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. 2022 Jun 23;22:692. doi: 10.1186/s12885-022-09736-5

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© The Author(s) 2022

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 — The workflow in the study. GSE13911, GSE29272, GSE79973, and GSE118916 denote four expression profiling datasets from the GEO database. ACGR: Asian Cancer Research Group, CAF: cancer-associated fibroblast, DAVID: The Database for Annotation, Visualization, and Integrated Discovery, DEG: differentially expressed genes, GC: Gastric cancer, GEPIA2: Gene Expression Profiling Interactive Analysis 2, GTEx: Genotype-Tissue Expression Project, KEGG: Kyoto Encyclopedia of Genes and Genomes, PPI: protein-protein interaction, STRING: The Search Tool for the Retrieval of Interacting Genes/Proteins, TCGA: The Cancer Genome Atlas, TIMER2.0: Tumor Immune Estimation Resource 2.0, UALCAN: University of Alabama Cancer Database