An isomiR expression panel based novel breast cancer classification approach using improved mutual information

Abstract

BackgroundGene expression-based profiling has been used to identify biomarkers for different breast cancer subtypes. However, this technique has many limitations. IsomiRs are isoforms of miRNAs that have critical roles in many biological processes and have been successfully used to distinguish various cancer types. Biomarker isomiRs for identifying different breast cancer subtypes has not been investigated. For the first time, we aim to show that isomiRs are better performing biomarkers and use them to explain molecular differences between breast cancer subtypes.ResultsIn this study, a novel method is proposed to identify specific isomiRs that faithfully classify breast cancer subtypes. First, as a null hypothesis method we removed the lowly expressed isomiRs from small sequencing data generated from diverse breast cancers types. Second, we developed an improved mutual information-based feature selection method to calculate the weight of each isomiR expression. The weight of isomiR measures the importance of a given isomiR in classifying breast cancer subtypes. The improved mutual information enables to apply the dataset in which the feature is continuous data and label is discrete data; whereby, the traditional mutual information cannot be applied in this dataset. Finally, the support vector machine (SVM) classifier is applied to find isomiR biomarkers for subtyping.ConclusionsHere we demonstrate that isomiRs can be used as biomarkers in the identification of different breast cancer subtypes, and in addition, they may provide new insights into the diverse molecular mechanisms of breast cancers. We have also shown that the classification of different subtypes of breast cancer based on isomiRs expression is more effective than using published gene expression profiling. The proposed method provides a better performance outcome than Fisher method and Hellinger method for discovering biomarkers to distinguish different breast cancer subtypes. This novel technique could be directly applied to identify biomarkers in other diseases.

Key MicroRNA’s and Their Targetome in Adrenocortical Cancer

“…More recently, Wang and colleagues used the same data to demonstrate that isomiRs that share their seed region (5 isoforms) could similarly discriminate between tumors [70]. In 2018, Lan and colleagues published the first study to use breast cancer TCGA small RNA sequencing expression data to show that the isomiR expression-based classification was superior to gene expression profiling at distinguishing between breast cancer subtypes [71]. Together these findings suggest that miRNAs may carry out significant functional roles in tumorigenesis at the isomiR level.…”

RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs

“…IsomiRs are functionally important and have distinct roles in tissue-specific gene regulation [203]. Furthermore, isomiR expression changes in disease and when compared to canonical miRNAs, can better classify different tumour types [204,205]. Therefore, the specificity of diagnostic and prognostic panels could be considerably improved if isomiRs were also tested for their suitability as biomarkers.…”

The Butterfly Effect of RNA Alterations on Transcriptomic Equilibrium

“…Recently, it has been demonstrated that the presence of 5 isomiRs could effectively classify 32 different tumor types with more than 90% sensitivity [150,155]. Findings from Telonis et al [149] and Lan et al [156] support the advantages of using isomiRs in the classification of breast cancer subtypes compared with protein-coding gene expression. High levels of isomiRs hsa-let-7i-5p | 3 a-1 and hsa-miR-197-3p | 3 a-1 have been found in ER negative tumors, but not in ER positive tumors [156].…”

“…High levels of isomiRs hsa-let-7i-5p | 3 a-1 and hsa-miR-197-3p | 3 a-1 have been found in ER negative tumors, but not in ER positive tumors [156]. On the other hand, the 5 isomiR hsa-miR-93-5p | 5 t-1 disrupts the estrogen signalling pathway by targeting SHC4, while hsa-miR-27a-3p | 5 t-1, hsa-miR-92a-1-3p | 5 -1, and hsa-miR-106b-3p | 5 t-1 interfere with mitogen-activated protein kinase (MAPK) signalling by targeting MAPK14, MAPK8, and RAP1B, respectively, in the ER positive tumors, showing the high specificity of these isomiRs in cancer subtype classification [156].…”

“…On the other hand, it has been shown that 3 modifications modulate miRNA stability [156,160]. For example, 3 adenylation of miR-21 by PAPD5 (also known as TUT3) accelerates miR-21 degradation [152], while 3 adenylation of miR-122 by PAPD4 (also known as TUT2) stabilizes the mature form of miR-122 in the liver [160].…”