miRNA expression profiling of hereditary breast tumors from BRCA1- and BRCA2-germline mutation carriers in Brazil

Pessôa‐Pereira, Danielle; Evangelista, Adriane Feijó; Causin, Rhafaela Lima; Vieira, René Aloísio da Costa; Abrahão-Machado, Lucas Faria; Santana, Iara Viana Vidigal; Silva, Vinícius Duval da; Souza, Karen Cristina Borba; Oliveira-Silva, Renato José de; Fernandes, Gabriela; Reis, Rui Manuel; Palmero, Edenir Inêz; Marques, Márcia Maria Chiquitelli

doi:10.1186/s12885-020-6640-y

Cited by 11 publications

(12 citation statements)

References 55 publications

(62 reference statements)

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“…Distinct microRNA signatures specifically characterized hereditary breast cancer (HBC), sporadic breast cancer (SBC) and HBCs of unknown genetic origin (also termed ‘BRCAX) from normal breast tissues (NBT) that are also wild-type/carriers/non-carriers of germline pathogenic variants of tumor suppressor genes, breast cancer type 1 and 2 ( BRCA1 and BRCA2 ) susceptibility genes [ 62 , 63 ]. Gene interaction network modeling linked BRCA1 mutations to the overexpression of insulin-like growth factor receptor-1β (IGF-R1β), which in turn led to the overexpression of HER2 and epithelial growth factor receptor (EGFR) proteins [ 64 ].…”

Section: Micrornas As Determinants Of Breast Tissue Heterogeneitymentioning

confidence: 99%

MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors

Richard

Davey

Annuk

et al. 2021

Cancers

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The current clinical practice of breast tumor classification relies on the routine immunohistochemistry-based expression analysis of hormone receptors, which is inadequate in addressing breast tumor heterogeneity and drug resistance. MicroRNA expression profiling in tumor tissue and in the circulation is an efficient alternative to intrinsic molecular subtyping that enables precise molecular classification of breast tumor variants, the prediction of tumor progression, risk stratification and also identifies critical regulators of the tumor microenvironment. This review integrates data from protein, gene and miRNA expression studies to elaborate on a unique miRNA-based 10-subtype taxonomy, which we propose as the current gold standard to allow appropriate classification and separation of breast cancer into a targetable strategy for therapy.

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Section: Micrornas As Determinants Of Breast Tissue Heterogeneitymentioning

confidence: 99%

MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors

Richard

Davey

Annuk

et al. 2021

Cancers

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“…R package limma screened differentially expressed miRNAs. The selection criteria were |logFC| > 1 [ 21 ] and P < 0.05 [ 22 ], and the volcano map and heatmap were drawn. Then, we used Diana to predict miRNA and found that hsa-miR-520b was upregulated in breast cancer and interacted with PTEN .…”

Section: Methodsmentioning

confidence: 99%

The Effect of miR-520b on Macrophage Polarization and T Cell Immunity by Targeting PTEN in Breast Cancer

Zhu

Yuan

2021

Journal of Oncology

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Background. Breast cancer is the most common cancer in women. miR-520b had binding sites with PTEN through the bioinformatics prediction. But few studies have been conducted on miR-520b and PTEN in breast cancer. We aimed to explore the effect of miR-520b and PTEN on breast cancer and the mechanisms involved. Methods. Clinical samples of breast cancer were collected. Bioinformatics analysis was performed to screen the differentially expressed miRNAs. CD4 T cells and CD8 T cells were cocultured with MCF-7 cells in the Transwell system. Moreover, MCF-7 cells and M0 macrophage cocultured cell lines were constructed. qRT-PCR, IF, western blot, flow cytometry, and ELISA were performed to detect related factors expression. Starbase and dual-luciferase reporter assay verified the binding of miR-520b to PTEN. The tumor formation model was established to study miR-520b and PTEN effects in vivo. Results. The differentially expressed miR-520b was screened via miRNAs sequencing and cell verification. miR-520b expression was high, PTEN was low in tumor tissues, T cells and NK cells were inhibited, and macrophages were transformed into M2 type, promoting immune escape. In addition, miR-520b bound to PTEN. Then, splenic CD4 T cells and CD8 T cells were successfully sorted. During CD4 T cell differentiation to Th1 and Treg, Th1 was inhibited, and Treg was activated. We found the polarization of macrophages was related to breast cancer. The proportion of CD206 cells increased and CD68 cells decreased in the miR-520b mimics group compared with the mimic NC group. Compared with the inhibitor NC group, the proportion of CD206 cells decreased, and CD68 cells increased in the miR-520b inhibitor group. In vivo experiments showed that miR-520b inhibitor inhibited tumor growth and promoted PTEN expression. The proportion of CD3, CD4, CD8, NK1.1, CD4+IFNγ, and CD68 cells increased, while FOXP3 and CD206 cells decreased in the miR-520b inhibitor group compared with the inhibitor NC group. However, the proportion of CD3, CD4, CD8, NK1.1, CD4+IFNγ, and CD68 cells decreased, while FOXP3 and CD206 cells increased after the addition of siPTEN. Conclusions. miR-520b inhibited PTEN and aggravated breast tumors. miR-520b inhibitor enhanced CD4 and CD8 cell populations in the tumor immune microenvironment and inhibited tumor growth.

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“…To implement SPCA in the comparisons we apply the code of [15]. We also compare against two commonly applied feature selection methods, namely Forward Regression (FR) [3], and Expression Fold Change (EFC) [13]. FR is similar to a correlation based feature selection, commonly applied in cancer prediction [4], except in FR the features are chosen to have low linear dependence with one another, which is not a consideration in oblivious feature selection.…”

Section: Appendix a Methodsmentioning

confidence: 99%

“…The desired application of this work is miRNA expression analysis and cancer prediction. SOP was compared against five similar methods from the literature which use orthogonal projections and sparsity constraints, namely, OPLS [20], FR [3], EFC [13], SPCA [21], and DROP-D [8]. The technique was proven to be highly effective in reducing the dimension of miRNA expression data, and offered a highly competitive performance when compared to the methods of the literature.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Dimensionality reduction by sparse orthogonal projection with applications to miRNA expression analysis and cancer prediction

Webber

Elias

2021

Preprint

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BackgroundHigh dimensionality, i.e. p > n, is an inherent feature of machine learning. Fitting a classification model directly to p-dimensional data risks overfitting and a reduction in accuracy. Thus, dimensionality reduction is necessary to address overfitting and high dimensionality.ResultsWe present a novel dimensionality reduction method which uses sparse, orthogonal projections to discover linear separations in reduced dimension space. The technique is applied to miRNA expression analysis and cancer prediction. We use least squares fitting and orthogonality constraints to find a set of orthogonal directions which are highly correlated to the class labels. We also enforce L1 norm sparsity penalties, to prevent overfitting and remove the uninformative features from the model. Our method is shown to offer a highly competitive classification performance on synthetic examples and real miRNA expression data when compared to similar methods from the literature which use sparsity ideas and orthogonal projections.DiscussionA novel technique is introduced here, which uses sparse, orthogonal projections for dimensionality reduction. The approach is shown to be highly effective in reducing the dimension of miRNA expression data. The application of focus in this article is miRNA expression analysis and cancer predction. The technique may be generalizable, however, to other high dimensionality datasets.

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miRNA expression profiling of hereditary breast tumors from BRCA1- and BRCA2-germline mutation carriers in Brazil

Cited by 11 publications

References 55 publications

MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors

MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors

The Effect of miR-520b on Macrophage Polarization and T Cell Immunity by Targeting PTEN in Breast Cancer

Dimensionality reduction by sparse orthogonal projection with applications to miRNA expression analysis and cancer prediction

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