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. 2023 Jul 1;25:112. doi: 10.1186/s13075-023-03094-8

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© The Author(s) 2023

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/. 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 — Bioinformatics analysis to identify key biomarkers in LN. A Volcano plots of the differentially expressed genes (DEGs). Red: genes upregulated in LN; green: genes downregulated in LN. B GO functional enrichment for the DEGs. C KEGG pathway enrichment for the DEGs. D Candidate diagnostic marker genes identified using LASSO logistic regression. E Candidate diagnostic marker genes identified using SVM-RFE. F Candidate diagnostic marker genes identified using RF algorithm. G Venn diagram of candidate diagnostic genes identified using three algorithms. H The difference in expression of IFI16 in training set. I ROC curve for IFI16 using the training set. J The difference in expression of IFI16 in validation set. K ROC curve for IFI16 using validation set