Yury O Nunez Lopez1, Berta Victoria2, Pawel Golusinski3, Wojciech Golusinski3, Michal M Masternak2,3. 1. Translational Research Institute for Metabolism & Diabetes, Florida Hospital, 301 East Princeton St., Orlando, FL 32804, USA. 2. Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Blvd., Orlando, FL 32827, USA. 3. Department of Head and Neck Surgery, The Greater Poland Cancer Centre, 15 Garbary St., 61-866 Poznan, Poland.
Abstract
AIM: 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. BACKGROUND: 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. MATERIALS AND METHODS: 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 limma 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 randomForestSRC package. RESULTS: 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). CONCLUSIONS: 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.
AIM: 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. BACKGROUND: 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. MATERIALS AND METHODS: 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 limma 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 randomForestSRC package. RESULTS: 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). CONCLUSIONS: 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.
Entities:
Keywords:
Cancer; Classifier; HNSCC; MicroRNA; Random forest; Survival
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