Huang Sicong, Chen Ruiqi, Gao Shihua, Shi Yongjie, Xiao Qiwen, Zhou Qiang, Wei Jie, Kang Jiale, Sun Weimin, Hu Yingyu, Shen Gang, Jia Hongyun
Department of Clinical Laboratory, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, China.
Department of Transfusion Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
J Oncol. 2022 Dec 1;2022:9395876. doi: 10.1155/2022/9395876. eCollection 2022.
Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce.
The "limma" package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes' discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes.
A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the "valine, leucine, and isoleucine degradation" pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the "blood vessel development" and "vasculature development" in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted.
This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.
婴儿血管瘤（IHs）是婴儿期常见的良性血管肿瘤，可能会产生严重后果。关于IHs诊断标志物的研究较少。
应用“limma”软件包识别发育中的IHs中差异表达基因（DEGs）。在Cytoscape软件中使用插件ClueGO对DEGs进行功能富集分析。利用检索相互作用基因的搜索工具（STRING）数据库构建蛋白质-蛋白质相互作用（PPI）网络。采用最小绝对收缩和选择算子（LASSO）回归模型和支持向量机递归特征消除（SVM-RFE）分析来识别IHs的诊断基因。通过受试者工作特征（ROC）曲线评估诊断基因的鉴别能力。基于基因本体（GO）和京都基因与基因组百科全书（KEGG）的单基因基因集富集分析（GSEA）。通过比较毒理基因组学数据库（CTD）挖掘与诊断基因相关的化学物质。最后，利用在线网站Network Analyst预测靶向诊断基因的转录因子。
从6个月、12个月和24个月大婴儿的IHs样本中总共筛选出205个DEGs。这些基因主要参与血管生成和发育相关、内皮细胞相关的生物学过程。然后我们挖掘出127个相互作用蛋白，并创建了一个包含127个节点和251条边的网络。此外，LASSO和SVM-RRF算法鉴定出5个诊断基因，即跨膜蛋白2（TMEM2）、鸟苷酸环化酶1A2（GUCY1A2）、胰岛1（ISL1）、色氨酸-tRNA合成酶（WARS）和前列腺六跨膜上皮抗原4（STEAP4）。ROC曲线分析结果表明，这些诊断基因具有强大的能力区分IHs样本与正常样本。接下来，单基因的GSEA结果表明，所有5个诊断基因在IHs发育过程中均抑制“缬氨酸、亮氨酸和异亮氨酸降解”途径。WARS、TMEM2和STEAP4在IHs中激活“血管发育”和“脉管系统发育”。随后，挖掘出靶向TMEM2、GUCY1A2、ISL1和STEAP4的抑制剂。最后，预测出14个调控GUCY1A2的转录因子、14个调控STEAP4的转录因子和26个调控ISL1的转录因子。
本研究鉴定出5个IHs诊断标志物，并进一步探索了其机制和靶向药物，为IHs的诊断和治疗提供了依据。
