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 |
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
|
[1] | Fei Qin, Hao Yu, Changrong Xu, Huihui Chen, Jianling Bai. Safety of axitinib and sorafenib monotherapy for patients with renal cell carcinoma: a meta-analysis[J]. The Journal of Biomedical Research, 2018, 32(1): 30-38. DOI: 10.7555/JBR.32.20170080 |
[2] | Qian Liu, Cheng Xu, Guixiang Ji, Hui Liu, Wentao Shao, Chunlan Zhang, Aihua Gu, Peng Zhao. Effect of exposure to ambient PM2.5 pollution on the risk of respiratory tract diseases: a meta-analysis of cohort studies[J]. The Journal of Biomedical Research, 2017, 31(2): 130-142. DOI: 10.7555/JBR.31.20160071 |
[3] | Wei Qian, Kuanfeng Xu, Wenting Jia, Ling Lan, Xuqin Zheng, Xueyang Yang, Dai Cui. Association between TSHR gene polymorphism and the risk of Graves' disease: a meta-analysis[J]. The Journal of Biomedical Research, 2016, 30(6): 466-475. DOI: 10.7555/JBR.30.20140144 |
[4] | Jinshan Tang, Ziqiang Zhu, Tao Sui, Dechao Kong, Xiaojian Cao. Position and complications of pedicle screw insertion with or without image-navigation techniques in the thoracolumbar spine: a meta-analysis of comparative studies[J]. The Journal of Biomedical Research, 2014, 28(3): 228-239. DOI: 10.7555/JBR.28.20130159 |
[5] | Wenze Sun, Liping Song, Ting Ai, Yingbing Zhang, Ying Gao, Jie Cui. Prognostic value of MET, cyclin D1 and MET gene copy number in non-small cell lung cancer[J]. The Journal of Biomedical Research, 2013, 27(3): 220-230. DOI: 10.7555/JBR.27.20130004 |
[6] | Zhiqiang Yin, Jiali Xu, Dan Luo. Efficacy and tolerance of tacrolimus and pimecrolimus for atopic dermatitis: a meta-analysis[J]. The Journal of Biomedical Research, 2011, 25(6): 385-391. DOI: 10.1016/S1674-8301(11)60051-1 |
[7] | Liang Zong, Ping Chen, Yinbing Chen, Guohao Shi. Pouch Roux-en-Y vs No Pouch Roux-en-Y following total gastrectomy: a meta-analysis based on 12 studies[J]. The Journal of Biomedical Research, 2011, 25(2): 90-99. DOI: 10.1016/S1674-8301(11)60011-0 |
[8] | Lifeng Zhang, Ning Shao, Qianqian Yu, Lixin Hua, Yuanyuan Mi, Ninghan Feng. Association between p53 Pro72Arg polymorphism and prostate cancer risk: a meta-analysis[J]. The Journal of Biomedical Research, 2011, 25(1): 25-32. DOI: 10.1016/S1674-8301(11)60003-1 |
[9] | Yuanyuan Mi, Qianqian Yu, Zhichao Min, Bin Xu, Lifeng Zhang, Wei Zhang, Ninghan Feng, Lixin Hua. Arg462Gln and Asp541Glu polymorphisms in ribonuclease L and prostate cancer risk: a meta-analysis[J]. The Journal of Biomedical Research, 2010, 24(5): 365-373. DOI: 10.1016/S1674-8301(10)60049-8 |
[10] | Bingbing Wei, Yunyun Zhang, Bo Xi, Junkai Chang, Jinming Bai, Jiantang Su. CYP17 T27C polymorphism and prostate cancer risk:a meta-analysis based on 31 studies[J]. The Journal of Biomedical Research, 2010, 24(3): 233-241. |
1. | Hou W, Guan F, Chen W, et al. Breastfeeding, genetic susceptibility, and the risk of asthma and allergic diseases in children and adolescents: a retrospective national population-based cohort study. BMC Public Health, 2024, 24(1): 3056. DOI:10.1186/s12889-024-20501-0 |
2. | Nandi S, Varotariya K, Luhana S, et al. GWAS for identification of genomic regions and candidate genes in vegetable crops. Funct Integr Genomics, 2024, 24(6): 203. DOI:10.1007/s10142-024-01477-x |
3. | Sung HL, Lin WY. Causal effects of cardiovascular health on five epigenetic clocks. Clin Epigenetics, 2024, 16(1): 134. DOI:10.1186/s13148-024-01752-5 |
4. | 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 |
5. | 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 |
6. | 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 |
7. | 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 |
8. | Han J, Zhang L, Yan R, et al. CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies-With Applications to Studies of Breast Cancer. Genes (Basel), 2023, 14(3): 586. DOI:10.3390/genes14030586 |
9. | 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 |
10. | 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 |
11. | 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 |
12. | Shao Z, Wang T, Qiao J, et al. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics, 2022, 23(1): 359. DOI:10.1186/s12859-022-04897-3 |
13. | 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 |
14. | 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 |
15. | 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 |
16. | 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 |
17. | 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 |
18. | 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 |
19. | 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 |
20. | Gao Y, Zhang J, Zhao H, et al. Instrumental Heterogeneity in Sex-Specific Two-Sample Mendelian Randomization: Empirical Results From the Relationship Between Anthropometric Traits and Breast/Prostate Cancer. Front Genet, 2021, 12: 651332. DOI:10.3389/fgene.2021.651332 |
21. | 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 |
22. | Muneeb M, Henschel A. Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods. BMC Bioinformatics, 2021, 22(1): 198. DOI:10.1186/s12859-021-04077-9 |
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. | Scossa F, Fernie AR. Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?. Comput Struct Biotechnol J, 2021, 19: 1579-1594. DOI:10.1016/j.csbj.2021.03.008 |
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. | 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 |
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. | Ramanan VK, Wang X, Przybelski SA, et al. Variants in PPP2R2B and IGF2BP3 are associated with higher tau deposition. Brain Commun, 2020, 2(2): fcaa159. DOI:10.1093/braincomms/fcaa159 |
29. | 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 |
30. | Xiao L, Yuan Z, Jin S, et al. Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis. Front Genet, 2020, 11: 587243. DOI:10.3389/fgene.2020.587243 |
31. | 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 |
32. | Kuo TT, Jiang X, Tang H, et al. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Med Genomics, 2020, 13(Suppl 7): 98. DOI:10.1186/s12920-020-0715-0 |
33. | Padilla-Martínez F, Collin F, Kwasniewski M, et al. Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes. Int J Mol Sci, 2020, 21(5): 1703. DOI:10.3390/ijms21051703 |
34. | Lan T, Yang B, Zhang X, et al. Statistical Methods and Software for Substance Use and Dependence Genetic Research. Curr Genomics, 2019, 20(3): 172-183. DOI:10.2174/1389202920666190617094930 |
35. | Gaudillo J, Rodriguez JJR, Nazareno A, et al. Machine learning approach to single nucleotide polymorphism-based asthma prediction. PLoS One, 2019, 14(12): e0225574. DOI:10.1371/journal.pone.0225574 |
36. | Romagnoni A, Jégou S, Van Steen K, et al. Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data. Sci Rep, 2019, 9(1): 10351. DOI:10.1038/s41598-019-46649-z |
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. | 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 |
41. | 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 |
42. | Läll K, Mägi R, Morris A, et al. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med, 2017, 19(3): 322-329. DOI:10.1038/gim.2016.103 |
43. | 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 |
44. | 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 |
45. | 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 |