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Construction and validation of prognostic risk score model based on the DE-MRGs

JS Jun Su
YL Yue Li
QL Qing Liu
GP Gang Peng
CQ Chaoying Qin
YL Yang Li
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Based on the TCGA cohort, the Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used for further dimensionality reduction of prognostic DE-MRGs. Finally, 12 DE-MRGs were selected to the construct a prognostic risk model. The formula for risk score was as follows: Risk score = 0.2132 * expression level of PLAUR + 0.0261 * expression level of RBP1 + 0.0048 * expression level of ABCB8 + 0.2553 * expression level of TOMM7 − 0.1868 * expression level of MFF + 0.0714 * expression level of SSBP1 + 0.1927 * expression level of MRPL36 + 0.1686 * expression level of AGK + 0.07 * expression level of HK1—0.1339 * expression level of APEX1 + 0.2896 * expression level of NUDT1 − 0.3503 * expression level of PHB2. The Kaplan–Meier (K-M) analysis, univariate cox and multivariate cox analysis, and time dependent Receiver Operation Characteristic (ROC) curve were used to reveal the prognostic value of our model. The GSE16011 and GSE147352 GBM cohorts were used to validate this prognostic model.

Copyright and License information: The Author(s) ©2022
Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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