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. 2019 Sep 13;9:13216. doi: 10.1038/s41598-019-49271-1

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Principal component analysis (PCA-plot) of differentially expressed genes (DEGs). PCA-plot displays summarised data of 3,587 DEGs in 18 prostate tissues (triangle) and seven US-FNA (circle) samples. In general, variances between samples on the x-axis (principal component 1, PC1) separated the data the most (61.1%) and are associated with diagnosis: non-malignant (green) and malignant (blue) samples. Variances on y-axis were lower with 11.3% (principal component 2, PC2) and related to different sampling techniques.