Department of Orthodontics, School of Stomatology, China Medical University, Shenyang, Liaoning 110002, P.R. China.
Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116027, P.R. China.
Mol Med Rep. 2020 Mar;21(3):1647-1657. doi: 10.3892/mmr.2020.10964. Epub 2020 Jan 27.
The present study aimed to identify sensitive, specific and independent prognostic biomarkers in head and neck cancer (HNC) based on microRNA expression profiles and other high‑throughput sequencing data in The Cancer Genome Atlas (TCGA) database. Identification of such prognostic biomarkers could provide insight into HNC diagnosis and treatment. The differential expression profiles of microRNAs between HNC tissues and adjacent cancer tissues in the TCGA database were analyzed (log fold‑change >2; P<0.01). Univariate and multivariate Cox regression analyses of the differentially expressed microRNAs were performed to determine those significantly related to the survival of patients with HNC. The identified microRNAs were verified by survival and receiver operating characteristic curve analyses. To better predict prognosis, a combined prognostic model (risk equation) was established based on the risk coefficient of each microRNA, calculated by a multivariate Cox regression analysis, and the risk score was calculated. To explore the signaling pathways related to prognosis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on the differentially expressed genes between the high‑risk and low‑risk groups, grouped according to the median risk score. A total of 89 differentially expressed microRNAs between HNC and adjacent cancer tissues were screened, 11 of which were identified as risk factors related to HNC survival by the univariate Cox regression analysis (P<0.05). The multivariate Cox regression analysis showed that three of the 11 microRNAs, hsa‑miR‑99a, hsa‑miR‑499a and hsa‑miR‑1911 (all P<0.01), were identified as independent risk factors significantly related to patient survival. The risk equation used was as follows: Risk score=(‑0.1597 x hsa‑miR‑99a) + (0.1871 x hsa‑miR‑499a) + (0.1033 x hsa‑miR‑1911). KEGG and GO analyses showed that the JAK‑STAT signaling pathway and some metabolic pathways were associated with HNC prognosis. The present study suggested that hsa‑miR‑99a, hsa‑miR‑499a and hsa‑miR‑1911 may serve as potential prognostic biomarkers in HNC.
本研究旨在基于癌症基因组图谱（TCGA）数据库中的 microRNA 表达谱和其他高通量测序数据，鉴定头颈部癌症（HNC）中敏感、特异和独立的预后生物标志物。鉴定此类预后生物标志物可为 HNC 的诊断和治疗提供新的思路。分析 TCGA 数据库中 HNC 组织与癌旁组织之间 microRNA 的差异表达谱（log 倍数变化>2；P<0.01）。对差异表达 microRNA 进行单因素和多因素 Cox 回归分析，以确定与 HNC 患者生存显著相关的 microRNA。通过生存和受试者工作特征曲线分析验证鉴定出的 microRNA。为了更好地预测预后，根据多因素 Cox 回归分析计算的每个 microRNA 的风险系数，建立了基于风险系数的联合预后模型（风险方程），并计算风险评分。为了探索与预后相关的信号通路，根据中位风险评分将高风险和低风险组进行分组，对高风险和低风险组之间的差异表达基因进行京都基因与基因组百科全书（KEGG）和基因本体论（GO）分析。筛选出 HNC 与癌旁组织之间的 89 个差异表达 microRNA，其中 11 个通过单因素 Cox 回归分析（P<0.05）被鉴定为与 HNC 生存相关的危险因素。多因素 Cox 回归分析显示，这 11 个 microRNA 中的 3 个，hsa-miR-99a、hsa-miR-499a 和 hsa-miR-1911（均 P<0.01），被鉴定为与患者生存显著相关的独立危险因素。使用的风险方程如下：风险评分=（-0.1597 x hsa-miR-99a）+（0.1871 x hsa-miR-499a）+（0.1033 x hsa-miR-1911）。KEGG 和 GO 分析表明，JAK-STAT 信号通路和一些代谢途径与 HNC 预后相关。本研究提示 hsa-miR-99a、hsa-miR-499a 和 hsa-miR-1911 可能作为 HNC 潜在的预后生物标志物。
