title: Comparisons of isomiR patterns and classification performance using the rank-based MANOVA and 10-fold cross-validation.
abstract: Next generation sequencing technology has identified a series of miRNA variants (named "isomiRs"), which might be associated with cancer progression. We provide a new strategy to reanalyze the miR-seq datasets through a view of the isomiR spectrum. Firstly, differentially expressed (DE) isomiRs were detected with the DESeq algorithm based on negative binomial distribution. Secondly, the rank-based MANOVA was adopted to compare the isomiR patterns between normal and tumor tissues. Moreover, a comprehensive survey on classification performance of three features was conducted, including the logistic regression, k-nearest neighbors and Random Forest. Finally, functional enrichment analysis was performed with the putative targets of specific isomiRs to elucidate their biological functions. Furthermore, the methods were applied to the downloaded miR-seq datasets of breast invasive carcinoma from TCGA. We found that the expression levels of multiple isomiRs derived from the same miRNA locus showed significant inconsistency between normal and tumor samples. In most cases, logistic regression with multiple DE isomiRs was superior to the others, with highest AUC and lowest AIC. Similarly, DE isomiRs performed best in the average accuracy of standard classifiers. Integrated targets were significantly enriched in some cancer-related pathways, including MAPK signaling pathway, and focal adhesion. Collectively, we could recommend the rank-based MANOVA for comparing different isomiR patterns, and further investigation on isomiRs needs to be considered in miRNA sequencing research.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Yang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Li
authors.initials: L
authors.lastname: Guo
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Shao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China. Electronic address: fengchen@njmu.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D001483:Base Sequence; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D016000:Cluster Analysis; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D035683:MicroRNAs / Q000145:classification / Q000235:genetics*
chemical_list: D035683:MicroRNAs
keywords: Classification performance; Differential expression; IsomiR patterns; miRNA sequencing research