Citation: | Wang Jing, He Xuezhi, Lu Xiyi, Amin Karim Muhammad, Miao Dengshun, Zhang Erbao. A novel long non-coding RNA NFIA-AS1 is down-regulated in gastric cancer and inhibits proliferation of gastric cancer cells[J]. The Journal of Biomedical Research, 2019, 33(6): 371-381. DOI: 10.7555/JBR.33.20190015 |
[1] |
Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65: 87–108. doi: 10.3322/caac.21262
|
[2] |
Young JA, Shimi SM, Kerr L, et al. Reduction in gastric cancer surgical mortality over 10 years: An adverse events analysis[J]. Ann Med Surg (Lond), 2014, 3: 26–30. doi: 10.1016/j.amsu.2014.03.003
|
[3] |
Milne AN, Carneiro F, O'Morain C, et al. Nature meets nurture: molecular genetics of gastric cancer[J]. Hum Genet, 2009, 126: 615–28. doi: 10.1007/s00439-009-0722-x
|
[4] |
Slaby O, Laga R, Sedlacek O. Therapeutic targeting of non-coding RNAs in cancer[J]. Biochem J, 2017, 474: 4219–4251. doi: 10.1042/BCJ20170079
|
[5] |
Beermann J, Piccoli MT, Viereck J, et al. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches[J]. Physiol Rev, 2016, 96: 1297–325. doi: 10.1152/physrev.00041.2015
|
[6] |
Gu W, Gao T, Sun Y, et al. LncRNA expression profile reveals the potential role of lncRNAs in gastric carcinogenesis[J]. Cancer Biomark, 2015, 15: 249–58. doi: 10.3233/CBM-150460
|
[7] |
Muers M. RNA: Genome-wide views of long non-coding RNAs[J]. Nat Rev Genet, 2011, 12: 742.
|
[8] |
Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs[J]. Cell, 2009, 136: 629–41. doi: 10.1016/j.cell.2009.02.006
|
[9] |
Rinn JL, Kertesz M, Wang JK, et al. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs[J]. Cell, 2007, 129: 1311–23. doi: 10.1016/j.cell.2007.05.022
|
[10] |
Gupta RA, Shah N, Wang KC, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis[J]. Nature, 2010, 464: 1071–6. doi: 10.1038/nature08975
|
[11] |
Gutschner T, Hammerle M, Eissmann M, et al. The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells[J]. Cancer Res, 2013, 73: 1180–9. doi: 10.1158/0008-5472.CAN-12-2850
|
[12] |
Yang Q, Zhang RW, Sui PC, et al. Dysregulation of non-coding RNAs in gastric cancer[J]. World J Gastroenterol, 2015, 21: 10956–81. doi: 10.3748/wjg.v21.i39.10956
|
[13] |
Salmena L, Poliseno L, Tay Y, et al. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?[J]. Cell, 2011, 146: 353–8. doi: 10.1016/j.cell.2011.07.014
|
[14] |
Zhang EB, Han L, Yin DD, et al. c-Myc-induced, long, noncoding H19 affects cell proliferation and predicts a poor prognosis in patients with gastric cancer[J]. Med Oncol, 2014, 31: 914. doi: 10.1007/s12032-014-0914-7
|
[15] |
Zhou X, Yin C, Dang Y, et al. Identification of the long non-coding RNA H19 in plasma as a novel biomarker for diagnosis of gastric cancer[J]. Sci Rep, 2015, 5: 11516. doi: 10.1038/srep11516
|
[16] |
Yang F, Bi J, Xue X, et al. Up-regulated long non-coding RNA H19 contributes to proliferation of gastric cancer cells[J]. FEBS J, 2012, 279: 3159–65. doi: 10.1111/j.1742-4658.2012.08694.x
|
[17] |
Li H, Yu B, Li J, et al. Overexpression of lncRNA H19 enhances carcinogenesis and metastasis of gastric cancer[J]. Oncotarget, 2014, 5: 2318–29.
|
[18] |
Yin D, He X, Zhang E, et al. Long noncoding RNA GAS5 affects cell proliferation and predicts a poor prognosis in patients with colorectal cancer[J]. Med Oncol, 2014, 31: 253. doi: 10.1007/s12032-014-0253-8
|
[19] |
Zhang E, He X, Zhang C, et al. A novel long noncoding RNA HOXC-AS3 mediates tumorigenesis of gastric cancer by binding to YBX1[J]. Genome Biol, 2018, 19: 154. doi: 10.1186/s13059-018-1523-0
|
[20] |
Tsai MC, Spitale RC, Chang HY. Long intergenic noncoding RNAs: new links in cancer progression[J]. Cancer Res, 2011, 71: 3–7. doi: 10.1158/0008-5472.CAN-10-2483
|
[21] |
Gibb EA, Brown CJ, Lam WL. The functional role of long non-coding RNA in human carcinomas[J]. Mol Cancer, 2011, 10: 38. doi: 10.1186/1476-4598-10-38
|
[22] |
Zhu Q, Lv T, Wu Y, et al. Long non-coding RNA 00312 regulated by HOXA5 inhibits tumour proliferation and promotes apoptosis in non-small cell lung cancer[J]. J Cell Mol Med, 2017, 21: 2184–2198. doi: 10.1111/jcmm.13142
|
[23] |
Zhang E, Yin D, Han L, et al. E2F1-induced upregulation of long noncoding RNA LINC00668 predicts a poor prognosis of gastric cancer and promotes cell proliferation through epigenetically silencing of CKIs[J]. Oncotarget, 2016, 7: 23212–26.
|
[24] |
Liu D, Xia P, Diao D, et al. MiRNA-429 suppresses the growth of gastric cancer cells in vitro[J]. J Biomed Res, 2012, 26: 389–93. doi: 10.7555/JBR.26.20120029
|
[25] |
Dan J, Wang J, Wang Y, et al. LncRNA-MEG3 inhibits proliferation and metastasis by regulating miRNA-21 in gastric cancer[J]. Biomed Pharmacother, 2018, 99: 931–938. doi: 10.1016/j.biopha.2018.01.164
|
[26] |
Yang J, Li C, Mudd A, et al. LncRNA PVT1 predicts prognosis and regulates tumor growth in prostate cancer[J]. Biosci Biotechnol Biochem, 2017, 81: 2301–2306. doi: 10.1080/09168451.2017.1387048
|
[27] |
Song P, Jiang B, Liu Z, et al. A three-lncRNA expression signature associated with the prognosis of gastric cancer patients[J]. Cancer Med, 2017, 6: 1154–1164. doi: 10.1002/cam4.1047
|
[28] |
Song W, Wang K, Zou SB. UCA1 lncRNA in metastases and prognosis[J]. Panminerva Med, 2017, 59: 278–279.
|
[29] |
Kotake Y, Naemura M, Murasaki C, et al. Transcriptional Regulation of the p16 Tumor Suppressor Gene[J]. Anticancer Res, 2015, 35: 4397–401.
|
[30] |
Dickson MA. Molecular pathways: CDK4 inhibitors for cancer therapy[J]. Clin Cancer Res, 2014, 20: 3379–83. doi: 10.1158/1078-0432.CCR-13-1551
|
[31] |
Gopalan PK, Villegas AG, Cao C, et al. CDK4/6 inhibition stabilizes disease in patients with p16-null non-small cell lung cancer and is synergistic with mTOR inhibition[J]. Oncotarget, 2018, 9: 37352–37366.
|
[32] |
Mou H, Yu L, Zheng X, et al. p16 gene expression in pancreatic cancer tissue and its importance in diagnosis[J]. J Biol Regul Homeost Agents, 2017, 31: 1043–1047.
|
[33] |
Sang Y, Tang J, Li S, et al. LncRNA PANDAR regulates the G1/S transition of breast cancer cells by suppressing p16(INK4A) expression[J]. Sci Rep, 2016, 6: 22366. doi: 10.1038/srep22366
|
[34] |
Kong R, Zhang EB, Yin DD, et al. Long noncoding RNA PVT1 indicates a poor prognosis of gastric cancer and promotes cell proliferation through epigenetically regulating p15 and p16[J]. Mol Cancer, 2015, 14: 82. doi: 10.1186/s12943-015-0355-8
|
[35] |
Xu TP, Wang YF, Xiong WL, et al. E2F1 induces TINCR transcriptional activity and accelerates gastric cancer progression via activation of TINCR/STAU1/CDKN2B signaling axis[J]. Cell Death Dis, 2017, 8: e2837. doi: 10.1038/cddis.2017.205
|
[1] | Yifei Cheng, Rongjie Shi, Shuai Ben, Silu Chen, Shuwei Li, Junyi Xin, Meilin Wang, Gong Cheng. Genetic variation of circHIBADH enhances prostate cancer risk through regulating HNRNPA1-related RNA splicing[J]. The Journal of Biomedical Research, 2024, 38(4): 358-368. DOI: 10.7555/JBR.38.20240030 |
[2] | 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 |
[3] | Zhu Chenchen, Jiang Haonan, Deng Wenjie, Zhao Shuo, Li Kaiquan, Wang Yuting, Wei Qinjun, Du Jun. Activation of p38/HSP27 pathway counters melatonin-induced inhibitory effect on proliferation of human gastric cancer cells[J]. The Journal of Biomedical Research, 2019, 33(5): 317-324. DOI: 10.7555/JBR.33.20180066 |
[4] | Jia Meiqun, Ren Lulu, Hu Lingmin, Ma Hongxia, Jin Guangfu, Li Dake, Li Ni, Hu Zhibin, Hang Dong. Association of long non-coding RNA HOTAIR and MALAT1 variants with cervical cancer risk in Han Chinese women[J]. The Journal of Biomedical Research, 2019, 33(5): 308-316. DOI: 10.7555/JBR.33.20180096 |
[5] | Xu Yuyu, Wang Pengqi, Xu Chaoqi, Shan Xiaoyun, Feng Qing. Acrylamide induces HepG2 cell proliferation through upregulation of miR-21 expression[J]. The Journal of Biomedical Research, 2019, 33(3): 181-191. DOI: 10.7555/JBR.31.20170016 |
[6] | Lintao Wang, Yanyan Peng, Kaikai Shi, Haixiao Wang, Jianlei Lu, Yanli Li, Changyan Ma. Osthole inhibits proliferation of human breast cancer cells by inducing cell cycle arrest and apoptosis[J]. The Journal of Biomedical Research, 2015, 29(2): 132-138. DOI: 10.7555/JBR.27.20120115 |
[7] | Mengying Liu, Yao Hu, Lijuan Zhu, Chen Chen, Yu Zhang, Weixiang Sun, Qigang Zhou. Overexpression of the mTERT gene by adenoviral vectors promotes the proliferation of neuronal stem cells in vitro and stimulates neurogenesis in the hippocampus of mice[J]. The Journal of Biomedical Research, 2012, 26(5): 381-388. DOI: 10.7555/JBR.26.20110078 |
[8] | Wen Qiu, Yan Li, Jianbo Zhou, Chenhui Zhao, Jing Zhang, Kai Shan, Dan Zhao, Yingwei Wang. TSP-1 promotes glomerular mesangial cell proliferation and extracellular matrix secretion in Thy-1 nephritis rats[J]. The Journal of Biomedical Research, 2011, 25(6): 402-410. DOI: 10.1016/S1674-8301(11)60053-5 |
[9] | Ping Li, Xiaoming Lu, Yingyi Wang, Lihua Sun, Chunfa Qian, Wei Yan, Ning Liu, Yongping You, Zhen Fu. MiR-181b suppresses proliferation of and reduces chemoresistance to temozolomide in U87 glioma stem cells[J]. The Journal of Biomedical Research, 2010, 24(6): 436-443. DOI: 10.1016/S1674-8301(10)60058-9 |
[10] | Tingting Zhao, Ren Zhang, Mingbo Wang. Prediction of candidate small non-coding RNAs in Agrobacterium by computational analysis[J]. The Journal of Biomedical Research, 2010, 24(1): 33-42. |
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 |