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DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone.
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RECQ1 Helicase in Genomic Stability and Cancer
RECQ1 (also known as RECQL or RECQL1) belongs to the RecQ family of DNA helicases, members of which are linked with rare genetic diseases of cancer predisposition in humans. RECQ1 is implicated in several cellular processes, including DNA repair, cell cycle and growth, telomere maintenance, and transcription. Earlier studies have demonstrated a unique requirement of RECQ1 in ensuring chromosomal stability and suggested its potential involvement in tumorigenesis. Recent reports have suggested that RECQ1 is a potential breast cancer susceptibility gene, and missense mutations in this gene contribute to familial breast cancer development. Here, we provide a framework for understanding how the genetic or functional loss of RECQ1 might contribute to genomic instability and cancer.
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- Award ID(s):
- 1832163
- PAR ID:
- 10173473
- Date Published:
- Journal Name:
- Genes
- Volume:
- 11
- Issue:
- 6
- ISSN:
- 2073-4425
- Page Range / eLocation ID:
- 622
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/genes11060622
