Glycan-related gene expression signatures in breast cancer subtypes; relation to survival

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.

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Logic-based Analysis of Gene Expression Data Predicts Pathway Crosstalk between TNF, TGFB1 and EGF in Basal-like Breast Cancer

10.1101/614933 ◽

2019 ◽

Author(s):

Kyuri Jo ◽

Beatriz Santos Buitrago ◽

Minsu Kim ◽

Sungmin Rhee ◽

Carolyn Talcott ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Signaling Pathways ◽

Expression Profiles ◽

Formal System ◽

Gene Expression Profiles ◽

Breast Cancer Subtypes ◽

Post Translational Modifications ◽

Cancer Subtypes ◽

Pathway Crosstalk

AbstractFor breast cancer, clinically important subtypes are well characterised at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterise biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences.We present a logic-based approach to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Proposed method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential pathway crosstalk via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes.AvailabilityAnalysis result is available at http://epigenomics.snu.ac.kr/PL/

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Identification of Epigenetic Biomarkers with the use of Gene Expression and DNA Methylation for Breast Cancer Subtypes

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) ◽

10.1109/tencon.2019.8929636 ◽

2019 ◽

Author(s):

Indrajit Saha ◽

Somnath Rakshit ◽

Michal Wlasnowolski ◽

Dariusz Plewczynski

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Dna Methylation ◽

Breast Cancer Subtypes ◽

Cancer Subtypes ◽

Epigenetic Biomarkers

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Gene expression in 16q is associated with survival and differs between Sørlie breast cancer subtypes

Genes Chromosomes and Cancer ◽

10.1002/gcc.20392 ◽

2007 ◽

Vol 46 (1) ◽

pp. 87-97 ◽

Cited By ~ 12

Author(s):

Kristian Wennmalm ◽

Stefano Calza ◽

Alexander Ploner ◽

Per Hall ◽

Judith Bjöhle ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Breast Cancer Subtypes ◽

Cancer Subtypes

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Hubness weighted svm ensemble for prediction of breast cancer subtypes

Technology and Health Care ◽

10.3233/thc-212825 ◽

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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