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. 2021 Feb 9;11:3365. doi: 10.1038/s41598-021-82635-0

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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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Cumulative ROC curve for the risk prediction models. (a) ROC curve for all the cases and controls for the model formed with the proteins AHSG, KRT6C and AZGP1 (b) ROC curve for the cases of late stage of primary tumour (T3/T4) and controls for the model formed with the proteins AHSG, KRT6C and AZGP1 (c) ROC curve for the cases with N0 stage and controls for the model formed with the proteins AHSG, KRT6C, AZGP1 and BPIFB2 (d) ROC curve for the cases with N+ stage and controls for the model formed with the proteins KRT6C and AZGP1. The characteristics of the ROC curve are mentioned in the inset of each figure. AUC area under the curve, SEN sensitivity, SPE specificity, PPV positive predictive value and NPV negative predictive value.