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. 2023 Sep 4;24:523. doi: 10.1186/s12864-023-09583-z

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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. 5 — Analysis between UBRs and cancer hallmark-related pathways. (A) Correlation network between UBRs and pathways. The red line means a positive correlation and the blue line means a negative correlation. (B) Quantitative distribution of UBRs that were significantly correlated with specific pathways. (C) Quantitative distribution of pathways that were significantly correlated with specific UBRs. (D) Correlation between pathways or partial UBRs. Bottom left, correlation between pathways; top right, correlation between UBRs. (E) Correlation between USP49 and KCTD7. (F) Correlation between BRCA1 and BARD1. (G) Correlation between E2F targets pathway and G2M checkpoint pathway. (H) Subnetworks identified from the 79 UBRs’ PPI networks. Blue represents the subnetwork 1