Literature DB >> 29318799

The Author's Response: Bioinformatics Analysis in Downstream Genes of the mTOR Pathway to Predict Recurrence and Progression of Bladder Cancer.

Chang Hyuk Yoo1.   

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Year:  2018        PMID: 29318799      PMCID: PMC5760817          DOI: 10.3346/jkms.2018.33.e32

Source DB:  PubMed          Journal:  J Korean Med Sci        ISSN: 1011-8934            Impact factor:   2.153


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Dear editor, Thank you for your interest to our recent report. Also, I really appreciate you and your reasonable comments for our paper. Due to the high false positive caused by the characteristics of microarray, it is right that gene selection is performed by rational statistical method, based on analysis of variance model and so on. However, in practice, changes in expression of at least 2-fold have been considered significant, although only rarely has any statistical or biological justification been offered for the selection of this threshold.12 Of course, the data must be normalized to eliminate systematic variations, which are not related to changes in relative RNA abundance for individual genes. I agree with you that choosing the proper statistical method and obtaining more accurate and convincing results of differentially expressed genes (DEGs) analysis is the basis for further analysis. As you know, over the past years, numerous tools have emerged for microarray data analysis. Nonetheless, depending on what statistical method researcher use, the results of microarray data are slightly different.34 Although we did not use the proper statistical method, we selected highly expression level-changes genes of at least 2-fold and confirmed the expression level of genes considered as key genes using reverse transcription-polymerase chain reaction or western blot.5 I think our results show the unprecedented insight of gene regulation in recurrence and progression of bladder cancer.
  5 in total

Review 1.  High-density microarrays for gene expression analysis.

Authors:  M K Deyholos; D W Galbraith
Journal:  Cytometry       Date:  2001-04-01

Review 2.  Bioinformatics analysis of microarray data.

Authors:  Yunyu Zhang; Joseph Szustakowski; Martina Schinke
Journal:  Methods Mol Biol       Date:  2009

3.  Use of a cDNA microarray to analyse gene expression patterns in human cancer.

Authors:  J DeRisi; L Penland; P O Brown; M L Bittner; P S Meltzer; M Ray; Y Chen; Y A Su; J M Trent
Journal:  Nat Genet       Date:  1996-12       Impact factor: 38.330

4.  Comparison of High-Level Microarray Analysis Methods in the Context of Result Consistency.

Authors:  Kornel Chrominski; Magdalena Tkacz
Journal:  PLoS One       Date:  2015-06-09       Impact factor: 3.240

5.  Quantitative flux analysis reveals folate-dependent NADPH production.

Authors:  Jing Fan; Jiangbin Ye; Jurre J Kamphorst; Tomer Shlomi; Craig B Thompson; Joshua D Rabinowitz
Journal:  Nature       Date:  2014-05-04       Impact factor: 49.962

  5 in total

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