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. 2023 Feb 11;14:778. doi: 10.1038/s41467-023-36462-8

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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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Schematic overview of the experimental design and data acquisition process for proteomics, phosphoproteomics, WES, and RNA-seq. NAT indicates normal adjacent tissue. b The genomic profiles of AEG patients. The top panel shows the tumor mutation burden (TMB) in each patient. The top bars show the clinicopathological features of AEG patients. The middle panel is the oncoplot generated with maftools depicting the top 30 mutated cancer-related genes in the present AEG cohort. The bottom panel shows the proportion of different types of nucleotide substitutions in each patient. The right panel represents mutation types and frequencies for each gene. P, two-sided Wilcoxon’s rank test for age, sex, smoking, and alcohol, and Kruskal–Wallis rank sum test for Siewert type and tumor stages. c Overview of the proteomics profile in 103 AEG patients. d Overview of the phosphoproteomics profile of 206 samples from 103 AEG patients. e Overview of the RNA-seq profile of 83 tumors and paired NAT samples.