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. 2024 Oct 30;5(11):1697–1712. doi: 10.1038/s43018-024-00823-z

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© The Author(s) 2024

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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Extended Data Fig. 7 — (a) Distributions of ranks for P-E connectivity and corresponding gene expression levels of key oncogenes and proliferation markers across various cancer progression stages. (b) Model fit assessment for the predicted changes in gene expression in adenocarcinoma using the ‘initial’ model, which utilizes the baseline P-E connectivity. (c) Model fit assessment for the predicted changes in gene expression in adenocarcinoma using the ‘differential’ model, which considers changes in epigenetic landscapes. (d) Spearman correlation matrix showing the similarity of each feature between different stages. (e) Mean squared error (MSE) and (f) Pearson’s r coefficient of the ‘initial’ model for the prediction of gene expression changes in adenocarcinoma compared to the indicated baseline stages. Prediction scores obtained by models trained with mucosa and polyp datasets were compared by using independent t test (N = 10 random initiation states). For mean square error (MSE), p = 2.10E-5, p = 3.21E-11, and p = 0.33; for Pearson’s r, p = 1.39E-5, p = 4.08E-13, and p = 2.78E-3, for mucosa-polyp, mucosa-AdeCa, and polyp-AdeCa prediction models, respectively. (g) Distributions of minimal mean square error (MSE) of ‘Initial’ model trained with equal or less than 20 epochs (N = 10 random initiation states) with the removal of indicated features. Significance p values of differential MSE caused by missing features compared to complete model (All) are evaluated by using independent t-test. Respectively, p = 3.69E-6, p = 0.57, p = 0.41, p = 0.01, and p = 0.03 for models removing P-E connectivity, P stripe strength, P accessibility, E accessibility, and P methylation feature. (h) The top 20 influential features impacting gene expression predictions in adenocarcinoma, as determined by SHAP (SHapley Additive exPlanations) analysis for the ‘initial’ model. (i) The top 20 influential features for the ‘differential’ polyp model, with features named after transcription factors indicating their binding presence at the promoter (p) or enhancer (e) regions, based on the ENCODE database.

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