Background This study aimed to develop multi-RNA-based models using a competing endogenous RNA (ceRNA) regulatory network to provide survival risk prediction in head and neck squamous cell carcinoma (HNSCC). Methods All long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA expression data and clinicopathological features related to HNSCC were derived from The Cancer Genome Atlas. Differentially expressed RNAs were calculated using R. Prognostic factors were identified using univariate Cox regression analysis. Functional analysis was performed using GO, KEGG pathways, and PPI network. Based on the results, we derived a risk signature and compared high- and low-risk subgroups using LASSO regression analysis. Survival analysis and the relationship between risk signature and clinicopathological features were performed using log-rank tests and Cox regression analysis. A ceRNA regulatory network was constructed, and prognostic lncRNAs and miRNA expression levels were validated in vitro and in vivo. Results A list of 207 lncRNAs, 18 miRNAs and 362 mRNAs related to overall survival was established. Five lncRNAs (HOTTIP, LINC00460, RMST, SFTA1P, and TM4SF19-AS1), one miRNA (hsa-miR-206), and one mRNA (STC2) were used to construct the ceRNA network. Three prognostic models contained 13 lncRNAs, eight miRNAs, and 17 mRNAs, which correlated with the patient status, disease-free survival (DFS), stage, grade, T stage, N stage, TP53 mutation status, angiolymphatic invasion, HPV status, and extracapsular spread. KEGG pathway analysis revealed significant enrichment of “Transcriptional misregulation in cancer” and “Neuroactive ligand-receptor interaction.” In addition, HOTTIP, LINC00460, miR-206 and STC2 were validated in GTEx data, GEO microarrays and six HNSCC cell lines. Conclusions Our findings clarify the interaction of ceRNA regulatory networks and crucial clinicopathological features. These results show that prognostic biomarkers can be identified by constructing multi-RNA-based prognostic models, which can be used for survival risk prediction in patients with HNSCC.

中文翻译：

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头颈部鳞状细胞癌预后长非编码 RNA 相关竞争性内源性 RNA 网络

背景 本研究旨在利用竞争性内源性 RNA (ceRNA) 调控网络开发基于多 RNA 的模型，以预测头颈部鳞状细胞癌 (HNSCC) 的生存风险。方法 所有与 HNSCC 相关的长链非编码 RNA (lncRNA)、microRNA (miRNA) 和 mRNA 表达数据和临床病理学特征均来自癌症基因组图谱。使用 R 计算差异表达的 RNA。使用单变量 Cox 回归分析确定预后因素。使用 GO、KEGG 通路和 PPI 网络进行功能分析。根据结果​​，我们得出了风险特征，并使用 LASSO 回归分析比较了高风险和低风险亚组。使用对数秩检验和 Cox 回归分析进行生存分析以及风险特征和临床病理学特征之间的关系。构建了一个 ceRNA 调控网络，并在体外和体内验证了预后 lncRNA 和 miRNA 表达水平。结果建立了与总生存期相关的207个lncRNA、18个miRNA和362个mRNA的列表。使用五种 lncRNA（HOTTIP、LINC00460、RMST、SFTA1P 和 TM4SF19-AS1）、一种 miRNA（hsa-miR-206）和一种 mRNA（STC2）来构建 ceRNA 网络。三个预后模型包含 13 个 lncRNA、8 个 miRNA 和 17 个 mRNA，它们与患者状态、无病生存期 (DFS)、分期、分级、T 分期、N 分期、TP53 突变状态、血管淋巴浸润、HPV 状态和包膜外扩散。KEGG 通路分析揭示了“癌症中的转录失调”和“神经活性配体-受体相互作用”的显着富集。此外，HOTTIP、LINC00460、miR-206 和 STC2 在 GTEx 数据、GEO 微阵列和六种 HNSCC 细胞系中得到验证。结论 我们的研究结果阐明了 ceRNA 调控网络和关键临床病理学特征的相互作用。这些结果表明，可以通过构建基于多 RNA 的预后模型来识别预后生物标志物，该模型可用于 HNSCC 患者的生存风险预测。结论 我们的研究结果阐明了 ceRNA 调控网络和关键临床病理学特征的相互作用。这些结果表明，可以通过构建基于多 RNA 的预后模型来识别预后生物标志物，该模型可用于 HNSCC 患者的生存风险预测。结论 我们的研究结果阐明了 ceRNA 调控网络和关键临床病理学特征的相互作用。这些结果表明，可以通过构建基于多 RNA 的预后模型来识别预后生物标志物，该模型可用于 HNSCC 患者的生存风险预测。