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. 2021 Dec 15;11:24022. doi: 10.1038/s41598-021-03432-3

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© The Author(s) 2021

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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Assessment of the risk models in predicting prognosis of HCC. (A) The survival of samples in TCGA-LIHC was analyzed by six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2). (B) The Kaplan–Meier curve of sample survival in TCGA-LIHC was predicted by a risk model consisting of CA9, CXCL5, MMP12, SLC1A5 and G6PD. (C) The Kaplan–Meier curve of sample survival in TCGA-LIHC was predicted by a risk model composed of SPINK1, TXNRD1, LCAT and PZP. (D) C index of our risk model and the other three risk models. (E) ROC curve of six-gene signature for predicting HCC prognosis. (F) ROC curve for predicting HCC prognosis by risk model composed of CA9, CXCL5, MMP12, SLC1A5 and G6PD. (G) ROC curve of the model consisting of SPINK1, TXNRD1, LCAT and PZP to predict HCC survival.