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. 2023 Mar 17;21:203. doi: 10.1186/s12967-023-04038-1

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© The Author(s) 2023

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 — Identification of FCN1 as a candidate biomarker for PIBD diagnosis. A Overall workflow for construction and external validation of the diagnostic model. B Volcano plot of differentially expressed genes (DEGs) between PIBD and non-IBD rectal biopsy samples of GSE117993. Genes with adjusted P < 0.05 and |log2(Fold Change)|> 2 are considered as DEGs, and these DEGs are listed in Table S1. C LASSO logistic regression algorithm for biomarker selection. D mSVM-RFE algorithm for biomarker selection. E Venn diagram visualizing the overlap of selected markers between two algorithms. Receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) of diagnostic prediction models based on FCN1 and LINC01558 in the colon F and blood G validation datasets (GSE126124)