Nunez Lopez Yury O, Victoria Berta, Golusinski Pawel, Golusinski Wojciech, Masternak Michal M
Translational Research Institute for Metabolism & Diabetes, Florida Hospital, 301 East Princeton St., Orlando, FL 32804, USA.
Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Blvd., Orlando, FL 32827, USA.
Rep Pract Oncol Radiother. 2018 Jan-Feb;23(1):6-20. doi: 10.1016/j.rpor.2017.10.003. Epub 2017 Nov 20.
To characterize the miRNA expression profile in head and neck squamous cell carcinoma (HNSSC) accounting for a broad range of cancer subtypes and consequently identify an optimal miRNA signature with prognostic value.
HNSCC is consistently among the most common cancers worldwide. Its mortality rate is about 50% because of the characteristic aggressive behavior of these cancers and the prevalent late diagnosis. The heterogeneity of the disease has hampered the development of robust prognostic tools with broad clinical utility.
The Cancer Genome Atlas HNSC dataset was used to analyze level 3 miRNA-Seq data from 497 HNSCC patients. Differential expression (DE) analysis was implemented using the package and multivariate linear model that adjusted for the confounding effects of age at diagnosis, gender, race, alcohol history, anatomic neoplasm subdivision, pathologic stage, T and N stages, and vital status. Random forest (RF) for survival analysis was implemented using the package.
A characteristic DE miRNA signature of HNSCC, comprised of 11 upregulated (i.e., miR-196b-5p, miR-1269a, miR-196a-5p, miR-4652-3p, miR-210-3p, miR-1293, miR-615-3p, miR-503-5p, miR-455-3p, miR-205-5p, and miR-21-5p) and 9 downregulated (miR-376c-3p, miR-378c, miR-29c-3p, miR-101-3p, miR-195-5p, miR-299-5p, miR-139-5p, miR-6510-3p, miR-375) miRNAs was identified. An optimal RF survival model was built from seven variables including age at diagnosis, miR-378c, miR-6510-3p, stage N, pathologic stage, gender, and race (listed in order of variable importance).
The joint differential miRNA expression and survival analysis controlling for multiple confounding covariates implemented in this study allowed for the identification of a previously undetected prognostic miRNA signature characteristic of a broad range of HNSCC.
描述涵盖广泛癌症亚型的头颈部鳞状细胞癌（HNSSC）中的微小RNA（miRNA）表达谱，从而确定具有预后价值的最佳miRNA特征。
头颈部鳞状细胞癌一直是全球最常见的癌症之一。由于这些癌症具有侵袭性特征且普遍诊断较晚，其死亡率约为50%。该疾病的异质性阻碍了具有广泛临床应用价值的可靠预后工具的开发。
使用癌症基因组图谱头颈部鳞状细胞癌数据集分析来自497名头颈部鳞状细胞癌患者的3级miRNA测序数据。使用该软件包和多变量线性模型进行差异表达（DE）分析，该模型针对诊断时年龄、性别、种族、饮酒史、肿瘤解剖细分、病理分期、T和N分期以及生存状态的混杂效应进行了调整。使用该软件包进行随机森林（RF）生存分析。
确定了头颈部鳞状细胞癌的一个特征性DE miRNA特征，由11个上调（即miR-196b-5p、miR-1269a、miR-196a-5p、miR-4652-3p、miR-210-3p、miR-1293、miR-615-3p、miR-503-5p、miR-455-3p、miR-205-5p和miR-21-5p）和9个下调（miR-376c-3p、miR-378c、miR-29c-3p、miR-101-3p、miR-195-5p、miR-299-5p、miR-139-5p、miR-6510-3p、miR-375）的miRNA组成。从包括诊断时年龄、miR-378c、miR-6510-3p、N分期、病理分期、性别和种族（按变量重要性顺序列出）的七个变量构建了一个最佳RF生存模型。
本研究中实施的联合差异miRNA表达和生存分析，同时控制了多个混杂协变量，从而能够识别出一种先前未被发现的、广泛的头颈部鳞状细胞癌特征性预后miRNA特征。
