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. 2022 Mar 7;15:50. doi: 10.1186/s12920-022-01196-x

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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. 2 — Time-dependent receiver operating characteristic curves based on the PRGs prognostic signature (PPS). In terms of predicting the 1-year survival probability, the risk score of the prognostic signature is more accurate than any single gene in the signature or clinical parameters. a The capability of PPS and PRGs in predicting prognosis of EAC patients in the training cohort. b The capability of PPS and PRGs in predicting the prognosis of EAC patients in the validation cohort. c The capability of PPS and PRGs in predicting the prognosis of EAC patients in the test cohort. d The capability of PPS and clinical parameters in predicting the prognosis of the EAC patients in the entire TCGA cohort