bioRxivBioinformaticsPreprint
A comprehensive investigation of m6A regulators prognostic value and associated molecular pathways in breast invasive cancer
Abstract
Breast invasive cancer (BIC) is one of the most commonly observed and the deadliest cancer among women. Despite the progress that has been made in improving breast cancer outcomes by the development of advanced treatment options, due to the heterogeneity and complexity of the disease, more studies are required to explore underlying molecular mechanisms of breast cancer which may provide useful insights to overcome the constraints related to current therapeutic options. The goal of this study was to reveal the crucial roles of m6A regulatory proteins in BIC development using various publicly available datasets and databases. We first conducted a comprehensive analysis to depict the mutation frequency and types for m6A regulatory genes in sub-types of BIC for the evaluation of the genetic alterations landscape of breast cancer. Changes in expression levels of m6A regulatory factors were identified as the key genetic alteration in BIC. Implementation of Kaplan-Meier tool to assess the predictive value of m6A pathway components in BIC validated the use of VIRMA, METTL14, RBM15B, EIF3B, YTHDF1, and YTHDF3 as prognostic biomarkers of breast cancer. We then examined the enriched gene ontology (GO) terms and KEGG pathways for the tumor samples with genetic alterations in the m6A pathway. Dysregulation of m6A regulatory factors in BIC was associated with cell division and survival-related pathways such as nuclear division and chromosome segregation via the upregulation of the genes functioning in these biological processes and the gained overactivity of these pathways may account for poor prognosis of the disease. The performed analyses highlighted m6A pathway genes as potential regulators of BIC growth and as a valuable set to be utilized as clinical biomarkers in BIC disease.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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