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. 2020 Mar 11;10:4526. doi: 10.1038/s41598-020-61273-y

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© The Author(s) 2020

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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Comprehensive profiling of Taiwanese CRC transcriptomes reveals cohort-specific alterations. Hierarchical clustering of (A) DEGs and (B) DEMs from Taiwanese CRC patients demonstrating a disturbed expression profile between normal (blue) and tumor (red) tissues. GSEA charts of top ten significantly enriched (P value < 0.05) upregulated (C, NES > 0) and downregulated (D, NES < 0) KEGG pathways, where each row represents indicated NES value and the color shows the significance of enrichment result in -log₁₀ transformed P value. Detailed enrichment results are listed in the Table S3, Additional file 2. Gene set over-representation analysis of top ten (E) upregulated and (F) downregulated DEMs targeting mRNAs enriched pathways. Construction of miRNA–mRNA correlation network based on (G) upregulated DEMs/downregulated DEGs and (H) downregulated DEMs/upregulated DEGs, in which DEGs are targeted by more than two miRNAs. miRNAs, squares; mRNA, circles; upregulated, red; downregulated, green. (I) Bar plot of betweenness scores for top ten nodes from networks (G) and (H).