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. 2020 Jul 2;10:10937. doi: 10.1038/s41598-020-67753-5

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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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Logistic regression model for prediction of Glioblastoma using QNPbyTAP genes that are atleast 20-fold-upregulated in QNP relative to TAP and are significantly enriched in in Glioblastoma HuAgeGBsplit_18 WGCNA module. Effects plot showing probability of Glioblastoma (DiseaseGrade_4) with change in expression of CD151 (A), CEND1 (B), DCHS1 (C), SMPD1 (D), TPP1 (E), GATD1 (F), RNH1 (G) and SMCR8 (H). Chi-square test and Hosmer and Lemeshow goodness of fit (GOF) test for logistic regression model without gene–gene interaction (I) and with gene–gene interaction (J).