Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Department of Surgery, St. Olav's Hospital, Trondheim, Norway.
NPJ Syst Biol Appl. 2024 Nov 4;10(1):127. doi: 10.1038/s41540-024-00455-0.
Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44.
与胃肠上皮化生（GIM）向胃腺癌（GA）进展相关的生物标志物，即与 GA 相关的 GIM，可提供有价值的信息，用于识别 GA 风险增加的患者。本研究旨在利用多组学分析揭示与 GA 相关的 GIM 的潜在生物标志物，并预测潜在的药物重用来预防患者的 GA。多组学分析包括通过微阵列基因表达（MGE）获得的基因表达矩阵（GEM）、ScType（一种完全自动化且超快的细胞类型识别方法，仅基于给定的 scRNA-seq 数据）、Ingenuity 通路分析、PageRank 中心性、GO 和 MSigDB 富集分析、Cytoscape、人类蛋白质图谱和分子对接分析，结合免疫组织化学。为了鉴定与 GA 相关的 GIM，我们从 16 名接受胃切除术的 GIM-GA 患者中收集了配对的手术活检组织，共获得了 64 个样本（每个胃 4 个活检 x 16 名患者）用于 MGE。通过纳入 37 名患者内镜活检的 scRNAseq 和免疫组织化学数据集进行了联合分析。本研究的结果显示了与 GA 相关的 GIM 的潜在生物标志物，包括个体患者的 GEM、个体基因（如 RBP2 和 CD44）、信号通路、分子网络以及具有关键拓扑节点的信号通路网络。因此，确定了具有重新利用药物潜力的治疗靶点，包括表皮生长因子受体、原癌基因酪氨酸蛋白激酶Src、黏着斑蛋白、转录因子 Jun、乳腺癌 1 型易感性蛋白、肿瘤抗原 p53、鼠双微体 2 和 CD44。
