3.8

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2.4

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Juan Zhou, Yiran Xu, Luyao Wang, Yu Cong, Ke Huang, Xinxing Pan, Guangquan Liu, Wenqu Li, Chenchen Dai, Pengfei Xu, Xuemei Jia. LncRNA IDH1-AS1 sponges miR-518c-5p to suppress proliferation of epithelial ovarian cancer cell by targeting RMB47[J]. The Journal of Biomedical Research, 2024, 38(1): 51-65. DOI: 10.7555/JBR.37.20230097
Citation: Juan Zhou, Yiran Xu, Luyao Wang, Yu Cong, Ke Huang, Xinxing Pan, Guangquan Liu, Wenqu Li, Chenchen Dai, Pengfei Xu, Xuemei Jia. LncRNA IDH1-AS1 sponges miR-518c-5p to suppress proliferation of epithelial ovarian cancer cell by targeting RMB47[J]. The Journal of Biomedical Research, 2024, 38(1): 51-65. DOI: 10.7555/JBR.37.20230097

LncRNA IDH1-AS1 sponges miR-518c-5p to suppress proliferation of epithelial ovarian cancer cell by targeting RMB47

More Information
  • Corresponding author:

    Xuemei Jia, Department of Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, 123 Mochou Rd, Nanjing, Jiangsu 210004, China. E-mail: xmjia@njmu.edu.cn; Pengfei Xu, Nanjing Maternity and Child Health Medical Institute, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, 123 Mochou Rd, Nanjing, Jiangsu 210004, China. E-mail: pengfeixu@njmu.edu.cn

  • △These authors contributed equally to this work.

  • Received Date: April 17, 2023
  • Revised Date: August 21, 2023
  • Accepted Date: August 28, 2023
  • Available Online: September 01, 2023
  • Published Date: November 19, 2023
  • Long noncoding RNA (lncRNA) IDH1 antisense RNA 1 (IDH1-AS1) is involved in the progression of multiple cancers, but its role in epithelial ovarian cancer (EOC) is unknown. Therefore, we investigated the expression levels of IDH1-AS1 in EOC cells and normal ovarian epithelial cells by quantitative real-time PCR (qPCR). We first evaluated the effects of IDH1-AS1 on the proliferation, migration, and invasion of EOC cells through cell counting kit-8, colony formation, EdU, transwell, wound-healing, and xenograft assays. We then explored the downstream targets of IDH1-AS1 and verified the results by a dual-luciferase reporter, qPCR, rescue experiments, and Western blotting. We found that the expression levels of IDH1-AS1 were lower in EOC cells than in normal ovarian epithelial cells. High IDH1-AS1 expression of EOC patients from the Gene Expression Profiling Interactive Analysis database indicated a favorable prognosis, because IDH1-AS1 inhibited cell proliferation and xenograft tumor growth of EOC. IDH1-AS1 sponged miR-518c-5p whose overexpression promoted EOC cell proliferation. The miR-518c-5p mimic also reversed the proliferation-inhibiting effect induced by IDH1-AS1 overexpression. Furthermore, we found that RNA binding motif protein 47 (RBM47) was the downstream target of miR-518c-5p, that upregulation of RBM47 inhibited EOC cell proliferation, and that RBM47 overexpressing plasmid counteracted the proliferation-promoting effect caused by the IDH1-AS1 knockdown. Taken together, IDH1-AS1 may suppress EOC cell proliferation and tumor growth via the miR-518c-5p/RBM47 axis.

  • We acknowledge and appreciate our institutional colleagues for their experimental technical support.

    CLC number: R73-3, Document code: A

    The authors reported no conflict of interests.

  • [1]
    Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17–48. doi: 10.3322/caac.21763
    [2]
    Wang M, Zhang J, Wu Y. Tumor metabolism rewiring in epithelial ovarian cancer[J]. J Ovarian Res, 2023, 16(1): 108. doi: 10.1186/s13048-023-01196-0
    [3]
    Kuroki L, Guntupalli SR. Treatment of epithelial ovarian cancer[J]. BMJ, 2020, 371: m3773. doi: 10.1136/bmj.m3773
    [4]
    Salamini-Montemurri M, Lamas-Maceiras M, Lorenzo-Catoira L, et al. Identification of lncRNAs deregulated in epithelial ovarian cancer based on a gene expression profiling meta-analysis[J]. Int J Mol Sci, 2023, 24(13): 10798. doi: 10.3390/ijms241310798
    [5]
    Zhao S, Zhang X, Chen S, et al. Natural antisense transcripts in the biological hallmarks of cancer: powerful regulators hidden in the dark[J]. J Exp Clin Cancer Res, 2020, 39(1): 187. doi: 10.1186/s13046-020-01700-0
    [6]
    Xu F, Huang M, Chen Q, et al. LncRNA HIF1A-AS1 promotes gemcitabine resistance of pancreatic cancer by enhancing glycolysis through modulating the AKT/YB1/HIF1α pathway[J]. Cancer Res, 2021, 81(22): 5678–5691. doi: 10.1158/0008-5472.CAN-21-0281
    [7]
    Yang L, Chen Y, Liu N, et al. Low expression of TRAF3IP2-AS1 promotes progression of NONO-TFE3 translocation renal cell carcinoma by stimulating N6-methyladenosine of PARP1 mRNA and downregulating PTEN[J]. J Hematol Oncol, 2021, 14(1): 46. doi: 10.1186/s13045-021-01059-5
    [8]
    Liu Y, Zhang P, Wu Q, et al. Long non-coding RNA NR2F1-AS1 induces breast cancer lung metastatic dormancy by regulating NR2F1 and ΔNp63[J]. Nat Commun, 2021, 12(1): 5232. doi: 10.1038/s41467-021-25552-0
    [9]
    Xiang S, Gu H, Jin L, et al. LncRNA IDH1-AS1 links the functions of c-Myc and HIF1α via IDH1 to regulate the Warburg effect[J]. Proc Natl Acad Sci U S A, 2018, 115(7): E1465–E1474. doi: 10.1073/pnas.1711257115
    [10]
    Zhang N, Li Z, Bai F, et al. PAX5-induced upregulation of IDH1-AS1 promotes tumor growth in prostate cancer by regulating ATG5-mediated autophagy[J]. Cell Death Dis, 2019, 10(10): 734. doi: 10.1038/s41419-019-1932-3
    [11]
    Wang J, Quan Y, Lv J, et al. LncRNA IDH1-AS1 suppresses cell proliferation and tumor growth in glioma[J]. Biochem Cell Biol, 2020, 98(5): 556–564. doi: 10.1139/bcb-2019-0465
    [12]
    Braga EA, Fridman MV, Moscovtsev AA, et al. LncRNAs in ovarian cancer progression, metastasis, and main pathways: ceRNA and alternative mechanisms[J]. Int J Mol Sci, 2020, 21(22): 8855. doi: 10.3390/ijms21228855
    [13]
    Klar M, Hasenburg A, Hasanov M, et al. Prognostic factors in young ovarian cancer patients: An analysis of four prospective phase III intergroup trials of the AGO Study Group, GINECO and NSGO[J]. Eur J Cancer, 2016, 66: 114–124. doi: 10.1016/j.ejca.2016.07.014
    [14]
    Chang LC, Huang CF, Lai MS, et al. Prognostic factors in epithelial ovarian cancer: a population-based study[J]. PLoS One, 2018, 13(3): e0194993. doi: 10.1371/journal.pone.0194993
    [15]
    Rosendahl M, Høgdall CK, Mosgaard BJ. Restaging and survival analysis of 4036 ovarian cancer patients according to the 2013 FIGO classification for ovarian, fallopian tube, and primary peritoneal cancer[J]. Int J Gynecol Cancer, 2016, 26(4): 680–687. doi: 10.1097/IGC.0000000000000675
    [16]
    Peres LC, Cushing-Haugen KL, Köbel M, et al. Invasive epithelial ovarian cancer survival by histotype and disease stage[J]. J Natl Cancer Inst, 2019, 111(1): 60–68. doi: 10.1093/jnci/djy071
    [17]
    Martinez A, Pomel C, Filleron T, et al. Prognostic relevance of celiac lymph node involvement in ovarian cancer[J]. Int J Gynecol Cancer, 2014, 24(1): 48–53. doi: 10.1097/IGC.0000000000000041
    [18]
    Ataseven B, Grimm C, Harter P, et al. Prognostic value of lymph node ratio in patients with advanced epithelial ovarian cancer[J]. Gynecol Oncol, 2014, 135(3): 435–440. doi: 10.1016/j.ygyno.2014.10.003
    [19]
    Wu S, Ding L, Xu H, et al. The long non-coding RNA IDH1-AS1 promotes prostate cancer progression by enhancing IDH1 enzyme activity[J]. Onco Targets Ther, 2020, 13: 7897–7906. doi: 10.2147/OTT.S251915
    [20]
    Chen L. Linking long noncoding RNA localization and function[J]. Trends Biochem Sci, 2016, 41(9): 761–772. doi: 10.1016/j.tibs.2016.07.003
    [21]
    Fernandes JCR, Acuña SM, Aoki JI, et al. Long non-coding RNAs in the regulation of gene expression: physiology and disease[J]. Non-Coding RNA, 2019, 5(1): 17. doi: 10.3390/ncrna5010017
    [22]
    Fan Y, Wang L, Han X, et al. LncRNA ASB16-AS1 accelerates cellular process and chemoresistance of ovarian cancer cells by regulating GOLM1 expression via targeting miR-3918[J]. Biochem Biophys Res Commun, 2023, 675: 1–9. doi: 10.1016/j.bbrc.2023.06.068
    [23]
    Su M, Huang P, Li Q. Long noncoding RNA SNHG6 promotes the malignant phenotypes of ovarian cancer cells via miR-543/YAP1 pathway[J]. Heliyon, 2023, 9(5): e16291. doi: 10.1016/j.heliyon.2023.e16291
    [24]
    Li Y, Zhu X, Zhang C, et al. Long noncoding RNA FTX promotes epithelial-mesenchymal transition of epithelial ovarian cancer through modulating miR-7515/TPD52 and activating Met/Akt/mTOR[J]. Histol Histopathol, 2023, 9: 18620. doi: 10.14670/HH-18-620
    [25]
    Flor I, Bullerdiek J. The dark side of a success story: microRNAs of the C19MC cluster in human tumours[J]. J Pathol, 2012, 227(3): 270–274. doi: 10.1002/path.4014
    [26]
    Dyrskjøt L, Ostenfeld MS, Bramsen JB, et al. Genomic profiling of microRNAs in bladder cancer: miR-129 is associated with poor outcome and promotes cell death in vitro[J]. Cancer Res, 2009, 69(11): 4851–4860. doi: 10.1158/0008-5472.CAN-08-4043
    [27]
    Zhao J, Yang J, Lin J, et al. Identification of miRNAs associated with tumorigenesis of retinoblastoma by miRNA microarray analysis[J]. Childs Nerv Syst, 2009, 25(1): 13–20. doi: 10.1007/s00381-008-0701-x
    [28]
    Kinouchi M, Uchida D, Kuribayashi N, et al. Involvement of miR-518c-5p to growth and metastasis in oral cancer[J]. PLoS One, 2014, 9(12): e115936. doi: 10.1371/journal.pone.0115936
    [29]
    He J, Han Z, Luo J, et al. Hsa_Circ_0007843 acts as a miR-518c-5p sponge to regulate the migration and invasion of colon cancer SW480 cells[J]. Front Genet, 2020, 11: 9. doi: 10.3389/fgene.2020.00009
    [30]
    Fossat N, Radziewic T, Jones V, et al. Conditional restoration and inactivation of Rbm47 reveal its tissue-context requirement for viability and growth[J]. Genesis, 2016, 54(3): 115–122. doi: 10.1002/dvg.22920
    [31]
    Radine C, Peters D, Reese A, et al. The RNA-binding protein RBM47 is a novel regulator of cell fate decisions by transcriptionally controlling the p53-p21-axis[J]. Cell Death Differ, 2020, 27(4): 1274–1285. doi: 10.1038/s41418-019-0414-6
    [32]
    Sakurai T, Isogaya K, Sakai S, et al. RNA-binding motif protein 47 inhibits Nrf2 activity to suppress tumor growth in lung adenocarcinoma[J]. Oncogene, 2016, 35(38): 5000–5009. doi: 10.1038/onc.2016.35
    [33]
    Guo T, You K, Chen X, et al. RBM47 inhibits hepatocellular carcinoma progression by targeting UPF1 as a DNA/RNA regulator[J]. Cell Death Discov, 2022, 8(1): 320. doi: 10.1038/s41420-022-01112-3
    [34]
    Qin Y, Sun W, Wang Z, et al. RBM47/SNHG5/FOXO3 axis activates autophagy and inhibits cell proliferation in papillary thyroid carcinoma[J]. Cell Death Dis, 2022, 13(3): 270. doi: 10.1038/s41419-022-04728-6
    [35]
    Shen D, Jiang Y, Li J, et al. The RNA-binding protein RBM47 inhibits non-small cell lung carcinoma metastasis through modulation of AXIN1 mRNA stability and Wnt/β-catentin signaling[J]. Surg Oncol, 2020, 34: 31–39. doi: 10.1016/j.suronc.2020.02.011
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    1. Lee SB, Hong KW, Park B, et al. Impact of genetic markers related to hyper-HDL cholesterol on the prevalence of myocardial infarction: a KoGES study. J Lipid Res, 2025, 66(4): 100777. DOI:10.1016/j.jlr.2025.100777
    2. Liu Y, Hou W, Gao T, et al. Influence and role of polygenic risk score in the development of 32 complex diseases. J Glob Health, 2025, 15: 04071. DOI:10.7189/jogh.15.04071
    3. Nawade B, Shim SH, Chu SH, et al. Integrative transcriptogenomic analyses reveal the regulatory network underlying rice eating and cooking quality and identify a role for alpha-globulin in modulating starch and sucrose metabolism. Plant Commun, 2025, 6(5): 101287. DOI:10.1016/j.xplc.2025.101287
    4. Lee SB, Hong KW, Park B, et al. Impact of genetic markers related to hyper-HDL cholesterol on the prevalence of myocardial infarction: a KoGES study. J Lipid Res, 2025, 66(4): 100777. DOI:10.1016/j.jlr.2025.100777
    5. Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med, 2024, 22(1): 258. DOI:10.1186/s12967-024-05053-6
    6. Kang HY, Choe EK. Clinical Strategies in Gene Screening Counseling for the Healthy General Population. Korean J Fam Med, 2024, 45(2): 61-68. DOI:10.4082/kjfm.23.0254
    7. Lee SB, Choi JE, Hong KW, et al. Genetic Variants Linked to Myocardial Infarction in Individuals with Non-Alcoholic Fatty Liver Disease and Their Potential Interaction with Dietary Patterns. Nutrients, 2024, 16(5): 602. DOI:10.3390/nu16050602
    8. Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med, 2024, 22(1): 258. DOI:10.1186/s12967-024-05053-6
    9. Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med, 2024, 22(1): 258. DOI:10.1186/s12967-024-05053-6
    10. Kang HY, Choe EK. Clinical Strategies in Gene Screening Counseling for the Healthy General Population. Korean J Fam Med, 2024, 45(2): 61-68. DOI:10.4082/kjfm.23.0254
    11. Lee SB, Choi JE, Hong KW, et al. Genetic Variants Linked to Myocardial Infarction in Individuals with Non-Alcoholic Fatty Liver Disease and Their Potential Interaction with Dietary Patterns. Nutrients, 2024, 16(5): 602. DOI:10.3390/nu16050602
    12. Seo H, Park JH, Hwang JT, et al. Epigenetic Profiling of Type 2 Diabetes Mellitus: An Epigenome-Wide Association Study of DNA Methylation in the Korean Genome and Epidemiology Study. Genes (Basel), 2023, 14(12): 2207. DOI:10.3390/genes14122207
    13. Seo H, Park JH, Hwang JT, et al. Epigenetic Profiling of Type 2 Diabetes Mellitus: An Epigenome-Wide Association Study of DNA Methylation in the Korean Genome and Epidemiology Study. Genes (Basel), 2023, 14(12): 2207. DOI:10.3390/genes14122207
    14. Seo H, Park JH, Hwang JT, et al. Epigenetic Profiling of Type 2 Diabetes Mellitus: An Epigenome-Wide Association Study of DNA Methylation in the Korean Genome and Epidemiology Study. Genes (Basel), 2023, 14(12): 2207. DOI:10.3390/genes14122207
    15. Qiao J, Shao Z, Wu Y, et al. Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing. J Transl Med, 2022, 20(1): 424. DOI:10.1186/s12967-022-03637-8
    16. Padilla-Martinez F, Szczerbiński Ł, Citko A, et al. Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study. Int J Mol Sci, 2022, 23(24): 16081. DOI:10.3390/ijms232416081
    17. Muneeb M, Feng S, Henschel A. Transfer learning for genotype-phenotype prediction using deep learning models. BMC Bioinformatics, 2022, 23(1): 511. DOI:10.1186/s12859-022-05036-8
    18. Roh H. A genome-wide association study of the occurrence of genetic variations in Edwardsiella piscicida, Vibrio harveyi, and Streptococcus parauberis under stressed environments. J Fish Dis, 2022, 45(9): 1373-1388. DOI:10.1111/jfd.13668
    19. Zhang M, Qiao J, Zhang S, et al. Exploring the association between birthweight and breast cancer using summary statistics from a perspective of genetic correlation, mediation, and causality. J Transl Med, 2022, 20(1): 227. DOI:10.1186/s12967-022-03435-2
    20. Zhang M, Qiao J, Zhang S, et al. Exploring the association between birthweight and breast cancer using summary statistics from a perspective of genetic correlation, mediation, and causality. J Transl Med, 2022, 20(1): 227. DOI:10.1186/s12967-022-03435-2
    21. Padilla-Martinez F, Szczerbiński Ł, Citko A, et al. Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study. Int J Mol Sci, 2022, 23(24): 16081. DOI:10.3390/ijms232416081
    22. Muneeb M, Feng S, Henschel A. Transfer learning for genotype-phenotype prediction using deep learning models. BMC Bioinformatics, 2022, 23(1): 511. DOI:10.1186/s12859-022-05036-8
    23. O'Rielly DD, Rahman P. Genetic Epidemiology of Complex Phenotypes. Methods Mol Biol, 2021, 2249: 335-367. DOI:10.1007/978-1-0716-1138-8_19
    24. Monnot S, Desaint H, Mary-Huard T, et al. Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances. Cells, 2021, 10(11): 3080. DOI:10.3390/cells10113080
    25. Lu H, Zhang J, Jiang Z, et al. Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics. Front Genet, 2021, 12: 656545. DOI:10.3389/fgene.2021.656545
    26. Petersen KS, Kris-Etherton PM, McCabe GP, et al. Perspective: Planning and Conducting Statistical Analyses for Human Nutrition Randomized Controlled Trials: Ensuring Data Quality and Integrity. Adv Nutr, 2021, 12(5): 1610-1624. DOI:10.1093/advances/nmab045
    27. Dennis JK, Sealock JM, Straub P, et al. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med, 2021, 13(1): 6. DOI:10.1186/s13073-020-00820-8
    28. Lu H, Wei Y, Jiang Z, et al. Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies. J Transl Med, 2021, 19(1): 418. DOI:10.1186/s12967-021-03090-z
    29. Lu H, Qiao J, Shao Z, et al. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med, 2021, 19(1): 314. DOI:10.1186/s12916-021-02186-z
    30. Mkize N, Maiwashe A, Dzama K, et al. Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review. Pathogens, 2021, 10(12): 1604. DOI:10.3390/pathogens10121604
    31. McGuire D, Jiang Y, Liu M, et al. Model-based assessment of replicability for genome-wide association meta-analysis. Nat Commun, 2021, 12(1): 1964. DOI:10.1038/s41467-021-21226-z
    32. Mkize N, Maiwashe A, Dzama K, et al. Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review. Pathogens, 2021, 10(12): 1604. DOI:10.3390/pathogens10121604
    33. McGuire D, Jiang Y, Liu M, et al. Model-based assessment of replicability for genome-wide association meta-analysis. Nat Commun, 2021, 12(1): 1964. DOI:10.1038/s41467-021-21226-z
    34. Yamamoto A, Shibuya T. More practical differentially private publication of key statistics in GWAS. Bioinform Adv, 2021, 1(1): vbab004. DOI:10.1093/bioadv/vbab004
    35. Jin T, Youn J, Kim AN, et al. Interactions of Habitual Coffee Consumption by Genetic Polymorphisms with the Risk of Prediabetes and Type 2 Diabetes Combined. Nutrients, 2020, 12(8): 2228. DOI:10.3390/nu12082228
    36. Chen H, Wang T, Yang J, et al. Improved Detection of Potentially Pleiotropic Genes in Coronary Artery Disease and Chronic Kidney Disease Using GWAS Summary Statistics. Front Genet, 2020, 11: 592461. DOI:10.3389/fgene.2020.592461
    37. Himmerich H, Bentley J, Kan C, et al. Genetic risk factors for eating disorders: an update and insights into pathophysiology. Ther Adv Psychopharmacol, 2019, 9: 2045125318814734. DOI:10.1177/2045125318814734
    38. Sanyal N, Lo MT, Kauppi K, et al. GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies. Bioinformatics, 2019, 35(1): 1-11. DOI:10.1093/bioinformatics/bty472
    39. Brinster R, Köttgen A, Tayo BO, et al. Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation. BMC Bioinformatics, 2018, 19(1): 78. DOI:10.1186/s12859-018-2081-x
    40. Brinster R, Köttgen A, Tayo BO, et al. Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation. BMC Bioinformatics, 2018, 19(1): 78. DOI:10.1186/s12859-018-2081-x
    41. Zeng P, Wang T, Huang S. Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models. Sci Rep, 2017, 7(1): 15237. DOI:10.1038/s41598-017-15055-8
    42. Zeng P, Zhou X, Huang S. Prediction of gene expression with cis-SNPs using mixed models and regularization methods. BMC Genomics, 2017, 18(1): 368. DOI:10.1186/s12864-017-3759-6
    43. Zeng P, Zhou X, Huang S. Prediction of gene expression with cis-SNPs using mixed models and regularization methods. BMC Genomics, 2017, 18(1): 368. DOI:10.1186/s12864-017-3759-6
    44. Zeng P, Wang T, Huang S. Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models. Sci Rep, 2017, 7(1): 15237. DOI:10.1038/s41598-017-15055-8
    45. Zhang Q, Zhao Y, Zhang R, et al. A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. PLoS One, 2016, 11(6): e0156895. DOI:10.1371/journal.pone.0156895
    46. Umehara H, Numata S, Tajima A, et al. Calcium Signaling Pathway Is Associated with the Long-Term Clinical Response to Selective Serotonin Reuptake Inhibitors (SSRI) and SSRI with Antipsychotics in Patients with Obsessive-Compulsive Disorder. PLoS One, 2016, 11(6): e0157232. DOI:10.1371/journal.pone.0157232
    47. Gasc C, Peyretaillade E, Peyret P. Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms. Nucleic Acids Res, 2016, 44(10): 4504-18. DOI:10.1093/nar/gkw309
    48. Zhang Q, Zhao Y, Zhang R, et al. A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. PLoS One, 2016, 11(6): e0156895. DOI:10.1371/journal.pone.0156895
    49. Gasc C, Peyretaillade E, Peyret P. Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms. Nucleic Acids Res, 2016, 44(10): 4504-18. DOI:10.1093/nar/gkw309

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