Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
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Transcriptome Network Analysis Reveals Potential Candidate Genes for Esophageal Squamous Cell Carcinoma

  • Ma, Zheng (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Guo, Wei (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Niu, Hui-Jun (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Yang, Fan (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Wang, Ru-Wen (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Jiang, Yao-Guang (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Zhao, Yun-Ping (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University)
  • Published : 2012.03.31
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Abstract

The esophageal squamous cell carcinoma (ESCC) is an aggressive tumor with a poor prognosis. Understanding molecular changes in ESCC should improve identification of risk factors with different molecular subtypes and provide potential targets for early detection and therapy. Our study aimed to obtain a molecular signature of ESCC through the regulation network based on differentially expressed genes (DEGs). We used the GSE23400 series to identify potential genes related to ESCC. Based on bioinformatics we constructed a regulation network. From the results, we could establish that many transcription factors and pathways closely related with ESCC were linked by our method. STAT1 also arose as a hub node in our transcriptome network, along with some transcription factors like CCNB1, TAP1, RARG and IFITM1 proven to be related with ESCC by previous studies. In conclusion, our regulation network provided information on important genes which might be useful in investigating the complex interacting mechanisms underlying the disease.

Keywords

References

  1. Ayshamgul H, Ma H, Ilyar S, et al (2011). Association of defective HLA-I expression with antigen processing machinery and their association with clinicopathological characteristics in Kazak patients with esophageal cancer. Chin Med J (Engl), 124, 341-6.
  2. Brivanlou AH, Darnell JE, Jr (2002). Signal transduction and the control of gene expression. Science, 295, 813-8. https://doi.org/10.1126/science.1066355
  3. Chattopadhyay I, Phukan R, Singh A, et al (2009). Molecular profiling to identify molecular mechanism in esophageal cancer with familial clustering. Oncol Rep, 21, 1135-46.
  4. Draghici S, Khatri P, Martins RP, et al (2003). Global functional profiling of gene expression. Genomics, 81, 98-104. https://doi.org/10.1016/S0888-7543(02)00021-6
  5. Draghici S, Khatri P, Tarca AL, et al (2007). A systems biology approach for pathway level analysis. Genome Res, 17, 1537-45. https://doi.org/10.1101/gr.6202607
  6. Fumoto S, Shimokuni T, Tanimoto K, et al (2008). Selection of a novel drug-response predictor in esophageal cancer: a novel screening method using microarray and identification of IFITM1 as a potent marker gene of CDDP response. Int J Oncol, 32, 413-23.
  7. Guo QM (2003). DNA microarray and cancer. Curr Opin Oncol, 15, 36-43. https://doi.org/10.1097/00001622-200301000-00005
  8. Holmes RS, Vaughan TL (2007). Epidemiology and pathogenesis of esophageal cancer. Semin Radiat Oncol, 17, 2-9. https://doi.org/10.1016/j.semradonc.2006.09.003
  9. Hourihan RN, O'Sullivan GC, Morgan JG (2003). Transcriptional gene expression profiles of oesophageal adenocarcinoma and normal oesophageal tissues. Anticancer Res, 23, 161-5.
  10. Ichiba M, Miyazaki Y, Kitamura S, et al (2002). Epidermal growth factor inhibits the growth of TE8 esophageal cancer cells through the activation of STAT1. J Gastroenterol, 37, 497-503. https://doi.org/10.1007/s005350200077
  11. Jiang C, Xuan Z, Zhao F, Zhang MQ (2007). TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res, 35, D137-40. https://doi.org/10.1093/nar/gkl1041
  12. Jiang Q, Wang Y, Hao Y, et al (2009). miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Re, 37, D98-104. https://doi.org/10.1093/nar/gkn714
  13. Kanehisa M (2002). The KEGG database. Novartis Found Symp, 247, 91-101. https://doi.org/10.1002/0470857897.ch8
  14. Kihara C, Tsunoda T, Tanaka T, et al (2001). Prediction of sensitivity of esophageal tumors to adjuvant chemotherapy by cDNA microarray analysis of gene-expression profiles. Cancer Res, 61, 6474-9.
  15. Kuwano H, Kato H, Miyazaki T, et al (2005). Genetic alterations in esophageal cancer. Surgery today, 35, 7-18. https://doi.org/10.1007/s00595-004-2885-3
  16. Lin J, Lin L, Thomas DG, et al (2005). Melanoma-associated antigens in esophageal adenocarcinoma: identification of novel MAGE-A10 splice variants. Clin Cancer Res, 10, 5708-16.
  17. Lin PW, Lee RC, Chern MS, et al (2006). Primary malignant melanoma of the esophagus. J Chinese Med Association, 69, 334-7. https://doi.org/10.1016/S1726-4901(09)70269-2
  18. Lu M, Zhang Q, Deng M, et al (2008). An analysis of human microRNA and disease associations. PLoS One, 3, e3420. https://doi.org/10.1371/journal.pone.0003420
  19. Lu XF, Li EM, Du ZP, et al (2010). Specificity protein 1 regulates fascin expression in esophageal squamous cell carcinoma as the result of the epidermal growth factor/extracellular signal-regulated kinase signaling pathway activation. Cell Mol Life Sci, 67, 3313-29. https://doi.org/10.1007/s00018-010-0382-y
  20. Maere S, Heymans K, Kuiper M (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics, 21, 3448-9. https://doi.org/10.1093/bioinformatics/bti551
  21. Matsumura Y, Yashiro M, Ohira M, et al (2005). 5-Fluorouracil up-regulates interferon pathway gene expression in esophageal cancer cells. Anticancer Res, 25, 3271-8.
  22. McCabe M, Dlamini Z (2005). The molecular mechanisms of oesophageal cancer. Int Immunopharmacol, 5, 1113-30. https://doi.org/10.1016/j.intimp.2004.11.017
  23. Morgan G (1996). Relationship between colorectal and esophageal cancer. Dis Colon Rectum, 39, 237. https://doi.org/10.1007/BF02068083
  24. Nakamura T, Mohri H, Shimazaki M, et al (1997). Esophageal metastasis from prostate cancer: diagnostic use of reverse transcriptase-polymerase chain reaction for prostate-specific antigen. J Gastroenterol, 32, 236-40. https://doi.org/10.1007/BF02936374
  25. Nozoe T, Korenaga D, Kabashima A, et al (2002). Significance of cyclin B1 expression as an independent prognostic indicator of patients with squamous cell carcinoma of the esophagus. Clin Cancer Res, 8, 817-22.
  26. Papadopoulos GL, Reczko M, Simossis VA, et al (2009). The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res, 37, D155-8. https://doi.org/10.1093/nar/gkn809
  27. Papineni S, Chintharlapalli S, Abdelrahim M, et al (2009). Tolfenamic acid inhibits esophageal cancer through repression of specificity proteins and c-Met. Carcinogenesis, 30, 1193-201. https://doi.org/10.1093/carcin/bgp092
  28. Paweletz CP, Ornstein DK, Roth MJ, et al (2000). Loss of annexin 1 correlates with early onset of tumorigenesis in esophageal and prostate carcinoma. Cancer Res, 60, 6293.
  29. Shannon P, Markiel A, Ozier O, et al (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498-504. https://doi.org/10.1101/gr.1239303
  30. Smyth GK (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 3, Article 3.
  31. Song Y, Zhao C, Dong L, et al (2008). Overexpression of cyclin B1 in human esophageal squamous cell carcinoma cells induces tumor cell invasive growth and metastasis. Carcinogenesis, 29, 307-15. https://doi.org/10.1093/carcin/bgm269
  32. Spies M, Dasu MR, Svrakic N, et al (2002). Gene expression analysis in burn wounds of rats. Am J Physiol Regul Integr Comp Physiol, 283, R918-30. https://doi.org/10.1152/ajpregu.00170.2002
  33. Su H, Hu N, Yang HH, et al (2011). Global gene expression profiling and validation in esophageal squamous cell carcinoma and its association with clinical phenotypes. Clin Cancer Res, 17, 2955-66. https://doi.org/10.1158/1078-0432.CCR-10-2724
  34. Tavazoie S, Hughes JD, Campbell MJ, et al (1999). Systematic determination of genetic network architecture. Nat Genet, 22, 281-5. https://doi.org/10.1038/10343
  35. Vaughan TL, Kiemeney L, McKnight B (1995). Colorectal cancer in patients with esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev, 4, 93.
  36. Wachi S, Yoneda K, Wu R (2005). Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics, 21, 4205-8. https://doi.org/10.1093/bioinformatics/bti688
  37. Watanabe G, Kaganoi J, Imamura M, et al (2001). Progression of esophageal carcinoma by loss of EGF-STAT1 pathway. Cancer J, 7, 132-9.
  38. Wingender E (2008). The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform, 9, 326-32. https://doi.org/10.1093/bib/bbn016
  39. Xiao F, Zuo Z, Cai G, et al (2009). miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res, 37, D105-10. https://doi.org/10.1093/nar/gkn851
  40. Yang JH, Li JH, Shao P, et al (2011). starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res, 39, D202-9. https://doi.org/10.1093/nar/gkq1056

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