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. 2020 Nov 19;20:555. doi: 10.1186/s12935-020-01635-8

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© The Author(s) 2020

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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Fig. 1 — Potential predictors selection using Lasso regression analysis. a The changing trajectory of each predictor. The horizontal axis represents the log value of the each predictor λ, and the vertical axis represents the coefficient of the independent predictor; b Tuning the penalty parameter in Lasso regression analysis using tenfold cross validation and 1 standard error of the minimum criteria; c Histogram shows the role of each predictor that contribute to the developed prognostic model. The predictors that contribute to the prognostic model are plotted on the x-axis, with their coefficients in the Lasso regression analysis plotted on the y-axis