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 = EXP DEPDC1 * 0.32636 + EXP CNFN * (−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.

Objective: Lymphovascular invasion (LOI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), predicts poor survival. However, the associated clinical characteristics remain uncertain, and the molecular mechanisms are largely unknown. Methods: Weighted gene co-expression network analysis was performed to construct gene co-expression networks and investigate the relationship between modules and LOI clinical trait. Functional enrichment and KEGG pathway enrichment analysis were performed for differentially expressed genesusing DAVID database. The protein-protein interaction network was constructed using Cytoscape software, and module analysis was performed using MCODE. Survival analysis and unsupervised hierarchical clustering were used to evaluate the relationships among LOI-associated genomic subtype, clinicopathological features and patient outcomes. And the potential targeted LOI molecular agents were identified with DrugBank. Results: 10 co-expression modules in two key modules (turquoise and pink) associated with tumor LOI were identified. Functional enrichment and KEGG analysis identified turquoise and pink modules played significant roles in the progression of HNSCC. The 24 genes in two modules were identified as hub genes. Clustering analysis with seven hub genes set further divided cases into subtypes 1 and 2, which were significantly associated with pathology-determined LOI status in both cohorts. The 10-year overall survival of subtype 2 was significantly worse than that of subtype 1. Conclusions: Our research revealed the key co-expression modules and identified seven prognostic biomarkers, including CCNA2, CNFN, DEPDC1, KIF18, KIF23, PRC1, TTK, which provide some new insights into LOI of HNSCC. Additionally, the small molecular agents may be a candidate drug for treating LOI.

Objective: Lymphovascular invasion (LOI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), predicts poor survival. However, the associated clinical characteristics remain uncertain, and the molecular mechanisms are largely unknown. Methods: Weighted gene co-expression network analysis was performed to construct gene co-expression networks and investigate the relationship between modules and LOI clinical trait. Functional enrichment and KEGG pathway enrichment analysis were performed for differentially expressed genes using DAVID database. The protein-protein interaction network was constructed using Cytoscape software, and module analysis was performed using MCODE. Prognosis role and expression analysis was further validated by survival analysis, GEPIA analysis and HPA database. 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. And the potential targeted LOI molecular agents were identified with DrugBank. Results: 10 co-expression modules in two key modules (turquoise and pink) associated with tumor LOI were identified. Functional enrichment and KEGG analysis identified turquoise and pink modules played significant roles in the progression of HNSCC. The seven hub genes (CNFN, KIF18B, KIF23, PRC1, CCNA2, DEPDC1 and TTK) in two modules were identified and validated by survival analysis and expression analysis. Multivariable Cox regression analysis was used to establish a prognostic risk formula (risk score = EXP DEPDC1 * 0.32636 + EXP CNFN * (-0.07544). The low-risk group had a better OS than the high-risk group ( P <0.001), and the areas under the receiver operating characteristic curve (AUCs) of 1-, 3- and 5-year OS were 0.582, 0.634 and 0.636, respectively. Eight small molecular agents, including XL844, AT7519, AT9283, Alvocidib, Nelarabine, Benzamidine, L-Glutamine, and Zinc, may be a candidate drug for treating LOI ( P < 0.05). Conclusions: Our research revealed the two-mRNA signature could serve as an independent biomarker to predict LOI risk, which provide some new insights into LOI of HNSCC. Additionally, the small molecular agents may be a candidate drug for treating LOI.