摘要：Head and neck squamous cell carcinoma (HNSCC) is one of the most malignant cancers with poor prognosis worldwide. Emerging evidence indicates that competing endogenous RNAs (ceRNAs) are involved in various diseases, however, the regulatory mechanisms of ceRNAs underlying HNSCC remain unclear. In this study, we retrieved differentially expressed long non-coding RNAs (DElncRNAs), messenger RNAs (DEmRNAs) and microRANs (DEmiRNAs) from The Cancer Genome Atlas database and constructed a ceRNA-based risk model in HNSCC by integrated bioinformatics approaches. Functional enrichment analyses showed that DEmRNAs might be involved in extracellular matrix related biological processes, and protein-protein interaction network further selected out prognostic genes, including MYL1 and ACTN2. Importantly, co-expressed RNAs identified by weighted co-expression gene network analysis constructed the ceRNA networks. Moreover, AC114730.3, AC136375.3, LAT and RYR3 were highly correlated to overall survival of HNSCC by Kaplan-Meier method and univariate Cox regression analysis, which were subsequently implemented multivariate Cox regression analysis to build the risk model. Our study provides a deeper understanding of ceRNAs on the regulatory mechanisms, which will facilitate the expansion of the roles on the ceRNAs in the tumorigenesis, development and treatment of HNSCC.
其他摘要：Abstract Head and neck squamous cell carcinoma (HNSCC) is one of the most malignant cancers with poor prognosis worldwide. Emerging evidence indicates that competing endogenous RNAs (ceRNAs) are involved in various diseases, however, the regulatory mechanisms of ceRNAs underlying HNSCC remain unclear. In this study, we retrieved differentially expressed long non-coding RNAs (DElncRNAs), messenger RNAs (DEmRNAs) and microRANs (DEmiRNAs) from The Cancer Genome Atlas database and constructed a ceRNA-based risk model in HNSCC by integrated bioinformatics approaches. Functional enrichment analyses showed that DEmRNAs might be involved in extracellular matrix related biological processes, and protein–protein interaction network further selected out prognostic genes, including MYL1 and ACTN2. Importantly, co-expressed RNAs identified by weighted co-expression gene network analysis constructed the ceRNA networks. Moreover, AC114730.3, AC136375.3, LAT and RYR3 were highly correlated to overall survival of HNSCC by Kaplan–Meier method and univariate Cox regression analysis, which were subsequently implemented multivariate Cox regression analysis to build the risk model. Our study provides a deeper understanding of ceRNAs on the regulatory mechanisms, which will facilitate the expansion of the roles on the ceRNAs in the tumorigenesis, development and treatment of HNSCC.
