Identification of Diagnostic Markers in Infantile Hemangiomas

Sicong Huang; Ruiqi Chen; Shihua Gao; Yongjie Shi; Qiwen Xiao; Qiang Zhou; Jie Wei; Jiale Kang; Weimin Sun; Yingyu Hu; Gang Shen; Hongyun Jia

doi:10.1155/2022/9395876

Identification of Diagnostic Markers in Infantile Hemangiomas

J Oncol. 2022 Dec 1:2022:9395876. doi: 10.1155/2022/9395876. eCollection 2022.

Authors

Sicong Huang¹, Ruiqi Chen², Shihua Gao³, Yongjie Shi¹, Qiwen Xiao¹, Qiang Zhou¹, Jie Wei¹, Jiale Kang¹, Weimin Sun¹, Yingyu Hu⁴, Gang Shen⁵, Hongyun Jia¹

Affiliations

¹ Department of Clinical Laboratory, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, China.
² Department of Transfusion Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
³ Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China.
⁴ Department of Hospital Management, Southern Medical University, Guangzhou, Guangdong 510515, China.
⁵ Department of Interventional Hemangioma, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China.

Abstract

Background: Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce.

Methods: The "limma" R 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.

Results: 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.

Conclusion: This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.