Opposite effects of demethylating treatment on VEGFA and HIF1A expression in MDA-MB-231 breast cancer cell line in hypoxic microenvironment

Amorim, Ísis Salviano Soares de; Souza, Layane Duarte de; Rodrigues, Juliana Alves; Fonseca, Adenilson de Souza da; König, Sandra; Mencalha, André Luiz

doi:10.1016/j.genrep.2019.100403

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Therefore, identifying molecular targets can lead to a more accurate diagnosis of the type of BC, precisely targeted treatments, and potentially treatments that are less toxic than the current ones 21 . In this regard, it is possible to highlight some proteins as potential molecular targets, like Transmembrane Protein 170B (TMEM170B) 22 , Lipocalin 2 (LCN2) 23 , Macrophage Migration Inhibitory Factor (MIF) 24 , Signal Transducer and Activator of Transcription 3 (STAT3) 25 , Tumor Protein P53 26 , Vascular Endothelial Growth Factor A (VEGFA) 27 , Estrogen Receptor 1(ESR1) 28 , BCL2 Associated X (BAX) 29 , Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) 30 , Matrix Metallopeptidase 1 (MMP1) 31 , and Maternal Embryonic Leucine Zipper Kinase (MELK), among others 32 . However, some of these molecules are biased to certain types of BC for a specific signal or were identified from a small biological sample 33 .…”

Section: Introductionmentioning

confidence: 99%

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Mares-Quiñones,

Galán-Vásquez,

Pérez-Rueda

et al. 2024

Sci Rep

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Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Mares-Quiñones,

Galán-Vásquez,

Pérez-Rueda

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Therefore, identifying molecular targets can lead to a more accurate diagnosis of the type of BC, precisely targeted treatments, and potentially less toxic than actual ones 139 . In such a way, it is possible to point to some proteins as potential molecular targets, like the Transmembrane Protein 170B (TMEM170B) 64 , Lipocalin 2 (LCN2) 85 , Macrophage Migration Inhibitory Factor (MIF) 15 , Signal Transducer and Activator of Transcription 3 (STAT3) 28 , Tumor Protein P53 (TP53) 131 , Vascular Endothelial Growth Factor A (VEGFA) 24 , Estrogen Receptor 1(ESR1) 27 (Dustin et al, 2021), BCL2 Associated X (BAX) 7 , Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) 138 , Matrix Metallopeptidase 1 (MMP1) 119 , and Maternal Embryonic Leucine Zipper Kinase (MELK), among others 88 . However, some of these molecules are biased for certain types of BC, for a speci c signal, or were identi ed from a small biological sample 19 .…”

Section: Introductionmentioning

confidence: 99%

Identification of modules and key genes associated with Breast Cancer subtypes through network analysis

Mares-Quiñones

Galán-Vásquez

Pérez‐Rueda

et al. 2023

Preprint

View full text Add to dashboard Cite

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression network analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3 a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

show abstract

Opposite effects of demethylating treatment on VEGFA and HIF1A expression in MDA-MB-231 breast cancer cell line in hypoxic microenvironment

Cited by 2 publications

References 12 publications

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Identification of modules and key genes associated with Breast Cancer subtypes through network analysis

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