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Genetic variants of SOX9 contribute to susceptibility of gliomas among Chinese population

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Oncotarget. 2016; 7:65916-65922. https://doi.org/10.18632/oncotarget.11679

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Liang Wang, Gang Li, Nan Liu, Zhen Wang, Xiaoshan Xu, Jing Qi, Dongni Ren, Pengxing Zhang, Yongsheng Zhang and Yanyang Tu _

Abstract

Liang Wang1,*, Gang Li1,*, Nan Liu2,*, Zhen Wang2, Xiaoshan Xu2, Jing Qi2, Dongni Ren2, Pengxing Zhang2, Yongsheng Zhang3, Yanyang Tu2

1Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, China

2Department of Experimental Surgery, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, China

3Department of Administrative, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, China

*These authors have contributed equally to this work

Correspondence to:

Yanyang Tu, email: tu.fmmu@gmail.com

Yongsheng Zhang, email: zhangys_td@163.com

Keywords: SOX9, gliomas, polymorphism, genetic susceptibility

Received: June 17, 2016    Accepted: August 15, 2016    Published: August 29, 2016

ABSTRACT

Gliomas make up about 80% of all malignant brain tumors, and cause serious public health problem. Genetic factors and environmental factors jointly caused the development of gliomas, and understanding of the genetic basis is a key component of preventive oncology. However, most genetic factors underlying carcinogenesis of gliomas remain largely unclear. In current study, we systematically evaluated whether genetic variants of SOX9 gene, a transcription factor that plays a central role in the development and differentiation of tumors, contribute to susceptibility of gliomas among Chinese population using a two-stage, case–control study. Results showed that SOX9 rs1042667 was significant associated with increased gliomas risk after adjusted by age, gender, family history of cancer, smoking status and alcohol status (Allele C vs A: OR=1.25; 95% CI=1.11-1.40; P=1.2×10−4). Compared with the carriers of genotype AA, both those of genotype AC (OR=1.37; 95% CI=1.13-1.66) and CC (OR=1.53; 95% CI=1.22-1.91) had significantly increased gliomas risk. This should be the first genetic association study which aims to evaluated the association between genetic variants of SOX9 and susceptibility of gliomas. Additional functional and association studies with different ethnic groups included are needed to further confirm our results.


Genetic variants of SOX9 contribute to susceptibility of gliomas among Chinese population | Wang | Oncotarget

INTRODUCTION

Brain and central nervous system (CNS) malignancies refer to series of rarely occurred tumors [1]. According to the data of National Office for Cancer Prevention and Control in China, the estimated numbers of new cases and deaths in China were 101600 and 61000, respectively [2]. Gliomas, which are thought to arise from glial cells, make up about 80% of all malignant brain tumors [3]. Many risk factors have been identified as potential contributors to gliomas risk [4]. For example, ionizing radiation exposure, allergies or atopic disease(s) history, and potential influence of occupational exposures have been identified to be associated with gliomas risk [46]. Understanding of the genetic basis is a key component of preventive oncology. Genome-wide association studies (GWAS) have successfully identified a large number of common single-nucleotide polymorphisms (SNPs) influencing gliomas risk, while these SNPs only explain a small proportion of the genetic risk [7]. Furthermore, due to the common methodological challenges among gliomas studies include small sample sizes, heterogeneity of tumor subtypes, and retrospective exposure assessment, limited genetic loci were identified for gliomas risk [811].

SRY (Sex Determining Region Y)-Box (SOX) genes, which encode transcription factors belonging to the HMG (High Mobility Group) superfamily, have been identified to be associated with a large number of tumour types in vivo [1214]. Among them, SOX9, which acts as a transcription factor that plays a central role in the development and differentiation of multiple cell lineages, were suggested to be associated with risk and prognosis of gliomas [1517]. Rani et al [18] found MiR-145 functed as a tumor-suppressive RNA by targeting Sox9 and adducin 3 in human glioma cells. Genetic association studies could provide solid evidence for the Oncogenic role of SOX9 gene in human malignant gliomas. To present, no studies have evaluated that whether genetic variants of SOX9 contribute to susceptibility of gliomas. Given the biological importance of SOX9 gene and its implication in gliomas, we performed the current two-stage, case–control study to investigated the association between tag SNPs in the SOX9 gene and gliomas susceptibility among Chinese population.

RESULTS

Demographic characteristics of the subjects

In current study, totally 400 gliomas patients and 400 healthy control were recruited in the discovery stage, while 800 gliomas patients and 800 healthy control were recruited in the validation stage. Table 1 shows the comparison of gliomas patients and controls by selective characteristics in both the discovery stage and the validation stage. No significant variation in age, gender, family history of cancer, and smoking status was found between gliomas patients and healthy controls in two stages (P > 0.05), while the gliomas patients are more likely to be drinkers in the validation stage (P<0.001).

Table 1: Comparison of gliomas patients and controls by selective characteristics

Variables

Discovery stage

Validation stage

Cases (n=400)

Controls (n=400)

P value

Cases (n=800)

Controls (n=800)

P value

Age (years)

48.2±3.8

48.5±3.3

0.233

45.3±4.2

45.1±4.7

0.370

Gender (male)

244 (61.0%)

248 (62.0%)

0.771

480 (60.0%)

468 (58.5%)

0.542

Family history of cancer

86 (21.5%)

75 (18.8%)

0.332

160 (20.0%)

131 (16.4%)

0.060

Smoking status

   Ever

100 (25.0%)

97 (24.2%)

0.806

162 (20.2%)

132 (16.5%)

0.053

   Never

300 (75.0%)

303 (75.8%)

638 (79.8%)

668 (83.5%)

Alcohol status

   Ever

114 (28.5%)

104 (26.0%)

0.435

238 (29.8%)

160 (20.0%)

P<0.001

   Never

326 (81.5%)

336 (84.0%)

562 (70.2%)

640 (80.0%)

Associations between SOX9 gene polymorphisms and gliomas risk in the discovery stage

Totally seven tagSNPs (rs1042667, rs918080, rs16977091, rs9893662, rs7502198, rs6501522, and rs9915657) are selected using the SNPinfo web-based software. As shown in Table 2, the genotype frequencies of the selected SNPs and their associations with gliomas risk are presented. All of the genotype distributions for the seven tag SNPs were consistent with the HWE (P > 0.05). Our results indicated that SOX9 rs1042667 was significant associated with increased gliomas risk (Allele C vs A: OR=1.26; 95% CI=1.04–1.54; P=0.019). Compared with the carriers of genotype AA, both those of genotype AC (OR=1.45; 95% CI=1.03–2.04) and CC (OR=1.55; 95% CI=1.06–2.28) had significantly increased gliomas risk. However, no significant trend was detected for rs918080, rs16977091, rs9893662, rs7502198, rs6501522, and rs9915657.

Table 2: Association between SOX9 gene polymorphisms and the risk of gliomas in the discovery stage

SNPs

Subject

Genotype (N)

OR (95 % CI)1

P value

11

12

22

2 vs 1

12 vs 11

22 vs 11

rs1042667

Case

91

195

114

1.26 (1.04–1.54)

1.45 (1.03–2.04)

1.55 (1.06–2.28)

0.019

Control

122

180

98

rs918080

Case

271

100

29

1.20 (0.93-1.55)

0.99 (0.72-1.37)

1.87 (1.00-3.50)

0.156

Control

280

104

16

rs16977091

Case

310

72

18

0.92 (0.69-1.22)

0.75 (0.53-1.06)

1.43 (0.68-3.01)

0.564

Control

296

92

12

rs9893662

Case

193

140

67

1.20 (0.97-1.48)

0.91 (0.68-1.24)

1.65 (1.07-2.54)

0.087

Control

200

158

42

rs7502198

Case

122

210

68

1.20 (0.88-1.64)

1.30 (0.85-1.98)

1.14 (0.94-1.39)

0.187

Control

140

200

60

rs6501522

Case

250

116

34

1.07 (0.84-1.36)

0.92 (0.68-1.25)

1.41 (0.82-2.45)

0.549

Control

250

126

24

rs9915657

Case

252

108

40

1.08 (0.85-1.36)

0.84 (0.61-1.14)

1.57 (0.93-2.66)

0.512

Control

248

127

25

1adjusted for Age, gender, family history of cancer, smoking status and alcohol status

Validation analysis of the association between SOX9 rs1042667 and gliomas risk

To validate the positive findings, the association between SOX9 rs1042667 and gliomas risk was evaluated in an independent stage (Table 3). The genotype distribution for rs1042667 was also consistent with the HWE in the stage (P > 0.05). The results showed that SOX9 rs1042667 was also significant associated with increased gliomas risk (Allele C vs A: OR=1.24; 95% CI=1.08-1.42; P=0.002). when merged two stages together, SOX9 rs1042667 was significant associated with increased gliomas risk (Allele C vs A: OR=1.25; 95% CI=1.11-1.40; P=1.2×10−4). Compared with the carriers of genotype AA, both those of genotype AC (OR=1.37; 95% CI=1.13-1.66) and CC (OR=1.53; 95% CI=1.22-1.91) had significantly increased gliomas risk. We also conducted stratified analysis by alcohol status. However, the results didn’t changed materially (Table 4).

Table 3: Genotype frequencies of rs1042667 and association with risk of gliomas in validation stage and the merged results

rs1042667

Cases (n=800)

Controls (n=800)

OR1 (95% CIs)

P value

validation stage

   AA

185 (23.1%)

236 (29.5%)

Reference

   AC

390 (48.8%)

374 (46.8%)

1.33 (1.05-1.69)

0.019

   CC

225 (28.1%)

190 (23.7%)

1.51 (1.15-1.98)

0.003

 Additive model

1.24 (1.08-1.42)

0.002

Merged results

   AA

276 (23.0%)

358 (29.8%)

Reference

   AC

585 (48.7%)

554 (46.2%)

1.37 (1.13-1.66)

1.6×10−3

   CC

339 (28.3%)

288 (24.0%)

1.53 (1.22-1.91)

1.8×10−4

 Additive model

1.25 (1.11-1.40)

1.2×10−4

1 adjusted for Age, gender, family history of cancer, smoking status and alcohol status

Table 4: Association of rs1042667 with risk of gliomas stratified by Alcohol status

rs1042667

Cases (n=1200)

Controls (n=1200)

OR1 (95% CIs)

P value

Ever drinkers

352

264

   AA

77 (21.9%)

80 (30.3%)

Reference

   AC

173 (49.1%)

121 (45.8%)

1.48 (1.05-1.64)

0.046

   CC

102 (29.0%)

63 (23.9%)

1.68 (1.08-2.62)

0.021

 Additive model

1.31 (1.05-1.64)

0.018

Never drinkers

848

936

   AA

199 (23.5%)

278 (29.7%)

Reference

   AC

412 (48.6%)

433 (46.3%)

1.33 (1.06-1.67)

0.014

   CC

237 (27.9%)

225 (24.0%)

1.47 (1.14-1.90)

3.2×10−3

 Additive model

1.23 (1.07-1.40)

2.5×10−3

1adjusted for Age, gender, family history of cancer, and smoking status

DISCUSSION

In current study, we systematically evaluated whether genetic variants of SOX9 contribute to susceptibility of gliomas among Chinese population using a two-stage, case–control study. Results strongly indicated that SOX9 rs1042667 was significant associated with 1.25-flod increased gliomas risk, after adjusted by age, gender, family history of cancer, smoking status and alcohol status. To be best of our knowledge, this should be the first genetic association study which aims to evaluated the association between genetic variants of SOX9 and susceptibility of gliomas.

The prognosis for patients with gliomas is often very poor, only ~2% of which aged 65 years or older [19]. Previous studies revealed that genetic, behavioral, environmental and developmental contributed to gliomas risk, although only exposure to therapeutic or high-dose radiation was firmly established [20]. Studies of genetic syndromes, familial aggregation, linkage and mutagen sensitivity, which identified specific candidate genes including APC, hMLH1, hMSH2, PMS2, PTEN, NF1, NF2, et al, indicated the genetic susceptibility to gliomas [2022]. Furthermore, genome-wide association studies (GWASs) show that common genetic variation contributes to the heritable risk of gliomas, and identify some new gliomas susceptibility loci, however, these only account for a small proportion of gliomas cases [8]. Additional studies, which aims to find more potential susceptibility loci to reveal the underlying genetic basis for gliomas, are needed and may yield increased insight into the development of this malignancy.

SOX9 gene is located on a gene desert on 17q24 in humans, and could recognize the CCTTGAG sequence along with other members of the HMG-box class DNA-binding proteins [23, 24]. Sox9 is required for the early differentiation of the prostate bud epithelia, and fully involved in the carcinogenesis, differentiation, and invasion prostate cancer through reactivating the WNT/beta-catenin signaling that mediates ductal morphogenesis [2528]. Besides, Sox9 was also associated the carcinogenesis, development and prognosis of colorectal cancer, lung cancer, melanoma, hepatocellular carcinoma, skin tumors, and cervical cancer [2937]. Pop et al [38] found a homozygous nonsense mutation in SOX9 in the dominant disorder campomelic dysplasia. Zhang et al [39] identified that multiple genetic variants mapping to a unique enhancer looping to the SOX9 oncogene could account for the risk associated with 17q24.3 locus of prostate cancer.

In current study, we confirmed that SOX9 rs1042667 was significantly associated with increased gliomas risk (Allele C vs A: OR=1.25; 95% CI=1.11-1.40; P=1.2×10−4). Using Quanto software (http://biostats.usc.edu/Quanto.html), we found we have 97% statistical power to get such an association. Rs1042667 located at the 3’ UTR region of the SOX9 gene. Using RegulomeDB [40, 41], rs1042667 was identified to be likely to affect binding capacity with other genes and transcription factors. While it was reported to be associated with the expression of AC005152.2 in Heart Left Ventricle, and could alter regulatory motifs, including GR_disc4, TATA_disc1, THAP1_disc2, and YY1_disc1, when analyzed by HaploReg v4.1 [42]. It was also predicted to be the binding site of transcription factors (ATF6_01, CACD_01, CDPCR3_01, et al) using the SNPinfo package (https://snpinfo.niehs.nih.gov/cgi-bin/snpinfo/snpfunc.cgi). Overall, our findings provided evidence for the important role of SOX9 gene in the tumorigenesis of gliomas.

Several limitations should be considered when interpreting the results of this study. First, due to the natural of case-control study design, selection and information bias might be unavoidable. We have tried our best to eliminate this by matching the control by age, gender, and race from the same hospital, a face to face interview, and unified training of the investigators. Second, gene-environment interactions was not detected in current study, which may be caused by the relatively not very large sample size.

Conclusively, the present two-stage, case-control study revealed that SOX9 rs1042667 was associated with increased gliomas risk in a Chinese population. Further functional and genetic association studies in larger population, with different ethnic groups included, are warranted to further validate our results and explore the possible mechanism of SOX9 gene in the carcinogenesis and development of gliomas.

MATERIALS AND METHODS

Subjects

All of the gliomas cases received treatments from the Tangdu Hospital until November 2015, we recruited 1,200 genetically unrelated Chinese patients with newly diagnosed and histopathologically confirmed primary gliomas. We also included a total of 1,200 age-, gender-, race-matched, cancer-free volunteers recruited from the same hospital. The response rate was approximately 95% and 92% for gliomas subjects and cancer-free controls, respectively. All the participants have no previous history of cancer and CNS-related diseases. Structured-interviewer-administered questionnaires were used to collect data on demographic characteristics and potential gliomas risk factors. The study was approved by the Institutional Review Board of Tangdu Hospital. All of the participants provided written informed consent by themselves or their guardians.

SNP selection and genotyping

The tag SNPs of SOX9 gene and its 10kb flanking region were selected using SNPinfo (http://snpinfo.niehs.nih.gov/) based on the criteria of minor allele frequency(MAF) >5% for Chinese Han subjects; Seven tag SNPs in the SOX9 gene that met the criteria were chosen in this study (Figure 1). Genomic DNAs were extracted by Qiagen DNA blood kit (Qiagen, Hilden, Germany) from whole blood samples collected from all subjects. The extraction of genomic DNAs was performed following the manufacturer’s protocols. SNP genotyping was performed by Sequenom MassArray iPLEX platform (Sequenom Inc., San Diego, CA, USA). To validate the accuracy of genotyping results and for quality control, approximately 10% of the samples were randomly selected and genotyped with sequencing. Results showed that the concordance for the quality control samples was 100%.

tag SNP selection of the SOX9 gene.

Figure 1: tag SNP selection of the SOX9 gene.

Statistical analysis

All the data was analyzed with SPSS software version 13.0 (SPSS Inc, Chicago, IL, USA). All statistical tests were two-sided, with a significance level of P < 0.05. The chi-square test was used to compare the difference in gender, family history of cancer, smoking status and alcohol status between gliomas patients and healthy controls, while Student’s paired t test was performed to compare the difference in age between gliomas patients and healthy controls. Genotypic frequencies in controls for each SNP were tested for departure from HWE using goodness-of-fit χ2 test. Odds ratios and corresponding 95% confidence intervals (CIs) were used to estimate the association between selected polymorphisms and gliomas risk. Adjusted ORs were calculated by multivariate analysis with unconditional logistic regression, with adjustment for age, gender, family history of cancer, smoking status and alcohol status.

ACKNOWLEDGMENTS

We thank all staff who were involved in the subject recruitment, telephone interviews, sample preparation and laboratory assays for their hard works. This work was supported by National Natural Scientifi c Foundation of China (NO. 81272419, NO. 81572983, NO. 81402081, NO. 81572697, NO. 81272776), Social development, science and technology research projects Of Shaanxi Province (2015SF027, 2016SF191) and Open Funding of Laboratory of Brain Disorders, Ministry of Science and Technology (2015NZDJ02).

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

REFERENCES

1. Amirian ES, Armstrong GN, Zhou R, Lau CC, Claus EB, Barnholtz-Sloan JS, Il’yasova D, Schildkraut J, Ali-Osman F, Sadetzki S, Johansen C, Houlston RS, Jenkins RB, et al. The Glioma International Case-Control Study: A Report From the Genetic Epidemiology of Glioma International Consortium. Am J Epidemiol. 2016; 183:85-91.

2. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016.

3. Goodenberger ML and Jenkins RB. Genetics of adult glioma. Cancer genetics. 2012; 205:613-621.

4. Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, Wrensch MR and Barnholtz-Sloan JS. The epidemiology of glioma in adults: a “state of the science” review. Neuro-oncology. 2014; 16:896-913.

5. Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, Wrensch MR and Barnholtz-Sloan JS. Response to “the epidemiology of glioma in adults: a ‘state of the science’ review”. Neuro-oncology. 2015; 17:624-626.

6. Morgan LL. The epidemiology of glioma in adults: a “state of the science” review. Neuro-oncology. 2015; 17:623-624.

7. Kinnersley B, Mitchell JS, Gousias K, Schramm J, Idbaih A, Labussiere M, Marie Y, Rahimian A, Wichmann HE, Schreiber S, Hoang-Xuan K, Delattre JY, Nothen MM, et al. Quantifying the heritability of glioma using genome-wide complex trait analysis. Scientific reports. 2015; 5:17267.

8. Kinnersley B, Labussiere M, Holroyd A, Di Stefano AL, Broderick P, Vijayakrishnan J, Mokhtari K, Delattre JY, Gousias K, Schramm J, Schoemaker MJ, Fleming SJ, Herms S, et al. Genome-wide association study identifies multiple susceptibility loci for glioma. Nat Commun. 2015; 6:8559.

9. Lee YH and Song GG. Genome-wide pathway analysis in glioma. Neoplasma. 2015; 62:230-238.

10. Kruczyk M, Przanowski P, Dabrowski M, Swiatek-Machado K, Mieczkowski J, Wallerman O, Ronowicz A, Piotrowski A, Wadelius C, Kaminska B and Komorowski J. Integration of genome-wide of Stat3 binding and epigenetic modification mapping with transcriptome reveals novel Stat3 target genes in glioma cells. Biochim Biophys Acta. 2014; 1839:1341-1350.

11. Rajaraman P, Melin BS, Wang Z, McKean-Cowdin R, Michaud DS, Wang SS, Bondy M, Houlston R, Jenkins RB, Wrensch M, Yeager M, Ahlbom A, Albanes D, et al. Genome-wide association study of glioma and meta-analysis. Hum Genet. 2012; 131:1877-1888.

12. Dong C, Wilhelm D and Koopman P. Sox genes and cancer. Cytogenet Genome Res. 2004; 105:442-447.

13. Zhu Y, Li Y, Jun Wei JW and Liu X. The role of Sox genes in lung morphogenesis and cancer. Int J Mol Sci. 2012; 13:15767-15783.

14. Lovell-Badge R. The early history of the Sox genes. Int J Biochem Cell Biol. 2010; 42:378-380.

15. Gao J, Zhang JY, Li YH and Ren F. Decreased expression of SOX9 indicates a better prognosis and inhibits the growth of glioma cells by inducing cell cycle arrest. International journal of clinical and experimental pathology. 2015; 8:10130-10138.

16. Liu H, Liu Z, Jiang B, Peng R, Ma Z and Lu J. SOX9 Overexpression Promotes Glioma Metastasis via Wnt/beta-Catenin Signaling. Cell biochemistry and biophysics. 2015.

17. Wang L, He S, Yuan J, Mao X, Cao Y, Zong J, Tu Y and Zhang Y. Oncogenic role of SOX9 expression in human malignant glioma. Med Oncol. 2012; 29:3484-3490.

18. Rani SB, Rathod SS, Karthik S, Kaur N, Muzumdar D and Shiras AS. MiR-145 functions as a tumor-suppressive RNA by targeting Sox9 and adducin 3 in human glioma cells. Neuro-oncology. 2013; 15:1302-1316.

19. Schwartzbaum JA, Fisher JL, Aldape KD and Wrensch M. Epidemiology and molecular pathology of glioma. Nature clinical practice Neurology. 2006; 2:494-503; quiz 491 p following 516.

20. Ohgaki H and Kleihues P. Epidemiology and etiology of gliomas. Acta Neuropathol. 2005; 109:93-108.

21. Hayashi S, Sasaki H, Kimura T, Abe T, Nakamura T, Kitamura Y, Miwa T, Kameyama K, Hirose Y and Yoshida K. Molecular-genetic and clinical characteristics of gliomas with astrocytic appearance and total 1p19q loss in a single institutional consecutive cohort. Oncotarget. 2015; 6:15871-15881. doi: 10.18632/oncotarget.3869.

22. Zhang J, Wu G, Miller CP, Tatevossian RG, Dalton JD, Tang B, Orisme W, Punchihewa C, Parker M, Qaddoumi I, Boop FA, Lu C, Kandoth C, et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat Genet. 2013; 45:602-612.

23. Song JH, Kim JH, Park S, Kang W, Kim HW, Kim HE and Jang JH. Signaling responses of osteoblast cells to hydroxyapatite: the activation of ERK and SOX9. Journal of bone and mineral metabolism. 2008; 26:138-142.

24. Tew SR, Clegg PD, Brew CJ, Redmond CM and Hardingham TE. SOX9 transduction of a human chondrocytic cell line identifies novel genes regulated in primary human chondrocytes and in osteoarthritis. Arthritis Res Ther. 2007; 9:R107.

25. Wang H, Leav I, Ibaragi S, Wegner M, Hu GF, Lu ML, Balk SP and Yuan X. SOX9 is expressed in human fetal prostate epithelium and enhances prostate cancer invasion. Cancer Res. 2008; 68:1625-1630.

26. Thomsen MK, Butler CM, Shen MM and Swain A. Sox9 is required for prostate development. Dev Biol. 2008; 316:302-311.

27. Thomsen MK, Francis JC and Swain A. The role of Sox9 in prostate development. Differentiation. 2008; 76:728-735.

28. Ma F, Ye H, He HH, Gerrin SJ, Chen S, Tanenbaum BA, Cai C, Sowalsky AG, He L, Wang H, Balk SP and Yuan X. SOX9 drives WNT pathway activation in prostate cancer. J Clin Invest. 2016; 126:1745-1758.

29. Marcker Espersen ML, Linnemann D, Christensen IJ, Alamili M, Troelsen JT and Hogdall E. SOX9 expression predicts relapse of stage II colon cancer patients. Hum Pathol. 2016; 52:38-46.

30. Wang X, Liu Y, Liu X, Yang J, Teng G, Zhang L and Zhou C. miR-124 inhibits cell proliferation, migration and invasion by directly targeting SOX9 in lung adenocarcinoma. Oncol Rep. 2016; 35:3115-3121.

31. Liu C, Liu L, Chen X, Cheng J, Zhang H, Shen J, Shan J, Xu Y, Yang Z, Lai M and Qian C. Sox9 Regulates Self-Renewal and Tumorigenicity by Promoting Symmetrical Cell Division of Cancer Stem Cells in Hepatocellular Carcinoma. Hepatology. 2016.

32. Ashkenazi S, Ortenberg R, Besser M, Schachter J and Markel G. SOX9 indirectly regulates CEACAM1 expression and immune resistance in melanoma cells. Oncotarget. 2016; 7: 30166-77. doi: 10.18632/oncotarget.7379.

33. Hong Y, Chen W, Du X, Ning H, Chen H, Shi R, Lin S, Xu R, Zhu J, Wu S and Zhou H. Upregulation of sex-determining region Y-box 9 (SOX9) promotes cell proliferation and tumorigenicity in esophageal squamous cell carcinoma. Oncotarget. 2015; 6:31241-31254. doi: 10.18632/oncotarget.5160.

34. Li J, Wang L, Liu Z, Zu C, Xing F, Yang P, Yang Y, Dang X and Wang K. MicroRNA-494 inhibits cell proliferation and invasion of chondrosarcoma cells in vivo and in vitro by directly targeting SOX9. Oncotarget. 2015; 6:26216-26229. doi: 10.18632/oncotarget.4460.

35. Wang HY, Lian P and Zheng PS. SOX9, a potential tumor suppressor in cervical cancer, transactivates p21WAF1/CIP1 and suppresses cervical tumor growth. Oncotarget. 2015; 6:20711-20722. doi: 10.18632/oncotarget.4133.

36. Shen Z, Deng H, Fang Y, Zhu X, Ye GT, Yan L, Liu H and Li G. Identification of the interplay between SOX9 and S100P in the metastasis and invasion of colon carcinoma. Oncotarget. 2015; 6:20672-20684. doi: 10.18632/oncotarget.3967.

37. Capaccione KM, Hong X, Morgan KM, Liu W, Bishop JM, Liu L, Markert E, Deen M, Minerowicz C, Bertino JR, Allen T and Pine SR. Sox9 mediates Notch1-induced mesenchymal features in lung adenocarcinoma. Oncotarget. 2014; 5:3636-3650. doi: 10.18632/oncotarget.1970.

38. Pop R, Zaragoza MV, Gaudette M, Dohrmann U and Scherer G. A homozygous nonsense mutation in SOX9 in the dominant disorder campomelic dysplasia: a case of mitotic gene conversion. Hum Genet. 2005; 117:43-53.

39. Zhang X, Cowper-Sal lari R, Bailey SD, Moore JH and Lupien M. Integrative functional genomics identifies an enhancer looping to the SOX9 gene disrupted by the 17q24.3 prostate cancer risk locus. Genome Res. 2012; 22:1437-1446.

40. Lee SY, Hong MJ, Jeon HS, Choi YY, Choi JE, Kang HG, Jung DK, Jin C, Do SK, Yoo SS, Seok Y, Lee EB, Shin KM, et al. Functional intronic ERCC1 polymorphism from regulomeDB can predict survival in lung cancer after surgery. Oncotarget. 2015; 6:24522-24532. doi: 10.18632/oncotarget.4083.

41. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S, Cherry JM and Snyder M. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012; 22:1790-1797.

42. Ward LD and Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016; 44:D877-881.


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