背景：小肠神经内分泌肿瘤（SI-NETs）是小肠最常见的恶性肿瘤，许多患者出现转移，其发病率逐年增加。我们旨在寻找有效的诊断生物标志物，用于诊断原发性和转移性SI-NETs患者。方法：我们从GEO数据库下载GSE65286（训练集）和GSE98894（测试集），进行差异基因表达分析，获得差异表达基因（DEGs）和差异表达长非编码RNA（DElncRNAs）。进一步通过基因本体论（GO）和基因组学百科全书（KEGG）富集分析探索了这些基因参与的功能和途径。此外，基于RNAInter和TRRUST v2数据库构建涉及SI-NETs异常基因的全局调控网络，并通过受试者工作特征曲线（ROC）识别出中心基因的诊断效能。结果：在训练集中获得了2,969个DEGs和DElncRNAs。富集分析显示，生物学过程（BPs）和KEGG途径主要与癌症相关。通过基因集富集分析（GSEA），我们获得了五个BP（细胞分裂、铁离子平衡、粘多糖代谢过程、血小板脱颗粒和三酰甘油代谢过程）和一个KEGG途径（ppar信号通路）。此外，得到的异常基因核心集包括MYL9、ITGV8、FGF2、FZD7和FLNC。与训练集一致，中心基因在原发性SI-NETs患者中上调，而转移性SI-NETs患者中则下调。显著的是，ROC分析结果显示，中心基因在训练集和测试集中的诊断能力强。结论：总之，我们构建了SI-NETs的全局调控网络。此外，我们获得了MYL9、ITGV8、FGF2、FZD7和FLNC等中心基因，这些中心基因可能对原发性和转移性SI-NET患者的诊断有用。版权所有©2023陈菱、孟庆华、黄强、廖海霞、唐璐璐、徐俊宽和李梅。

Background: Small intestinal neuroendocrine tumors (SI-NETs) are the most common malignant tumors of the small intestine, with many patients presenting with metastases and their incidence increasing. We aimed to find effective diagnostic biomarkers for patients with primary and metastatic SI-NETs that could be applied for clinical diagnosis. Methods: We downloaded GSE65286 (training set) and GSE98894 (test set) from the GEO database and performed differential gene expression analysis to obtain differentially expressed genes (DEGs) and differentially expressed long non-coding RNAs (DElncRNAs). The functions and pathways involved in these genes were further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, a global regulatory network involving dysregulated genes in SI-NETs was constructed based on RNAInter and TRRUST v2 databases, and the diagnostic power of hub genes was identified by receiver operating characteristic curve (ROC). Results: A total of 2,969 DEGs and DElncRNAs were obtained in the training set. Enrichment analysis revealed that biological processes (BPs) and KEGG pathways were mainly associated with cancer. Based on gene set enrichment analysis (GSEA), we obtained five BPs (cytokinesis, iron ion homeostasis, mucopolysaccharide metabolic process, platelet degranulation and triglyceride metabolic process) and one KEGG pathway (ppar signaling pathway). In addition, the core set of dysregulated genes obtained included MYL9, ITGV8, FGF2, FZD7, and FLNC. The hub genes were upregulated in patients with primary SI-NETs compared to patients with metastatic SI-NETs, which is consistent with the training set. Significantly, the results of ROC analysis showed that the diagnostic power of the hub genes was strong in both the training and test sets. Conclusion: In summary, we constructed a global regulatory network in SI-NETs. In addition, we obtained the hub genes including MYL9, ITGV8, FGF2, FZD7, and FLNC, which may be useful for the diagnosis of patients with primary and metastatic SI-NETs.Copyright © 2023 Chen, Meng, Huang, Liao, Tang, Xu and Li.