scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8049 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Breast Cancer Subtype ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Cancer Subtype ◽  
Gene Mrna

The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer

2019 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Untranslated Regions ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Free Energy Of Binding ◽  
Triple Negative Subtype

Distinct sets of candidate genes control the development of breast cancer subtypes. The expression of many genes is regulated by the binding of their mRNAs with miRNAs. The prediction of miRNA associations and target genes is essential in studying of breast cancer. The MirTarget program defines the following features of binding miRNA to mRNA: the start of the initiation of miRNA binding to mRNA; the localization of miRNA binding sites in 5'-untranslated regions (5'UTR), coding domain sequences (CDS) and 3'-untranslated regions (3'UTR); the free energy of binding of all miRNA nucleotides with mRNA; the schemes of interactions of all miRNAs nucleotides with mRNAs. The mRNAs of many genes have clusters (miRNA binding sites with overlapping nucleotide sequences) located in 5'UTR, CDS, or 3'UTR. There are clusters in 5'UTR of mRNA EPOR, MAZ and NISCH candidate genes of HER2 subtype. There are four clusters in CDS of mRNA MAZ gene, and in 3'UTR of mRNA BRCA2 and CDK6 genes. Candidate genes of triple-negative subtype are targets for multiple miRNAs. In 5'UTR of mRNA СBL gene, there are 11 sites; the mRNA for MMP2 gene contains five sites; the mRNA of RAB5A gene contains two clusters each of three sites. In 3'UTR of mRNA SFN gene, there are two clusters, each of three sites, and one cluster of 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in 5'UTR of mRNA FOXA1 gene and mRNA HMGA2 gene contains 15 sites. There are clusters of five sites in CDS of mRNA ITGB1 gene and five sites in 3'UTR of mRNA SOX4 genes. Clusters of eight sites and ten sites are identified in 3'UTR of mRNA SMAD3 and TGFB1 genes, respectively. The organization of miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in 5'UTR, CDS and 3'UTRs. This overlapping of miRNA binding sites creates a competition among miRNAs for the binding site. From 6,272 studied miRNAs only 29 miRNAs from miRBase and 88 novel miRNAs have binding sites in clusters of mRNA target genes of breast cancer.

scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer

2019 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Untranslated Regions ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Free Energy Of Binding ◽  
Triple Negative Subtype

Distinct sets of candidate genes control the development of breast cancer subtypes. The expression of many genes is regulated by the binding of their mRNAs with miRNAs. The prediction of miRNA associations and target genes is essential in studying of breast cancer. The MirTarget program defines the following features of binding miRNA to mRNA: the start of the initiation of miRNA binding to mRNA; the localization of miRNA binding sites in 5'-untranslated regions (5'UTR), coding domain sequences (CDS) and 3'-untranslated regions (3'UTR); the free energy of binding of all miRNA nucleotides with mRNA; the schemes of interactions of all miRNAs nucleotides with mRNAs. The mRNAs of many genes have clusters (miRNA binding sites with overlapping nucleotide sequences) located in 5'UTR, CDS, or 3'UTR. There are clusters in 5'UTR of mRNA EPOR, MAZ and NISCH candidate genes of HER2 subtype. There are four clusters in CDS of mRNA MAZ gene, and in 3'UTR of mRNA BRCA2 and CDK6 genes. Candidate genes of triple-negative subtype are targets for multiple miRNAs. In 5'UTR of mRNA СBL gene, there are 11 sites; the mRNA for MMP2 gene contains five sites; the mRNA of RAB5A gene contains two clusters each of three sites. In 3'UTR of mRNA SFN gene, there are two clusters, each of three sites, and one cluster of 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in 5'UTR of mRNA FOXA1 gene and mRNA HMGA2 gene contains 15 sites. There are clusters of five sites in CDS of mRNA ITGB1 gene and five sites in 3'UTR of mRNA SOX4 genes. Clusters of eight sites and ten sites are identified in 3'UTR of mRNA SMAD3 and TGFB1 genes, respectively. The organization of miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in 5'UTR, CDS and 3'UTRs. This overlapping of miRNA binding sites creates a competition among miRNAs for the binding site. From 6,272 studied miRNAs only 29 miRNAs from miRBase and 88 novel miRNAs have binding sites in clusters of mRNA target genes of breast cancer.

Adjuvant Chemotherapy: Which Patient? What Regimen?

2013 ◽  
pp. 3-8 ◽  
Author(s):  
Natalie Turner ◽  
Laura Biganzoli ◽  
Luca Malorni ◽  
Ilenia Migliaccio ◽  
Erica Moretti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Breast Cancer Subtype ◽  
Tumor Biology ◽  
Patient Characteristics ◽  
Tumor Stage ◽  
Luminal A ◽  
Genomic Analyses ◽  
Cancer Subtype ◽  
Clinicopathologic Factors

In the past, treatment decisions regarding adjuvant chemotherapy in early breast cancer (EBC) were made solely based on clinicopathologic factors. However, with increased awareness of the importance of underlying tumor biology, we are now able to use genomic analyses to determine molecular breast cancer subtype and thus identify patients with tumors that are chemotherapy resistant and unlikely to benefit from the addition of chemotherapy. Although genomics has allowed some patients to avoid chemotherapy—specifically those with luminal A–like breast cancer—these assays do not indicate which regimen is most appropriate. For this, consideration must be given to the combination of underlying tumor biology, tumor stage, and patient characteristics, such as age and tolerability of side effects.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

scholarly journals Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms

2016 ◽  
Vol 14 (05) ◽  
pp. 1644002 ◽  
Author(s):  
Jinwoo Park ◽  
Benjamin Hur ◽  
Sungmin Rhee ◽  
Sangsoo Lim ◽  
Min-Su Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Network ◽  
Cancer Biology ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
Information Theoretic ◽  
Cancer Subtypes ◽  
Network Mining ◽  
Subtype Classification ◽  
Cancer Subtype

A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed. We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.

scholarly journals Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Nicole J. Chew ◽  
Terry C. C. Lim Kam Sian ◽  
Elizabeth V. Nguyen ◽  
Sung-Young Shin ◽  
Jessica Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Therapeutic Target ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
Cancer Tissue ◽  
Breast Cancers ◽  
Luminal A ◽  
Tissue Samples ◽  
Cancer Subtypes ◽  
Luminal B

Abstract Background Particular breast cancer subtypes pose a clinical challenge due to limited targeted therapeutic options and/or poor responses to the existing targeted therapies. While cell lines provide useful pre-clinical models, patient-derived xenografts (PDX) and organoids (PDO) provide significant advantages, including maintenance of genetic and phenotypic heterogeneity, 3D architecture and for PDX, tumor–stroma interactions. In this study, we applied an integrated multi-omic approach across panels of breast cancer PDXs and PDOs in order to identify candidate therapeutic targets, with a major focus on specific FGFRs. Methods MS-based phosphoproteomics, RNAseq, WES and Western blotting were used to characterize aberrantly activated protein kinases and effects of specific FGFR inhibitors. PDX and PDO were treated with the selective tyrosine kinase inhibitors AZD4547 (FGFR1-3) and BLU9931 (FGFR4). FGFR4 expression in cancer tissue samples and PDOs was assessed by immunohistochemistry. METABRIC and TCGA datasets were interrogated to identify specific FGFR alterations and their association with breast cancer subtype and patient survival. Results Phosphoproteomic profiling across 18 triple-negative breast cancers (TNBC) and 1 luminal B PDX revealed considerable heterogeneity in kinase activation, but 1/3 of PDX exhibited enhanced phosphorylation of FGFR1, FGFR2 or FGFR4. One TNBC PDX with high FGFR2 activation was exquisitely sensitive to AZD4547. Integrated ‘omic analysis revealed a novel FGFR2-SKI fusion that comprised the majority of FGFR2 joined to the C-terminal region of SKI containing the coiled-coil domains. High FGFR4 phosphorylation characterized a luminal B PDX model and treatment with BLU9931 significantly decreased tumor growth. Phosphoproteomic and transcriptomic analyses confirmed on-target action of the two anti-FGFR drugs and also revealed novel effects on the spliceosome, metabolism and extracellular matrix (AZD4547) and RIG-I-like and NOD-like receptor signaling (BLU9931). Interrogation of public datasets revealed FGFR2 amplification, fusion or mutation in TNBC and other breast cancer subtypes, while FGFR4 overexpression and amplification occurred in all breast cancer subtypes and were associated with poor prognosis. Characterization of a PDO panel identified a luminal A PDO with high FGFR4 expression that was sensitive to BLU9931 treatment, further highlighting FGFR4 as a potential therapeutic target. Conclusions This work highlights how patient-derived models of human breast cancer provide powerful platforms for therapeutic target identification and analysis of drug action, and also the potential of specific FGFRs, including FGFR4, as targets for precision treatment.

scholarly journals Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

2021 ◽  
Vol 12 ◽  
Author(s):  
Diana García-Cortés ◽  
Enrique Hernández-Lemus ◽  
Jesús Espinal-Enríquez
Keyword(s):  
Breast Cancer ◽  
Network Topology ◽  
Binding Sites ◽  
Target Genes ◽  
Molecular Subtype ◽  
Differential Expression Analysis ◽  
Ctcf Binding ◽  
Luminal A ◽  
Long Distance ◽  
Transcriptomic Level

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Towards cancer mega-cohorts: A novel homogenization algorithm applied to diverse breast cancer RNA-Seq datasets.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13507-e13507
Author(s):  
Talal Ahmed ◽  
Mark Carty ◽  
Stephane Wenric ◽  
Raphael Pelossof
Keyword(s):  
Breast Cancer ◽  
Molecular Subtype ◽  
Breast Cancer Subtype ◽  
Model Performance ◽  
Test Results ◽  
Rna Seq ◽  
Luminal A ◽  
Test Set ◽  
Luminal B ◽  
Cancer Subtype

e13507 Background: Recent advances in transcriptomics have resulted in the emergence of several publicly available breast cancer RNA-Seq datasets, such as TCGA, SCAN-B, and METABRIC. However, molecular predictors cannot be applied across datasets without the correction of batch differences. In this study, we demonstrate a homogenization algorithm that allows the transfer of molecular subtype predictors from one RNA-Seq cohort to another. The algorithm only uses cohort-level RNA-Seq summary statistics, and therefore, does not require joint normalization of both datasets nor the transfer of patient information. Using this approach, we transferred a breast cancer subtype (Luminal A, Luminal B, HER2+, Basal) predictor trained on SCAN-B data to accurately predict subtypes from TCGA. Methods: First, we randomly split the TCGA cohort (n = 481 Luminal A, n = 189 Luminal B, n = 73 Her2+, n = 168 Basal) into two sets: TCGA-train and held-out TCGA-test (n = 455 and n = 456, respectively). Second, the SCAN-B cohort (n = 837) was homogenized with the TCGA-train set. Third, a molecular subtype predictor, based on a logistic regression model, was trained on homogenized SCAN-B RNA-Seq samples and used to predict the subtypes of TCGA-test RNA-Seq samples. For baseline comparison, a similar predictor trained on the non-homogenized SCAN-B cohort was tested on the TCGA-test set. The experimental framework was iterated 250 times. Reported P-values reflect a paired one-sided t-test. Results: To quantify model performance, we measured the average F1 score for each tumor subtype prediction from the held-out TCGA test set with and without cohort homogenization. The average F1 scores with vs. without homogenization were: Luminal A, 0.88 vs. 0.85 ( P< 1e-69); Luminal B, 0.74 vs. 0.51 ( P< 1e-183); Her2+, 0.73 vs. 0.53 ( P< 1e-99); Basal, 0.98 vs. 0.97 ( P< 1e-53). Overall, homogenization significantly outperformed no homogenization. Conclusions: We developed a novel homogenization algorithm that accurately transfers subtype predictors across diverse, independent breast cancer cohorts.

Hubness weighted svm ensemble for prediction of breast cancer subtypes

2021 ◽  
pp. 1-14
Author(s):  
S. Raja Sree ◽  
A. Kunthavai
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Support Vector Machine ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
High Dimensional ◽  
Support Vector ◽  
Gene Expressions ◽  
Cancer Subtypes ◽  
Cancer Subtype

BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions. OBJECTIVE: Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression. METHODS: For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction. RESULTS: The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods. CONCLUSIONS: Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.

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