RETRACTED ARTICLE: Dysregulation of cell cycle related genes and microRNAs distinguish the low- from high-risk of prostate cancer

详细信息    查看全文

• 作者：Jiling Wen ; Rongbing Li ; Xiaofei Wen ; Guangming Chou ; Jiasun Lu…
• 关键词：Prostate cancer ; Differentially expressed genes ; MicroRNAs
• 刊名：Diagnostic Pathology
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：9
• 期：1
• 全文大小：765 KB
• 参考文献：1. Chan, JM, Jou, RM, Carroll, PR (2004) The relative impact and future burden of prostate cancer in the United States. J Urol 172: pp. S13-S16 CrossRef
  2. Siegel, R, Naishadham, D, Jemal, A (2012) Cancer statistics, 2012. CA Cancer J Clin 62: pp. 10-29 CrossRef
  3. Shen, MM, Abate-Shen, C (2010) Molecular genetics of prostate cancer: new prospects for old challenges. Genes Dev 24: pp. 1967-2000 CrossRef
  4. Berezikov, E, Guryev, V, Van De Belt, J, Wienholds, E, Plasterk, RH, Cuppen, E (2005) Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120: pp. 21-24 CrossRef
  5. Logothetis, CJ, Lin, SH (2005) Osteoblasts in prostate cancer metastasis to bone. Nat Rev Cancer 5: pp. 21-28 CrossRef
  6. Nogueira, L, Corradi, R, Eastham, JA (2010) Other biomarkers for detecting prostate cancer. BJU Int 105: pp. 166-169 CrossRef
  7. Hoque, MO, Kim, MS, Ostrow, KL, Liu, J, Wisman, GB, Park, HL, Poeta, ML, Jeronimo, C, Henrique, R, Lendvai, A, Schuuring, E, Begum, S, Rosenbaum, E, Ongenaert, M, Yamashita, K, Califano, J, Westra, W, Van Der Zee, AG, Van Criekinge, W, Sidransky, D (2008) Genome-wide promoter analysis uncovers portions of the cancer methylome. Cancer Res 68: pp. 2661-2670 CrossRef
  8. Schilling, D, Reijke, T, Tombal, B, Taille, A, Hennenlotter, J, Stenzl, A (2010) The Prostate Cancer gene 3 assay: indications for use in clinical practice. BJU Int 105: pp. 452-455 CrossRef
  9. Hoque, MO (2009) DNA methylation changes in prostate cancer: current developments and future clinical implementation. Expert Rev Mol Diagn 9: pp. 243-257 CrossRef
  10. Taylor, BS, Schultz, N, Hieronymus, H, Gopalan, A, Xiao, Y, Carver, BS, Arora, VK, Kaushik, P, Cerami, E, Reva, B, Antipin, Y, Mitsiades, N, Landers, T, Dolgalev, I, Major, JE, Wilson, M, Socci, ND, Lash, AE, Heguy, A, Eastham, JA, Scher, HI, Reuter, VE, Scardino, PT, Sander, C, Sawyers, CL, Gerald, WL (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18: pp. 11-22 CrossRef
  11. Storey, JD, Tibshirani, R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: pp. 9440-9445 CrossRef
  12. Friedman, RC, Farh, KK, Burge, CB, Bartel, DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19: pp. 92-105 CrossRef
  13. Hsu, SD, Lin, FM, Wu, WY, Liang, C, Huang, WC, Chan, WL, Tsai, WT, Chen, GZ, Lee, CJ, Chiu, CM, Chien, CH, Wu, MC, Huang, CY, Tsou, AP, Huang, HD (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39: pp. D163-D169 CrossRef
  14. Daya, S (2002) Fisher exact test. Evid Based Obstet Gynecol 4: pp. 3-4 CrossRef
  15. Das, J, Yu, H (2012) HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 6: pp. 92 CrossRef
  16. Xu, K, Wu, ZJ, Groner, AC, He, HH, Cai, C, Lis, RT, Wu, X, Stack, EC, Loda, M, Liu, T, Xu, H, Cato, L, Thornton, JE, Gregory, RI, Morrissey, C, Vessella, RL, Montironi, R, Magi-Galluzzi, C, Kantoff, PW, Balk, SP, Liu, XS, Brown, M (2012) EZH2 oncogenic activity in castration-resistant prostate cancer cells
• 刊物主题：Pathology;
• 出版者：BioMed Central
• ISSN：1746-1596

文摘

Background Prostate cancer (PCa) is a biologically heterogeneous disease with considerable variation in clinical aggressiveness. In this study, bioinformatics was used to detect the patterns of gene expression alterations of PCa patients. Methods The gene expression profile GSE21034 and GSE21036 were downloaded from Gene Expression Omnibus (GEO) database. Significantly changed mRNA transcripts and microRNAs were identified between subtypes with favorable (cluster 2) and unfavorable (cluster 5) prognosis by two-side unequal variances t test. MicroRNAs and their potential target genes were identified by TargetScan and miRTarBase, respectively. Besides, the overlapped genes between the target genes of microRNAs and mRNA transcripts were assessed by Fisher-exact test (one side). The functional annotation was performed by DAVID, followed by construction of protein-protein interaction (PPI) network. Results Compared to cluster 2, 1556 up-regulated and 1288 down-regulated transcripts were identified in cluster 5. Total 28 microRNAs were up-regulated and 30 microRNAs were down-regulated in cluster 5. Besides, 12 microRNAs target transcripts were significantly overlapped with down-regulated transcripts in cluster 5 with none of them was found overlapped with up-regulated transcripts. Functional annotation showed that cell cycle was the most significant function. In the PPI network, BRCA1, CDK1, TK1 and TRAF2 were hub protein of signature genes in cluster 5, and TGFBR1, SMAD2 and SMAD4 were hub proteins of signature gnens in cluster 2. Conclusions Our findings raise the possibility that genes related with cell cycle and dysregulated miRNA at diagnosis might have clinical utility in distinguishing low- from high-risk PCa patients. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_156