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Integrative analysis of microRNA and mRNA expression profiles in non-small-cell lung cancer

Cancer Gene Therapy volume 23, pages 90–97 (2016)Cite this article

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Abstract

Non-small-cell lung cancer (NSCLC) represents the most common deadly disease. Emerging evidences suggest that abnormal epigenetic modulation via mRNAs and microRNAs (miRNAs) might be involved in the tumorigenesis. To explore novel therapeutic target of NSCLC, a more detailed mRNAs and miRNA expression profiling study is needed. High-quality total RNA including miRNA was isolated from NSCLC tissue and para-carcinoma tissue and used for RNA and small RNA sequencing. Results were analyzed bioinformatically and validated using quantitative real-time (qRT)-PCR. A total of 3530 genes (1977 up-regulated and 1553 down-regulated) and 211 miRNAs (171 up-regulated and 30 down-regulated) were differentially expressed (DE) in NSCLC tissue versus adjacent normal tissues. Furthermore, 157 novel miRNAs were predicted in our samples. Of these, 918 significant miRNA–mRNA pairs were identified, consisting of 100 miRNAs and 443 mRNAs. Gene ontology analysis revealed that most of the target genes were enriched in the terms of plasma membrane, binding, and multiple biological-molecular signaling processes. Pathway analysis of these miRNA signatures highlights their critical roles in calcium signaling pathway. Using qRT-PCR, the expression of several DE genes (KRAS and RBM5) and miRNAs (miR-1-5p, let-7b-5p, miR-21-5p, miR-1290, miR-149-5p, chr8_28846, chrX_31594, and chr9_29897) were confirmed. The integrative analysis based on mRNA and miRNA profiling may provide more potential molecular for the tumorigenesis and development of NSCLC.

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References

  1. Siegel R, Naishadham D, Jemal A . Cancer statistics. 2012 CA Cancer J Clin 2012; 62: 10–29.

    PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  3. Camidge DR, Pao W, Sequist LV . Acquired resistance to TKIs in solid tumours: learning from lung cancer. Nat Rev Clin Oncol 2014; 11: 473–481.

    Article  CAS  PubMed  Google Scholar 

  4. Hoffman PC, Mauer AM, Vokes EE . Lung cancer. Lancet 2000; 355: 479–485.

    Article  CAS  PubMed  Google Scholar 

  5. Mountain CF . Revisions in the international system for staging lung cancer. Chest 1997; 111: 1710–1717.

    Article  CAS  PubMed  Google Scholar 

  6. Naruke T, Goya T, Tsuchiya R, Suemasu K . Prognosis and survival in resected lung carcinoma based on the new international staging system. J Thorac Cardiovasc Surg 1988; 96: 440–447.

    CAS  PubMed  Google Scholar 

  7. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N . Widespread changes in protein synthesis induced by microRNAs. Nature 2008; 455: 58–63.

    Article  CAS  PubMed  Google Scholar 

  8. Bartel DP . MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116: 281–297.

    Article  CAS  PubMed  Google Scholar 

  9. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ . miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006; 34: D140–D144.

    Article  CAS  PubMed  Google Scholar 

  10. Friedman RC, Farh KK, Burge CB, Bartel DP . Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 2009; 19: 92–105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tang JT, Fang JY . MicroRNA regulatory network in human colorectal cancer. Mini Rev Med Chem 2009; 9: 921–926.

    Article  CAS  PubMed  Google Scholar 

  12. Tsuchiya S, Okuno Y, Tsujimoto G . MicroRNA: biogenetic and functional mechanisms and involvements in cell differentiation and cancer. J Pharmacol Sci 2006; 101: 267–270.

    Article  CAS  PubMed  Google Scholar 

  13. Sassen S, Miska EA, Caldas C . MicroRNA: implications for cancer. Virchows Arch 2008; 452: 1–10.

    Article  CAS  PubMed  Google Scholar 

  14. Chomczynski P, Sacchi N . Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 1987; 162: 156–159.

    Article  CAS  PubMed  Google Scholar 

  15. Trapnell C, Pachter L, Salzberg SL . TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009; 25: 1105–1111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Langmead B, Trapnell C, Pop M, Salzberg SL . Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009; 10: R25.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008; 26: 407–415.

    Article  PubMed  Google Scholar 

  18. Perdomo C, Campbell JD, Gerrein J, Tellez CS, Garrison CB, Walser TC et al. MicroRNA 4423 is a primate-specific regulator of airway epithelial cell differentiation and lung carcinogenesis. Proc Natl Acad Sci USA 2013; 110: 18946–18951.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kozomara A, Griffiths-Jones S . miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011; 39: D152–D157.

    Article  CAS  PubMed  Google Scholar 

  20. Quinlan AR, Hall IM . BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010; 26: 841–842.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nikolayeva O, Robinson MD . edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology. Methods Mol Biol 2014; 1150: 45–79.

    Article  CAS  PubMed  Google Scholar 

  22. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ et al. Combinatorial microRNA target predictions. Nat Genet 2005; 37: 495–500.

    Article  CAS  PubMed  Google Scholar 

  23. Lewis BP, Burge CB, Bartel DP . Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120: 15–20.

    CAS  PubMed  Google Scholar 

  24. Lionetti M, Biasiolo M, Agnelli L, Todoerti K, Mosca L, Fabris S et al. Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma. Blood 2009; 114: e20–e26.

    Article  CAS  PubMed  Google Scholar 

  25. Li Y, Xu J, Chen H, Bai J, Li S, Zhao Z et al. Comprehensive analysis of the functional microRNA-mRNA regulatory network identifies miRNA signatures associated with glioma malignant progression. Nucleic Acids Res 2013; 41: e203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang, da W, Sherman BT, Lempicki RA . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4: 44–57.

    Article  CAS  Google Scholar 

  27. Huang, da W, Sherman BT, Lempicki RA . Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37: 1–13.

    Article  Google Scholar 

  28. Han SS, Kim WJ, Hong Y, Hong SH, Lee SJ, Ryu DR et al. RNA sequencing identifies novel markers of non-small cell lung cancer. Lung Cancer 2014; 84: 229–235.

    Article  PubMed  Google Scholar 

  29. Ma J, Mannoor K, Gao L, Tan A, Guarnera MA, Zhan M et al. Characterization of microRNA transcriptome in lung cancer by next-generation deep sequencing. Mol Oncol 2014; 8: 1208–1219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sun X, Song Y, Tai X, Liu B, Ji W . MicroRNA expression and its detection in human supraglottic laryngeal squamous cell carcinoma. Biomed Rep 2013; 1: 743–746.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang B, Chen J, Ren Z, Chen Y, Li J, Miao X et al. A specific miRNA signature promotes radioresistance of human cervical cancer cells. Cancer Cell Int 2013; 13: 118.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Li A, Yu J, Kim H, Wolfgang CL, Canto MI, Hruban RH et al. MicroRNA array analysis finds elevated serum miR-1290 accurately distinguishes patients with low-stage pancreatic cancer from healthy and disease controls. Clin Cancer Res 2013; 19: 3600–3610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Belian E, Kurucz R, Treue D, Lage H . Effect of YB-1 on the regulation of micro RNA expression in drug-sensitive and drug-resistant gastric carcinoma cells. Anticancer Res 2010; 30: 629–633.

    CAS  PubMed  Google Scholar 

  34. Endo Y, Toyama T, Takahashi S, Yoshimoto N, Iwasa M, Asano T et al. miR-1290 and its potential targets are associated with characteristics of estrogen receptor alpha-positive breast cancer. Endocr Relat Cancer 2013; 20: 91–102.

    Article  CAS  PubMed  Google Scholar 

  35. Wu J, Ji X, Zhu L, Jiang Q, Wen Z, Xu S et al. Up-regulation of microRNA-1290 impairs cytokinesis and affects the reprogramming of colon cancer cells. Cancer Lett 2013; 329: 155–163.

    Article  CAS  PubMed  Google Scholar 

  36. Liu H, Brannon AR, Reddy AR, Alexe G, Seiler MW, Arreola A et al. Identifying mRNA targets of microRNA dysregulated in cancer: with application to clear cell Renal Cell Carcinoma. BMC Syst Biol 2010; 4: 51.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Li D, Chen P, Li XY, Zhang LY, Xiong W, Zhou M et al. Grade-specific expression profiles of miRNAs/mRNAs and docking study in human grade I-III astrocytomas. OMICS 2011; 15: 673–682.

    Article  CAS  PubMed  Google Scholar 

  38. Schaefer A, Jung M, Mollenkopf HJ, Wagner I, Stephan C, Jentzmik F et al. Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer 2010; 126: 1166–1176.

    CAS  PubMed  Google Scholar 

  39. Luo Z, Zhang L, Li Z, Li X, Li G, Yu H et al. An in silico analysis of dynamic changes in microRNA expression profiles in stepwise development of nasopharyngeal carcinoma. BMC Med Genomics 2012; 5: 3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jin L, Hu WL, Jiang CC, Wang JX, Han CC, Chu P et al. MicroRNA-149*, a p53-responsive microRNA, functions as an oncogenic regulator in human melanoma. Proc Natl Acad Sci USA 2011; 108: 15840–15845.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Molina-Pinelo S, Gutierrez G, Pastor MD, Hergueta M, Moreno-Bueno G, Garcia-Carbonero R et al. MicroRNA-dependent regulation of transcription in non-small cell lung cancer. PLoS One 2014; 9: e90524.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Sun CC, Liu ZD, Li SJ, Yang CL, Xue R, Xi Y et al. Down-regulation of c-Met and Bcl2 by microRNA-206, activates apoptosis, and inhibits tumor cell proliferation, migration and colony formation. Oncotarget 2015; 6: 25533–25574.

    PubMed  PubMed Central  Google Scholar 

  43. Sun CC, Sang M, Li SJ, Sun XD, Yang CL, Xi Y et al. Hsa-miR-139-5p inhibits proliferation and causes apoptosis associated with down-regulation of c-Met. Oncotarget 2015; 6: 39756–39792.

    PubMed  PubMed Central  Google Scholar 

  44. Berridge MJ, Lipp P, Bootman MD . The versatility and universality of calcium signalling. Nat Rev Mol Cell Biol 2000; 1: 11–21.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by a grant from the National Science Foundation of China (no. 81271943) and The Plan for Scientific and Technological Innovation Team of High-tech Industries of Wuhan Municipal Science and Technology Bureau (no. 2015070504020219) to Dejia Li. We thank ABlife. Inc (Wuhan, China) for the support and assistance of this manuscript.

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  1. C Yang and C Sun: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Occupational and Environmental Health, School of Public Health, Wuhan University, Wuhan, China

    C Yang, C Sun, X Liang & D Li

  2. Department of Thoracic Surgery, People’s Hospital of Wuhan University, Wuhan, China

    S Xie & J Huang

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  1. C Yang
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Correspondence to D Li.

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Yang, C., Sun, C., Liang, X. et al. Integrative analysis of microRNA and mRNA expression profiles in non-small-cell lung cancer. Cancer Gene Ther 23, 90–97 (2016). https://doi.org/10.1038/cgt.2016.5

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