ArticleCancers2020
Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Targeting Triple-Negative Breast Cancer: Resistance Mechanisms and Therapeutic Advancements.Cancer medicine · 2025Review
- Advances in the Understanding of the Pathogenesis of Triple-Negative Breast Cancer.Cancer medicine · 2024Review
- Bioinformatics characterization of variants of uncertain significance in pediatric sensorineural hearing loss.Frontiers in pediatrics · 2024Article
- Efficacy of metformin and electrical pulses in breast cancer MDA-MB-231 cells.Exploration of targeted anti-tumor therapy · 2024Article
- Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network.Orthopaedic surgery · 2023Article
- Multiomics insights on the onset, progression, and metastatic evolution of breast cancer.Frontiers in oncology · 2023Review
- Integrating Genomic Information with Tumor-Immune Microenvironment in Triple-Negative Breast Cancer.International journal of environmental research and public health · 2022Article
- PARP Inhibitors: A Major Therapeutic Option in Endocrine-Receptor Positive Breast Cancers.Cancers · 2022Review
- Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC).
methodsWe performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer.
resultsThe investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways.
conclusionThe investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.
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