mRNA in Breast Cancer: A Survey of Detection and Classification Methodologies

Breast cancer remains a serious concern, for women. The introduction of profiling techniques particularly analyzing expression has significantly transformed the approach to diagnosing and categorizing breast cancer offering more tailored treatment options. It is a potent method for diagnostic and classification purposes. In fact, mRNA profiling reveals gene expression profiles related to the most prevalent subtypes of the breast cancer. The process behind the detection and classification of the breast cancer by mRNA data is scrupulously reviewed and reported in this survey. Among these methods for the detection of the phenotypic heterogeneity of the breast cancer included in this survey are also machine-learning methods like the convolutional neural networks (CNNs). Several applications of mRNA in the investigation of genetic phenotype and the history of the use of mRNA for diagnostic and classification purposes in breast cancer are discussed in this overview. It explains how mRNA can allow the reproduction of phenotypic breast cancer heterogeneity with the use of transcriptome expression panels and illustrates the challenges and the current developments in the therapeutic approaches using mRNA and the application of these techniques in the context of the breast cancer experiments. This research serves the aim to demonstrate how breast cancer diagnosis and therapy could be far accurate and more personalized by examining the advantages and disadvantages of the current techniques.