ArticleMolecular genetics and genomics : MGG2019
Identification of the copy number variant biomarkers for breast cancer subtypes.
Article in Molecular genetics and genomics : MGG, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
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- A new insight into the impact of copy number variations on cell cycle deregulation of luminal-type breast cancer.Oncology reviews · 2025Review
- Comprehensive breast cancer risk analysis with whole exome sequencing and the prevalence ofBiomedical reports · 2024Article
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- AMD1 promotes breast cancer aggressiveness via a spermidine-eIF5A hypusination-TCF4 axis.Breast cancer research : BCR · 2024Article
- Hsa_circ_0007990 promotes breast cancer growth via inhibiting YBX1 protein degradation to activate E2F1 transcription.Cell death & disease · 2024Article
- Detecting Human Epidermal Growth Factor Receptor 2 (HER2) Amplification: Proof of Concept of an Alternative Approach.Cureus · 2023Article
- Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis.BioData mining · 2023Article
- Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.Scientific reports · 2022Article
- Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm.Cancers · 2022Article
- Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets.Cancer biology & medicine · 2022Article
- Hypermethylation of TMEM240 predicts poor hormone therapy response and disease progression in breast cancer.Molecular medicine (Cambridge, Mass.) · 2022Article
- Elevated transcription and glycosylation of B3GNT5 promotes breast cancer aggressiveness.Journal of experimental & clinical cancer research : CR · 2022Article
- New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm.Environmental science and pollution research international · 2022Article
- Copy Number Variation of Circulating Tumor DNA (ctDNA) Detected Using NIPT in Neoadjuvant Chemotherapy-Treated Ovarian Cancer Patients.Frontiers in genetics · 2022Article
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- CDK4/6 inhibitors: A potential therapeutic approach for triple negative breast cancer.MedComm · 2021Review
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
- Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification.Frontiers in genetics · 2021Article
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8 authors.
Funding
Abstract
Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew's correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.
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