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. 2019 Oct 8;6:194. doi: 10.1038/s41597-019-0207-2

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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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Fig. 1 — MMD: development, validation, and potential applications in oncology. Microarray-based datasets containing raw transcriptome profiles of patient-derived tumor tissues (TT) and non-tumor (NT) tissues were processed, merged, and batch-effect corrected using an integrated R pipeline. Validation of each cancer type-specific MMD was performed using PCA and RRHO algorithms. Clinical models trained using MMD can be applied to TCGA, facilitating the discovery of new biomarkers, development of prognostic models, and parallel cross-platform analyses with TCGA.