scholarly journals The Integrative Method Based on Module-Network for Identifying Driver Genes in Cancer Subtypes

2017 ◽  
Author(s):  
Xinguo Lu ◽  
Xing Li ◽  
Xin Qian ◽  
Qiumai Miao ◽  
Shaoliang Peng
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Breast Cancer Subtype ◽  
Enrichment Analysis ◽  
Potential Candidate ◽  
Statistical Machine Learning ◽  
Driver Genes ◽  
Module Network ◽  
Cancer Subtypes ◽  
Integrative Method

With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

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.

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.

scholarly journals The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes

Molecules ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 183 ◽  
Author(s):  
Xinguo Lu ◽  
Xing Li ◽  
Ping Liu ◽  
Xin Qian ◽  
Qiumai Miao ◽  
...  
Keyword(s):  
Driver Genes ◽  
Module Network ◽  
Cancer Subtypes ◽  
Integrative Method

Gene expression associated with lymphovascular invasion and genomic risk in early-stage breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 559-559
Author(s):  
Nina D'Abreo ◽  
Abhinav Rohatgi ◽  
Douglas Kanter Marks ◽  
Heather Kling ◽  
Josien Haan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Adhesion ◽  
Clinical Outcomes ◽  
Lymphovascular Invasion ◽  
Group Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Tumor Type ◽  
Luminal B

559 Background: Lymphovascular invasion (LVI), the passage of carcinoma cells through lymphatic and blood vessels, is an important early step in metastasis; however, LVI is excluded from most breast cancer (BC) clinical risk assessments. Previous studies assessed the prognostic value of LVI to estimate clinical outcomes. To gain understanding of the molecular basis of LVI, we evaluated differentially expressed genes (DEGs) between tumors with LVI versus those without LVI, stratified by the 70-gene signature (MammaPrint/MP) and 80-gene molecular subtyping signature (BluePrint/BP). Methods: The prospective, observational FLEX Study (NCT03053193) includes stage I-III BC patients who receive MP/BP testing and consent to full transcriptome and clinical data collection. Patients with LVI (n=581) and without LVI (n=600, randomly selected), enrolled from 2017 to present, were included. LVI was assessed by local pathology laboratories. Differential gene expression analysis of 44k Agilent microarray data was performed with R limma package. DEGs were compared within all samples, BP Luminal subtype, MP risk groups (Low Risk [LR]/Luminal A and High Risk [HR]/Luminal B), and by lymph node (LN) status. DEGs with FDR<0.05 were considered significant. Results: Of tumors with LVI (LVI+), 66% were MP HR; notably, 51% of tumors without LVI (LVI-) were MP HR. LVI was associated with larger T stage, LN involvement, high grade, negative ER status by IHC, and younger patient age (LVI+ vs. LVI-, p<0.05 for all comparisons). Patient ethnicity, obesity, and tumor type did not differ by LVI status; however, prevalence of type 2 diabetes trended higher in patients with LVI+ HR tumors (21%), compared with LVI- HR (15%, p=0.09) and LVI+ LR (11%, p=0.004). There were significant transcriptomic differences between LVI+ and LVI, with most DEGs evident in the Luminal B subset. DEGs in LVI+, LN-negative (LN-) tumors overlapped substantially with the overall Luminal group analysis. Functional enrichment analysis showed dysregulation of cell cycle, extracellular matrix (ECM) organization, cell adhesion, and cytokine receptor pathways. Gene sets related to insulin growth factor pathways were also enriched in LVI+ tumors. Conclusions: DEGs associated with LVI were primarily found in MP HR Luminal, LN-negative tumors; enrichment analysis suggested dysregulation of ECM organization and cell adhesion pathways, consistent with previous reports. DEGs were not associated with LVI presence in LN+ tumors, suggesting that LVI assessment may be less relevant in LN+ breast cancer. Future studies will assess clinical outcomes, as well as LVI-associated gene expression in BP Basal- and HER2-type tumors. However, the current analysis indicates few DEGs in LVI+ MP LR tumors; thus, the potential prognostic information gained from LVI-associated gene expression is likely already captured by the MP and BP signatures. Clinical trial information: NCT03053193.

scholarly journals Berberine Impairs the Survival of Triple Negative Breast Cancer Cells: Cellular and Molecular Analyses

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 506 ◽  
Author(s):  
Lamyae El Khalki ◽  
Virginie Maire ◽  
Thierry Dubois ◽  
Abdelmajid Zyad
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Potential Candidate ◽  
Tnbc Cell ◽  
Normal Human Breast ◽  
Good Potential ◽  
Breast Cells

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype. Non-available targeted therapy for TNBC represents its biggest treatment challenge. Thus, finding new promising effective drugs is urgently needed. In the present study, we investigated how berberine, a natural isoquinoline, impairs the survival of TNBC cells in both cellular and molecular levels. Our experimental model was based on the use of eight TNBC cell lines: MDA-MB-468, MDA-MB-231, HCC70, HCC38, HCC1937, HCC1143, BT-20, and BT-549. Berberine was cytotoxic against all treated TNBC cell lines. The most sensitive cell lines were HCC70 (IC50 = 0.19 µM), BT-20 (IC50 = 0.23 µM) and MDA-MB-468 (IC50 = 0.48 µM). Using flow cytometry techniques, berberine, at 0.5 and 1 µM for 120 and 144 h, not only induced cell cycle arrest, at G1 and/or G2/M phases, but it also triggered significant apoptosis. At the molecular level, these results are consistent with the expression of their related proteins using Western blot assays. Interestingly, while berberine was cytotoxic against TNBC cells, it had no effect on the viability of normal human breast cells MCF10A cultured in a 3D matrigel model. These results suggest that berberine may be a good potential candidate for TNBC drug development.

Identification of differential gene expression related to epirubicin-induced cardiomyopathy in breast cancer patients

2019 ◽  
Vol 39 (4) ◽  
pp. 393-401 ◽  
Author(s):  
J Peng ◽  
Z Wang ◽  
Y Li ◽  
D Lv ◽  
X Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Global Gene Expression ◽  
Pathway Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Chlorophyll Metabolism ◽  
Pathway Enrichment ◽  
Before And After

Background: Epirubicin is a potent chemotherapeutic agent for the treatment of breast cancer. However, it may lead to cardiotoxicity and cardiomyopathy, and no reliable biomarker was available for the early prediction of epirubicin-induced cardiomyopathy. Methods: Global gene expression changes of peripheral blood cells were studied using high-throughput RNA sequencing in three pair-matched breast cancer patients (patients who developed symptomatic cardiomyopathy paired with patients who did not) before and after the full session of epirubicin-based chemotherapy. Functional analysis was conducted using gene ontology and pathway enrichment analysis. Results: We identified 13 significantly differentially expressed genes between patients who developed symptomatic epirubicin-induced cardiomyopathy and their paired control who did not. Among them, the upregulated Bcl-associated X protein was related to “apoptosis,” while the downregulated 5′-aminolevulinate synthase 2 (ALAS2) was related to both “glycine, serine, and threonine metabolism” and “porphyrin and chlorophyll metabolism” in pathway enrichment analysis. Conclusions: ALAS2 and the metabolic pathways which were involved may play an important role in the development of epirubicin-induced cardiomyopathy. ALAS2 may be useful as an early biomarker for epirubicin-induced cardiotoxicity detection.

scholarly journals Metabolic Syndrome and the Risk of Breast Cancer and Subtypes by Race, Menopause and BMI

Cancers ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 299 ◽  
Author(s):  
Daniel Dibaba ◽  
Dejana Braithwaite ◽  
Tomi Akinyemiju
Keyword(s):  
Breast Cancer ◽  
Metabolic Syndrome ◽  
Invasive Breast Cancer ◽  
Ductal Carcinoma ◽  
Breast Cancer Subtype ◽  
Normal Weight ◽  
Health Study ◽  
Cancer Subtypes ◽  
Increased Risk ◽  
Cox Proportional Hazard Regression

The objective of this study was to investigate the association of metabolic syndrome (MetS) with the risk of invasive breast cancer and molecular subtypes across race, menopause, and body mass index (BMI) groups. We examined the association of metabolic syndrome and its components with risk of invasive breast cancer among 94,555 female participants of the National Institute of Health-American Association of Retired Persons (NIH-AARP) Diet and Health Study, accounting for ductal carcinoma in situ as a competing risk. Cox proportional hazard regression with the Fine and Gray method was used to generate hazard ratios (HR) and 95% confidence intervals (CI) adjusting for baseline sociodemographic, behavioral, and clinical covariates. During a mean follow-up of 14 years, 5380 (5.7%) women developed breast cancer. Overall, MetS at baseline was associated with a 13% increased risk of breast cancer compared to women without MetS (HR: 1.13, 95% CI: 1.00, 1.27); similar estimates were obtained among postmenopausal women (HR: 1.14, 95% CI: 1.01, 1.29). MetS was associated with a slight but non-significantly increased risk of breast cancer among those with both normal weight and overweight/obesity, and those with estrogen receptor positive breast cancer subtype. In the NIH-AARP cohort, MetS was associated with an increased risk of breast cancer. Further studies are needed to definitively evaluate the association of MetS with triple negative breast cancer subtypes across all levels of BMI.

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