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. 2020 Sep 23;10:15495. doi: 10.1038/s41598-020-72488-4

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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 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/.

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Construction of the prognosis prediction model for glioblastoma (GBM) patients by least absolute shrinkage and selection operator (LASSO) analysis. (A) The relationships between the p values of 92 genes and the hazard ratio (HR). (B) The relationships between the p values of 92 genes and the expression levels. Red dots represent significantly different immune-related genes regarding prognosis. (C) The changing trajectory of each independent variable. The horizontal axis represents the log value of the independent variable lambda, and the vertical axis represents the coefficient of the independent variable. With the increase in lambda, the number of independent variable coefficients tending to 0 also increases. (D) Confidence intervals for each lambda. The optimal model is acquired when the lambda is 0.04456.