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. 2022 Oct 19;23:437. doi: 10.1186/s12859-022-04984-5

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© 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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Fig. 3 — Summary of the 10 DEMGs and the selection of a 4 m6A-related gene signature in predicting LC as well as effect on LC prognosis and clinicalpathological characteristics of the 4 m6A-related gene signature. A Risk score and survival status for each patient in LC of TCGA datasets. B Kaplan–Meier OS curves for patients in the TCGA datasets designated to high- and low-risk groups depended on the risk score. C ROC curves demonstrated the predictive efficiency of the risk signature in LC of TCGA datasets. D Survival curve of CBLL1 between high- and low-expression levels in LC of TCGA datasets. E Nomogram for forecasting prognostic risk of LC patients. F, G Univariate and Multivariate analysis of the hazard ratios for risk score as independent prognostic elements to anticipate the overall survival