A key genomic signature associated with lymphovascular invasion in head and neck squamous cell carcinoma

BMC Cancer

Huali Jiang¹  Rong Li²  Xiaoting Huang²  Anan Xu²  Yawei Yuan²  Tao Xie²  Baiyao Wang²  Jian Zhang²  Jie Lin²  Hualong Jiang³  Jiexia Zhang⁴  Huaming Lin⁵ 

[1] Department of Cardiovascularology, Tungwah Hospital of Sun Yat-sen University;Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Disease;Department of Urology, Tungwah Hospital of Sun Yat-sen University;State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University;The First Tumor Department, Maoming People’s Hospital;

关键词: Lymphovascular invasion;    Head and neck squamous cell carcinoma;    Hub genes;    TCGA;    Weighted gene co-expression network analysis;   

DOI  :  10.1186/s12885-020-06728-1

来源: DOAJ

【 摘 要 】

Abstract Background Lymphovascular invasion (LOI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), is predictive of poor survival; however, the associated clinical characteristics and underlying molecular mechanisms remain largely unknown. Methods We performed weighted gene co-expression network analysis to construct gene co-expression networks and investigate the relationship between key modules and the LOI clinical phenotype. Functional enrichment and KEGG pathway analyses were performed with differentially expressed genes. A protein–protein interaction network was constructed using Cytoscape, and module analysis was performed using MCODE. Prognostic value, expression analysis, and survival analysis were conducted using hub genes; GEPIA and the Human Protein Atlas database were used to determine the mRNA and protein expression levels of hub genes, respectively. Multivariable Cox regression analysis was used to establish a prognostic risk formula and the areas under the receiver operating characteristic curve (AUCs) were used to evaluate prediction efficiency. Finally, potential small molecular agents that could target LOI were identified with DrugBank. Results Ten co-expression modules in two key modules (turquoise and pink) associated with LOI were identified. Functional enrichment and KEGG pathway analysis revealed that turquoise and pink modules played significant roles in HNSCC progression. Seven hub genes (CNFN, KIF18B, KIF23, PRC1, CCNA2, DEPDC1, and TTK) in the two modules were identified and validated by survival and expression analyses, and the following prognostic risk formula was established: [risk score = EXPDEPDC1 * 0.32636 + EXPCNFN * (− 0.07544)]. The low-risk group showed better overall survival than the high-risk group (P < 0.0001), and the AUCs for 1-, 3-, and 5-year overall survival were 0.582, 0.634, and 0.636, respectively. Eight small molecular agents, namely XL844, AT7519, AT9283, alvocidib, nelarabine, benzamidine, L-glutamine, and zinc, were identified as novel candidates for controlling LOI in HNSCC (P < 0.05). Conclusions The two-mRNA signature (CNFN and DEPDC1) could serve as an independent biomarker to predict LOI risk and provide new insights into the mechanisms underlying LOI in HNSCC. In addition, the small molecular agents appear promising for LOI treatment.

【 授权许可】

Unknown