Expression scoring of a small‐nucleolar‐RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Abstract

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.

Identification of Immune Subtypes for Predicting the Prognosis of Patients in Head and Neck Squamous Cell Carcinoma

“…First, our research is based on bioinformatics data analysis, which is theoretically feasible but not experimentally verified. 41 Experimental verification is necessary if a certain result is to be obtained. Secondly, the samples of this study are mainly from western populations, so it is unknown whether the conclusions of this study could apply to eastern populations, which may be biased.…”

Identification of a novel small nucleolar RNAs prognostic signature for patients with acute myelocytic leukemia

“…Huang et al identi ed several snoRNAs signi cantly related to the prognosis of colon adenocarcinoma by analyzing the TCGA RNA-seq dataset, and constructed a prognosis signature consisting of two snoRNAs expressions [14]. Similar to the research by Huang et al, Xing et al identi ed snoRNAs of 113 snoRNAs signi cantly related to the prognosis of HNSC by using the RNA-seq dataset of the TCGA head and neck squamous cell carcinoma (HNSC) cohort, and used the least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic signature that included 5 prognostic snoRNAs, which can signi cantly divide HNSC patients into high-and low-risk phenotypes [13]. In addition, Zhao et al used TCGA ccRCC RNA-seq dataset to identify a 6snoRNA signature for ccRCC diagnosis and prognosis prediction [12].…”

“…As early studies found that snoRNA was mainly located in the nucleoli and was related to the processing and modi cation of rRNA, its function was relatively simple [9][10][11]. However, in recent years, more and more RNA sequencing (RNA-seq) dataset show that snoRNA shows a general trend of high expression in tumors, and some studies show that snoRNA is involved in the occurrence, progression and prognosis of tumors, and may be a kinds of new clinical biomarkers [12][13][14].…”

Application of Machine Learning to Stomatology: A Comprehensive Review

“…Small nucleolar RNAs (snoRNAs) play important roles in tumorigenesis [99]. Lu et al [100] identified survivalrelated snoRNAs of Head and neck squamous cell carcinoma (HNSCC) by Cox regression analysis. A five-snoRNA (SNORD114-17: ENSG00000201569, SNORA36B: ENSG00000222370, SNORD78: ENSG00000212378, U3: ENSG00000212182, and U3: ENSG00000212195) was finally obtained to construct risk model.…”