Jiang Y. Wu K. Cao W. Long noncoding RNA KTN1-AS1 promotes head and neck squamous cell carcinoma cell epithelial-mesenchymal transition by targeting miR-153-3p Epigenomics 2020 12 6 487 505 10.2217/epi-2019-0173 32267161 7 Ji Y. Feng G. Hou Y. Yu Y. Wang R. Yuan H. Long noncoding RNA MEG3 decreases the growth of head and neck squamous cell carcinoma by regulating the expression of miR‐421 and E‐cadherin Cancer Medicine 2020 9 11 3954 3963 10.1002/cam4.3002 32277605 8 Wu B. Liu J. Wang B. Liao X. Cui Z. Ding N. Association on polymorphisms in LncRNA HOTAIR and susceptibility to HNSCC in Chinese population European Review for Medical and Pharmacological Sciences 2018 22 3 702 706 10.26355/eurrev_201802_14296 2-s2.0-85042862527 29461598 9 Xin C. Li J. L. Zhang Y. X. Yu Z. H. Polymorphisms in lncRNA PTENP1 and the risk of oral squamous cell carcinoma in a Chinese population European Review for Medical and Pharmacological Sciences 2018 22 17 5583 5587 10.26355/eurrev_201809_15822 30229832 10 Ma H. Pan J. S. Jin L. X. MicroRNA-17~92 inhibits colorectal cancer progression by targeting angiogenesis Cancer Letters 2016 376 2 293 302 10.1016/j.canlet.2016.04.011 2-s2.0-84963516615 27080303 11 Leivonen S. K. Makela R. Ostling P. Protein lysate microarray analysis to identify microRNAs regulating estrogen receptor signaling in breast cancer cell lines Oncogene 2009 28 44 3926 3936 10.1038/onc.2009.241 2-s2.0-70449124423 19684618 12 Gao X. Zhang R. Qu X. MiR-15a, miR-16-1 and miR-17-92 cluster expression are linked to poor prognosis in multiple myeloma Leukemia Research 2012 36 12 1505 1509 10.1016/j.leukres.2012.08.021 2-s2.0-84868206165 22959509 13 Wu H. Huang T. Ye Z. Fu X. Hu K. Yang X. Correlation of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with susceptibility and prognosis for multiple myeloma Clinical Lymphoma, Myeloma & Leukemia 2019 19 7 e359 e366 10.1016/j.clml.2019.03.018 2-s2.0-85064538498 14 Wang J.-X. Jia X.-J. Liu Y. Silencing of miR-17-5p suppresses cell proliferation and promotes cell apoptosis by directly targeting PIK3R1 in laryngeal squamous cell carcinoma Cancer Cell International 2020 20 1 10.1186/s12935-020-1096-3 15 Sannigrahi M. K. Sharma R. Panda N. K. Khullar M. Role of non-coding RNAs in head and neck squamous cell carcinoma: a narrative review Oral Diseases 2018 24 8 1417 1427 10.1111/odi.12782 2-s2.0-85032969910 28941018 16 Chen P. Bai Y. Li Y. Association between polymorphisms of MIR17HG and risk of colorectal cancer in the Chinese Han population Molecular Genetics & Genomic Medicine 2019 7 6, article e667 10.1002/mgg3.667 2-s2.0-85067284780 30941921 17 Chacon-Cortes D. Smith R. A. Lea R. A. Youl P. H. Griffiths L. R. Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer Tumour Biology 2015 36 7 5369 5376 10.1007/s13277-015-3200-1 2-s2.0-84938199876 25680407 18 He J. Zou Y. Liu X. Association of common genetic variants in pre-microRNAs and neuroblastoma susceptibility: a two-center study in Chinese children Molecular Therapy-Nucleic acids 2018 11 1 8 10.1016/j.omtn.2018.01.003 2-s2.0-85044852660 29858046 19 Ota A. Tagawa H. Karnan S. Identification and characterization of a novel gene, C13orf25, as a target for 13q31-q32 amplification in malignant lymphoma Cancer Research 2004 64 9 3087 3095 10.1158/0008-5472.CAN-03-3773 2-s2.0-2342449399 15126345 20 Ventura A. Young A. G. Winslow M. M. Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters Cell 2008 132 5 875 886 10.1016/j.cell.2008.02.019 2-s2.0-39749143354 18329372 21 Hayashita Y. Osada H. Tatematsu Y. A polycistronic microRNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation Cancer Research 2005 65 21 9628 9632 10.1158/0008-5472.CAN-05-2352 2-s2.0-27544495514 16266980 22 Jiang H. Wang P. Wang Q. Quantitatively controlling expression of miR-17~92 determines colon tumor progression in a mouse tumor model The American Journal of Pathology 2014 184 5 1355 1368 10.1016/j.ajpath.2014.01.037 2-s2.0-84899542912 24681249 23 O'Donnell K. A. Wentzel E. A. Zeller K. I. Dang C. V. Mendell J. T. c-Myc-regulated microRNAs modulate E2F1 expression Nature 2005 435 7043 839 843 10.1038/nature03677 2-s2.0-20444479428 15944709 24 Chandrashekar D. S. Bashel B. Balasubramanya S. A. H. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses Neoplasia 2017 19 8 649 658 10.1016/j.neo.2017.05.002 2-s2.0-85024094733 28732212 25 Ge Y. He Y. Jiang M. Polymorphisms in lncRNA PTENP1 and the risk of gastric cancer in a Chinese Population Population 2017 2017, article 6807452 1 8 10.1155/2017/6807452 2-s2.0-85029806384 28931965