Comment on “Nuclear localization of LDL receptor-related protein 1B in mammary gland carcinogenesis”
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To the editor:
We read with great interest the article by Asano and colleagues . The study illuminated that the nuclear-localized LRP1B intracellular domain promoted breast cancer progression with poor prognosis, which possibly through the NEAT1 pathway. The results are very helpful to find new therapeutic target for breast cancer patients. On the other hand, from our perspective, the bioinformatics analyses need further context as the statistics for differential fold changes in expression data are not explained fully. For example, the authors seem to use unadjusted p values for detecting differentially expressed genes (DEGs) regulated genes by nuclear-localized LRP1B intracellular domain. Due to the high false positives caused by a large number of probes and multiple comparisons, it seems essential to analyze microarray data properly to reach a reliable result by a statistical method. Only selecting DEGs with unadjusted p values < 0.05 in expression is not reliable and suitable for high-level microarray analysis.
Moreover, we would like to suggest using specialized high-level microarray analysis such as LIMMA (linear models for microarray analysis) , commonly used for statistical testing and analysis of differential expression data by using linear models, and choosing more than 1.5-fold expression changes and false discovery rate (FDR) < 0.05 as the cutoff is an appropriate and conservative approach to obtain DEGs . Moreover, modified significant analysis of microarray  is another and considerable non-parametric statistical algorithm; a 2-fold expression change and FDR < 0.1 is a rational cutoff to obtain DEGs.
In addition, choosing the optimal statistical approach  and obtaining accurate and convincing results of DEGs analysis are basis for further data analysis such as gene network analysis. We welcome the authors to offer further explanation of their data analysis and experimental approach. We suggest transcriptomics data-intensive research would benefit from these considerations and innovations in statistical and data analytical approaches.
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