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Expression scoring of a small‐nucleolar‐RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer
Author(s) -
Xing Lu,
Zhang Xiaoqi,
Zhang Xiaoqian,
Tong Dongdong
Publication year - 2020
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.29462
Subject(s) - small nucleolar rna , biology , carcinogenesis , malignancy , oncology , head and neck squamous cell carcinoma , univariate , proportional hazards model , biomarker , survival analysis , multivariate analysis , transcriptome , cancer , medicine , long non coding rna , rna , head and neck cancer , gene , multivariate statistics , gene expression , genetics , statistics , mathematics
Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival‐related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival‐related snoRNAs were identified, and a five‐snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five‐snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five‐snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.