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Comparison of the Gene Expression Profiles Between Smokers With and Without Lung Cancer Using RNA-Seq

  • Cheng, Peng ;
  • Cheng, You ;
  • Li, Yan ;
  • Zhao, Zhenguo ;
  • Gao, Hui ;
  • Li, Dong ;
  • Li, Hua ;
  • Zhang, Tao
  • Published : 2012.08.31

Abstract

Lung cancer seriously threatens human health, so it is important to investigate gene expression changes in affected individuals in comparison with healthy people. Here we compared the gene expression profiles between smokers with and without lung cancer. We found that the majority of the expressed genes (threshold was set as 0.1 RPKM) were the same in the two samples, with a small portion of the remainder being unique to smokers with and without lung cancer. Expression distribution patterns showed that most of the genes in smokers with and without lung cancer are expressed at low or moderate levels. We also found that the expression levels of the genes in smokers with lung cancer were lower than in smokers without lung cancer in general. Then we detected 27 differentially expressed genes in smokers with versus without lung cancer, and these differentially expressed genes were foudn to be involved in diverse processes. Our study provided detail expression profiles and expression changes between smokers with and without lung cancer.

Keywords

Genes;lung cancer;smokers;gene expression profiles;RNA-Seq

References

  1. Anders S, Huber W (2010). Differential expression analysis for sequence count data. Genome Biol, 11, R106. https://doi.org/10.1186/gb-2010-11-10-r106
  2. Velcich A, Yang WC, Heyer J, et al (2002). Colorectal cancer in mice genetically deficient in the mucin Muc2. Science, 295, 1726-9. https://doi.org/10.1126/science.1069094
  3. Beane J VJ, Schembri F, Anderlind C, et al (2011). Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq. Cancer Prev Res (Phila), 4, 803-17. https://doi.org/10.1158/1940-6207.CAPR-11-0212
  4. Biesalski HK, Bueno de Mesquita B, Chesson A, et al (1998). European consensus statement on lung cancer: risk factors and prevention. lung cancer panel. CA Cancer J Clin, 48, 167-76; discussion 4-6. https://doi.org/10.3322/canjclin.48.3.167
  5. Chepelev I, Wei G, Tang Q, Zhao K (2009). Detection of single nucleotide varisations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res, 37, e106. https://doi.org/10.1093/nar/gkp507
  6. F O (2011). Milos P M RNA sequencing: advances, challenges and opportunities. Nat Rev Genet, 12, 87-98. https://doi.org/10.1038/nrg2934
  7. Gan Q, Chepelev I, Wei G, et al (2010). Dynamic regulation of alternative splicing and chromatin structure in Drosophila gonads revealed by RNA-seq. Cell Res, 20, 763-83. https://doi.org/10.1038/cr.2010.64
  8. Geng Chen KY, Shi L, Fang Z, et al (2011). Comparative analysis of human protein-coding and noncoding RNAs between brain and 10 mixed cell lines by RNA-Seq. PLoS One, 21, e28318.
  9. Guo F LY, Liu Y, Wang J, Li Y, Li G (2010). Inhibition of metastasis-associated lung adenocarcinoma transcript 1 in CaSki human cervical cancer cells suppresses cell proliferation and invasion. Acta Biochim Biophys Sin (Shanghai), 42, 224-9. https://doi.org/10.1093/abbs/gmq008
  10. Guttman M, Garber M, Levin JZ, et al (2010). Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol, 28, 503-10. https://doi.org/10.1038/nbt.1633
  11. Hech SS (2003). Tobacco carcinogens, their biomarkers and tobacco-induced cancer. Nat Rev Cancer, 3, 733-44. https://doi.org/10.1038/nrc1190
  12. Huang da W SBT, Lempicki R A (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4, 44-57. https://doi.org/10.1038/nprot.2008.211
  13. Huang da W, Sherman BT, Lempicki RA (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37, 1-13. https://doi.org/10.1093/nar/gkn923
  14. Hui Jiang WHW (2008). SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics, 25, 2395-6.
  15. Jiang H, Wong WH (2009). Statistical inferences for isoform expression in RNA-Seq. Bioinformatics, 25, 1026-32. https://doi.org/10.1093/bioinformatics/btp113
  16. Maher CA, Kumar-Sinha C, Cao X, et al (2009). Transcriptome sequencing to detect gene fusions in cancer. Nature, 458, 97-101. https://doi.org/10.1038/nature07638
  17. Marguerat S, Bahler J (2010). RNA-seq: from technology to biology. Cell Mol Life Sci, 67, 569-79. https://doi.org/10.1007/s00018-009-0180-6
  18. Marioni JC, Mason CE, Mane SM, et al (2008). RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res, 18, 1509-17. https://doi.org/10.1101/gr.079558.108
  19. Mortazavi A, Williams BA, McCue K, et al (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods, 5, 621-8. https://doi.org/10.1038/nmeth.1226
  20. Nagalakshmi U, Waern K, Snyder M (2010). RNA-Seq: a method for comprehensive transcriptome analysis. Curr Protoc Mol Biol, Chapter 4, Unit 4 11 1-3.
  21. O'Donovan N, Fischer A, Abdo E, et al (2002). Differential expression of IgG Fc binding protein (FcgammaBP) in human normal thyroid tissue, thyroid adenomas and thyroid carcinomas. J Endocrinol, 174, 517-24. https://doi.org/10.1677/joe.0.1740517
  22. Pflueger D TS, Sboner A, Habegger L, et al (2011). Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing. Genome Res, 21, 56-67. https://doi.org/10.1101/gr.110684.110
  23. Sanna MT, Giardina B, Scatena R, et al (1994). Functional alterations in adult and fetal hemoglobin Sassari Asp-alpha 126(H9)-->His. The role of alpha 1 alpha 2 contact. J Biol Chem, 269, 18338-42.
  24. Sultan M, Schulz MH, Richard H, et al (2008). A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science, 321, 956-60. https://doi.org/10.1126/science.1160342
  25. Trapnell C, Williams BA, Pertea G, et al (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol, 28, 511-5. https://doi.org/10.1038/nbt.1621
  26. Tseng JJ, HY, Hsu SL, Chou MM (2009). Metastasis associated lung adenocarcinoma transcript 1 is up-regulated in placenta previa increta/percreta and strongly associated with trophoblast-like cell invasion in vitro. Mol Hum Reprod, 15, 725-31. https://doi.org/10.1093/molehr/gap071
  27. Wajcman H, Kister J, Vasseur C, et al (1992). Structure of the EF corner favors deamidation of asparaginyl residues in hemoglobin: the example of Hb La Roche-sur-Yon [beta 81 (EF5) Leu----His]. Biochim Biophys Acta, 1138, 127-32. https://doi.org/10.1016/0925-4439(92)90052-O
  28. Wang Z, Gerstein M, Snyder M (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 10, 57-63. https://doi.org/10.1038/nrg2484
  29. Xiao H, Ding J, Gao S, et al (2011). Never smokers with lung cancer: analysis of genetic variants. Asian Pac J Cancer Prev, 12, 2807-9.
  30. Yu CJ, Yang PC, Shun CT, et al (1996). Overexpression of MUC5 genes is associated with early post-operative metastasis in non-small-cell lung cancer. Int J Cancer, 69, 457-65. https://doi.org/10.1002/(SICI)1097-0215(19961220)69:6<457::AID-IJC7>3.0.CO;2-3

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