文献类型：期刊文献

英文题名：Exploring the key genes of colorectal "adenoma-cancer" based on graph transformer

作者：Yuan, Yue[1];Liu, Yu[1];Fan, Jingchun[2];Ren, Zhen[1]

第一作者：Yuan, Yue

通信作者：Ren, Z[1]

机构：[1]Gansu Univ Chinese Med, Sch Informat Engn, Lanzhou, Peoples R China;[2]Gansu Univ Chinese Med, Ctr Evidence based Med, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Informat Engn, Lanzhou, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：21

期号：10

外文期刊名：PLOS ONE

收录：;Scopus(收录号：2-s2.0-105052531330);WOS:【SCI-EXPANDED(收录号:WOS:001896794900017)】；

基金：The following three fund projects are all intended to support the first author YY. 1.Innovation and Entrepreneurship Fund Project of Gansu University of Chinese Medicine(2026CXZX-942). 2.Innovation Fund Project for College Teachers of Gansu Province(2026B-123). 3.Key Talent Project of Health Care in Gansu Province(2026SWJWRC008). The funder provided support in the form of salaries for authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.

语种：英文

摘要：Colorectal cancer (CRC) is one of the most lethal malignancies worldwide, and the precise identification of biomarkers from colonic adenoma to cancer is of great significance for preventing the development of adenocarcinoma. Given that existing methods inadequately capture the topological network relationships among genes, this study proposes a graph neural network model based on multifeature learning, named ChebTs, to investigate the correlation between key genes involved in the colorectal "adenoma-cancer" transition. The GSE41657 and GSE31905 datasets from the GEO database were stratified into normal, adenoma, and colorectal cancer groups. Feature encoding was introduced to enhance node features, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was constructed using Cytoscape software, and the aggregated information was embedded into the model for training to generate a list of key genes with corresponding importance scores. An attention pooling mechanism aggregated node-level representations into graph level representations for sample classification, and a two layer fully connected network, following activation and regularization, produced predicted probabilities. Furthermore, we provided interpretable analyses at the gene level using GNNExplainer. The results were validated through virtual knockout techniques. The eight screened genes, Fibronectin 1 (FN1), Claudin 2 (CLDN2), Interleukin-33 (IL-33), Matrix Metallopeptidase 1 (MMP1), Stanniocalcin 2 (STC2), Insulin-Like Growth Factor-Binding Protein 7 (IGFBP7), NADPH Oxidase 4 (NOX4), and Secreted Frizzled Related Protein 1 (SFRP1), were all found to be associated with overall survival (OS) in CRC. In this study, eight molecules closely related to the development of colorectal adenocarcinoma were screened out, and they may be diagnostic biomarkers of colorectal cancer. These genes affect the prognosis of patients by participating in biological processes such as remodeling of extracellular mechanisms, and are of great significance for preventing the carcinogenesis of adenoma.