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RETRACTED ARTICLE: Identification of featured biomarkers in different types of lung cancer with DNA microarray

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This article was retracted on 18 August 2015

Abstract

Lung cancer is a worldwide leading cause of cancer-related death. The aim of this study was to identify target genes and specific biomarkers for identification and treatment of different types of lung cancer with DNA microarray. Gene expression profile GSE6044 and miRNA microarray profile GSE17681 were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and miRNAs were screened with multtest package in R language. Then, functional enrichment analysis of identified DEGs was performed. Furthermore, the verified target genes based on screened miRNAs were selected from miRTarBase and miRecords databases. Then miRNA-target gene regulation network was constructed. APOE, CDC6 and ATP2B1were involved in most of the functions obtained for adenocarcinomas, small cell lung cancer and squamous cell carcinomas, respectively. The target DEGs of differentially expressed hsa-miR-29a included FGG in adenocarcinoma, RAN and COL4A1 in small cell lung cancer, GLUL in squamous cell carcinoma. The target DEGs of has-miR-7 were SNCA and SLC7A5 in adenocarcinoma and small cell lung cancer, respectively. ICAM1 and KIT were the target DEGs of hsa-miR-222 in adenocarcinoma and squamous cell carcinoma. The miRNAs and their differentially expressed target genes have the potential to be used in clinic for diagnosis and treatment of different kinds of lung cancer in the future.

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References

  1. Boyle P, Chapman C, Holdenrieder S, Murray A, Robertson C, Wood W, Maddison P, Healey G, Fairley G, Barnes A (2011) Clinical validation of an autoantibody test for lung cancer. Ann Oncol 22(2):383–389

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90

    Article  PubMed  Google Scholar 

  3. Ettinger DS, Akerley W, Bepler G, Blum MG, Chang A, Cheney RT, Chirieac LR, D’Amico TA, Demmy TL, Ganti AKP (2010) Non–small cell lung cancer. J Natl Compr Canc Netw 8(7):740–801

    CAS  PubMed  Google Scholar 

  4. Ebert W, Muley T, Drings P (1996) Does the assessment of serum markers in patients with lung cancer aid in the clinical decision making process? Anticancer Res 16(4B):2161–2168

    CAS  PubMed  Google Scholar 

  5. Ando S, Suzuki M, Yamamoto N, Iida T, Kimura H (2004) The prognostic value of both neuron-specific enolase (NSE) and Cyfra21-1 in small cell lung cancer. Anticancer Res 24(3B):1941–1946

    CAS  PubMed  Google Scholar 

  6. Mulshine JL, Sullivan DC (2005) Lung cancer screening. N Engl J Med 352(26):2714–2720

    Article  CAS  PubMed  Google Scholar 

  7. Pastorino U (2010) Lung cancer screening. Br J Cancer 102(12):1681–1686

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Brennan P, Hainaut P, Boffetta P (2011) Genetics of lung-cancer susceptibility. Lancet Oncol 12(4):399–408. doi:10.1016/S1470-2045(10)70126-1

    Article  CAS  PubMed  Google Scholar 

  9. Wang L, Xiong Y, Sun Y, Fang Z, Li L, Ji H, Shi T (2010) HLungDB: an integrated database of human lung cancer research. Nucleic Acids Res 38(Suppl 1):D665–D669

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C (2010) Serum MicroRNA signatures identified in a genome-wide serum MicroRNA expression profiling predict survival of non–small-cell lung cancer. J Clin Oncol 28(10):1721–1726

    Article  PubMed  Google Scholar 

  11. Yao Z, Fenoglio S, Gao DC, Camiolo M, Stiles B, Lindsted T, Schlederer M, Johns C, Altorki N, Mittal V (2010) TGF-β IL-6 axis mediates selective and adaptive mechanisms of resistance to molecular targeted therapy in lung cancer. Proc Natl Acad Sci 107(35):15535–15540

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Spies M, Dasu MR, Svrakic N, Nesic O, Barrow RE, Perez-Polo JR, Herndon DN (2002) Gene expression analysis in burn wounds of rats. Am J Physiol Regul Integr Comp Physiol 283(4):R918–R930

    Article  PubMed  Google Scholar 

  13. Eb WDT, Muller-Hermelink H (2004) World Health Organization classification of tumors: Pathology and genetics of tumors of the lung, pleura, thymus and heart. IARC Press, Lyon

    Google Scholar 

  14. Rohrbeck A, Neukirchen J, Rosskopf M, Pardillos GG, Geddert H, Schwalen A, Gabbert HE, von Haeseler A, Pitschke G, Schott M (2008) Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers. J Transl Med 6:69

    Article  PubMed Central  PubMed  Google Scholar 

  15. Keller A, Leidinger P, Borries A, Wendschlag A, Wucherpfennig F, Scheffler M, Huwer H, Lenhof H-P, Meese E (2009) miRNAs in lung cancer-studying complex fingerprints in patient’s blood cells by microarray experiments. BMC Cancer 9(1):353

    Article  PubMed Central  PubMed  Google Scholar 

  16. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17(6):520–525

    Article  CAS  PubMed  Google Scholar 

  17. André F, João S, Leonardo R, Carlos F (2006) Evaluating different methods of microarray data normalization. BMC Bioinformatics 7(1):469

  18. Dudoit S, Shaffer JP, Boldrick JC (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 18(1):71–103

  19. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57(1):289–300

  20. Lewis CI, von Leibniz GWF (1918) A survey of symbolic logic. University of California Press, Berkeley, CA

    Google Scholar 

  21. Nam D, Kim S-Y (2008) Gene-set approach for expression pattern analysis. Brief Bioinform 9(3):189–197

    Article  PubMed  Google Scholar 

  22. Allison DB, Cui X, Page GP, Sabripour M (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7(1):55–65

    Article  CAS  PubMed  Google Scholar 

  23. Da Wei Huang BTS, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    Article  Google Scholar 

  24. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009) miRecords: an integrated resource for microRNA–target interactions. Nucleic Acids Res 37(Suppl 1):D105–D110

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Hsu S-D, Lin F-M, Wu W-Y, Liang C, Huang W-C, Chan W-L, Tsai W-T, Chen G-Z, Lee C-J, Chiu C-M (2011) miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res 39(Suppl 1):D163–D169

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Parkin DM, Bray F, Devesa S (2001) Cancer burden in the year 2000. The global picture. Eur J Cancer 37:4–66

    Article  Google Scholar 

  27. Pirozynski M (2009) 100 years of lung cancer (Retraction of vol 100, pg 2073, 2006). Respir Med 103(8):1244

    Article  Google Scholar 

  28. Wang Y, Chen Z, Chen J, Pan J, Zhang W, Pan Q, Ding H, Lin X, Wen X, Li Y (2013) The diagnostic value of apolipoprotein E in malignant pleural effusion associated with non-small cell lung cancer. Clin Chim Acta 421:230–235

  29. Su W-P, Chen Y-T, Lai W-W, Lin C-C, Yan J-J, Su W-C (2011) Apolipoprotein E expression promotes lung adenocarcinoma proliferation and migration and as a potential survival marker in lung cancer. Lung Cancer 71(1):28–33

    Article  PubMed  Google Scholar 

  30. Petersen BO, Wagener C, Marinoni F, Kramer ER, Melixetian M, Denchi EL, Gieffers C, Matteucci C, Peters J-M, Helin K (2000) Cell cycle–and cell growth–regulated proteolysis of mammalian CDC6 is dependent on APC–CDH1. Genes Dev 14(18):2330–2343

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Basaki Y, K-i Taguchi, Izumi H, Murakami Y, Kubo T, Hosoi F, Watari K, Nakano K, Kawaguchi H, Ohno S, Kohno K, Ono M, Kuwano M (2010) Y-box binding protein-1 (YB-1) promotes cell cycle progression through CDC6-dependent pathway in human cancer cells. Eur J Cancer 46(5):954–965. doi:10.1016/j.ejca.2009.12.024

    Article  CAS  PubMed  Google Scholar 

  32. Kobayashi Y, Hirawa N, Tabara Y, Muraoka H, Fujita M, Miyazaki N, Fujiwara A, Ichikawa Y, Yamamoto Y, Ichihara N, Saka S, Wakui H, Yoshida S-I, Yatsu K, Toya Y, Yasuda G, Kohara K, Kita Y, Takei K, Goshima Y, Ishikawa Y, Ueshima H, Miki T, Umemura S (2012) Mice lacking hypertension candidate Gene ATP2B1 in vascular smooth muscle cells show significant blood pressure elevation. Hypertension 59(4):854–860. doi:10.1161/hypertensionaha.110.165068

    Article  CAS  PubMed  Google Scholar 

  33. Monteith GR, McAndrew D, Faddy HM, Roberts-Thomson SJ (2007) Calcium and cancer: targeting Ca2+ transport. Nat Rev Cancer 7(7):519–530

    Article  CAS  PubMed  Google Scholar 

  34. Rabinowits G, Gerçel-Taylor C, Day JM, Taylor DD, Kloecker GH (2009) Exosomal microRNA: a diagnostic marker for lung cancer. Clinical Lung Cancer 10(1):42–46. doi:10.3816/CLC.2009.n.006

    Article  CAS  PubMed  Google Scholar 

  35. Xiong Y, Fang JH, Yun JP, Yang J, Zhang Y, Jia WH, Zhuang SM (2010) Effects of MicroRNA-29 on apoptosis, tumorigenicity, and prognosis of hepatocellular carcinoma. Hepatology 51(3):836–845

    CAS  PubMed  Google Scholar 

  36. Fort A, Borel C, Migliavacca E, Antonarakis SE, Fish RJ, Neerman-Arbez M (2010) Regulation of fibrinogen production by microRNAs. Blood 116(14):2608–2615

    Article  CAS  PubMed  Google Scholar 

  37. Cushing L, Kuang PP, Qian J, Shao F, Wu J, Little F, Thannickal VJ, Cardoso WV, Lü J (2011) miR-29 is a major regulator of genes associated with pulmonary fibrosis. Am J Respir Cell Mol Biol 45(2):287–294

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Plaisier CL, Pan M, Baliga NS (2012) A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Res 22(11):2302–2314

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Gilad S, Lithwick-Yanai G, Barshack I, Benjamin S, Krivitsky I, Edmonston TB, Bibbo M, Thurm C, Horowitz L, Huang Y, Feinmesser M, Steve Hou J, St Cyr B, Burnstein I, Gibori H, Dromi N, Sanden M, Kushnir M, Aharonov R (2012) Classification of the four main types of lung cancer using a microRNA-based diagnostic assay. J Mol Diagn 14(5):510–517. doi:10.1016/j.jmoldx.2012.03.004

    Article  CAS  PubMed  Google Scholar 

  40. Kaira K, Oriuchi N, Imai H, Shimizu K, Yanagitani N, Sunaga N, Hisada T, Tanaka S, Ishizuka T, Kanai Y (2008) Prognostic significance of L-type amino acid transporter 1 expression in resectable stage I-III nonsmall cell lung cancer. Br J Cancer 98(4):742–748

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Kido Y, Tamai I, Uchino H, Suzuki F, Sai Y, Tsuji A (2001) Molecular and functional identification of large neutral amino acid transporters LAT1 and LAT2 and their pharmacological relevance at the blood-brain barrier. J Pharm Pharmacol 53(4):497–503

    Article  CAS  PubMed  Google Scholar 

  42. Miko E, Margitai Z, Czimmerer Z, Várkonyi I, Dezső B, Lányi Á, Bacsó Z, Scholtz B (2011) miR-126 inhibits proliferation of small cell lung cancer cells by targeting SLC7A5. FEBS Lett 585(8):1191–1196

    Article  CAS  PubMed  Google Scholar 

  43. Arnold CP, Tan R, Zhou B, Yue S-B, Schaffert S, Biggs JR, Doyonnas R, Lo M-C, Perry JM, Renault VM (2011) MicroRNA programs in normal and aberrant stem and progenitor cells. Genome Res 21(5):798–810

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Lin Q, Mao W, Shu Y, Lin F, Liu S, Shen H, Gao W, Li S, Shen D (2012) A cluster of specified microRNAs in peripheral blood as biomarkers for metastatic non-small-cell lung cancer by stem-loop RT-PCR. J Cancer Res Clin Oncol 138(1):85–93

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Guo L, Ji X, Yang S, Hou Z, Luo C, Fan J, Ni C, Chen F (2013) Genome-wide analysis of aberrantly expressed circulating miRNAs in patients with coal workers’ pneumoconiosis. Mol Biol Rep 40(5):3739–3747. doi:10.1007/s11033-012-2450-x

    Article  CAS  PubMed  Google Scholar 

  46. Thanopoulou E, Kotzamanis G, Pateras I, Ziras N, Papalambros A, Mariolis-Sapsakos T, Sigala F, Johnson E, Kotsinas A, Scorilas A, Gorgoulis V (2012) The single nucleotide polymorphism g.1548A > G (K469E) of the ICAM-1 gene is associated with worse prognosis in non-small cell lung cancer. Tumor Biol 33(5):1429–1436. doi:10.1007/s13277-012-0393-4

    Article  CAS  Google Scholar 

  47. Guney N, Soydinc HO, Derin D, Tas F, Camlica H, Duranyildiz D, Yasasever V, Topuz E (2008) Serum levels of intercellular adhesion molecule ICAM-1 and E-selectin in advanced stage non-small cell lung cancer. Med Oncol 25(2):194–200

    Article  CAS  PubMed  Google Scholar 

  48. Blagosklonny MV, Pardee AB (2001) Exploiting cancer cell cycling for selective protection of normal cells. Cancer Res 61(11):4301–4305

    CAS  PubMed  Google Scholar 

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Acknowledgments

We wish to express our warm thanks to all the authors who contributed a lot to the research.

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Correspondence to Chao Zhou or Liang-an Chen.

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The Publisher and Editor retract this article in accordance with the recommendations of the Committee on Publication Ethics (COPE). After a thorough investigation we have strong reason to believe that the peer review process was compromised.

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Zhou, C., Chen, H., Han, L. et al. RETRACTED ARTICLE: Identification of featured biomarkers in different types of lung cancer with DNA microarray. Mol Biol Rep 41, 6357–6363 (2014). https://doi.org/10.1007/s11033-014-3515-9

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  • DOI: https://doi.org/10.1007/s11033-014-3515-9

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