4.6

CiteScore

2.2

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
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
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

A novel long non-coding RNA NFIA-AS1 is down-regulated in gastric cancer and inhibits proliferation of gastric cancer cells

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  • Corresponding author:

    Dengshun Miao, Departments of Anatomy, Histology and Embryology, the Research Center for Bone and Stem Cells, Nanjing Medical University, Nanjing, Jiangsu 211166, China, E-mail: dsmiao@njmu.edu.cn

    Erbao Zhang, Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Nanjing Medical University, Nanjing, Jiangsu 211166, China, E-mail: erbaozhang@njmu.edu.cn

  • Received Date: January 27, 2019
  • Revised Date: March 31, 2019
  • Accepted Date: May 05, 2019
  • Available Online: July 30, 2019
  • Gastric cancer is one of the most common malignant gastrointestinal tumors whose morbidity and mortality account for the second and third place respectively in malignant tumors in China. As an important participant in tumor biology, the abnormal expression of long non-coding RNA (lncRNAs) in cancer cells is closely related to the occurrence and development of tumors and plays the role of oncogenes or tumor suppressor genes. In this study, we identified a novel lncRNA NFIA antisense RNA 1 (NFIA-AS1) and explored its role and clinical significance in gastric cancer. Real-time quantitative PCR was performed to detect the expression of NFIA-AS1 in tumor tissues and corresponding normal tissues from 42 pairs of gastric cancer samples. The lower expression of NFIA-AS1 was significantly associated with larger tumor size, lower histological grade, and advanced TNM stage. Kaplan-meier analysis showed that NFIA-AS1 expression could be used as an independent predictor of overall survival. We also demonstrated that overexpression of NFIA-AS1 significantly inhibited the proliferation of gastric cancer cells through affecting p16 levels. In conclusion, our results suggest that the lncRNA NFIA-AS1 may play the role of tumor suppressor gene, and serve as a biomarker for prognosis or progression of gastric cancer.
  • [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
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    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

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