scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Hormone Receptor-status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

2020 ◽  
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

AbstractThe cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer (BRCA) patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from the cancer genome atlas TCGA BRCA and METABRIC datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal BRCA. We also confirmed that non-matching subgroup classification affected the survival of BRCA patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

scholarly journals Analysis of LAGEs Family Gene Signature and Prognostic Relevance in Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 726
Author(s):  
Hoang Dang Khoa Ta ◽  
Wan-Chun Tang ◽  
Nam Nhut Phan ◽  
Gangga Anuraga ◽  
Sz-Ying Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Family Members ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Breast cancer (BRCA) is one of the most complex diseases and involves several biological processes. Members of the L-antigen (LAGE) family participate in the development of various cancers, but their expressions and prognostic values in breast cancer remain to be clarified. High-throughput methods for exploring disease progression mechanisms might play a pivotal role in the improvement of novel therapeutics. Therefore, gene expression profiles and clinical data of LAGE family members were acquired from the cBioportal database, followed by verification using the Oncomine and The Cancer Genome Atlas (TCGA) databases. In addition, the Kaplan-Meier method was applied to explore correlations between expressions of LAGE family members and prognoses of breast cancer patients. MetaCore, GlueGo, and GluePedia were used to comprehensively study the transcript expression signatures of LAGEs and their co-expressed genes together with LAGE-related signal transduction pathways in BRCA. The result indicated that higher LAGE3 messenger (m)RNA expressions were observed in BRCA tissues than in normal tissues, and they were also associated with the stage of BRCA patients. Kaplan-Meier plots showed that overexpression of LAGE1, LAGE2A, LAGE2B, and LAGE3 were highly correlated to poor survival in most types of breast cancer. Significant associations of LAGE family genes were correlated with the cell cycle, focal adhesion, and extracellular matrix (ECM) receptor interactions as indicated by functional enrichment analyses. Collectively, LAGE family members’ gene expression levels were related to adverse clinicopathological factors and prognoses of BRCA patients; therefore, LAGEs have the potential to serve as prognosticators of BRCA patients.

scholarly journals Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongfang Jia ◽  
Cheng Chen ◽  
Chen Chen ◽  
Fangfang Chen ◽  
Ningrui Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Gene Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Diagnostic Methods ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Hub Genes

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein–protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

2006 ◽  
Vol 24 (28) ◽  
pp. 4594-4602 ◽  
Author(s):  
Skye H. Cheng ◽  
Cheng-Fang Horng ◽  
Mike West ◽  
Erich Huang ◽  
Jennifer Pittman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Eligible Patient ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients ◽  
Predictive Index ◽  
Validation Data ◽  
Data Set

Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

scholarly journals Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen

2008 ◽  
Vol 113 (2) ◽  
pp. 275-283 ◽  
Author(s):  
Marleen Kok ◽  
Sabine C. Linn ◽  
Ryan K. Van Laar ◽  
Maurice P. H. M. Jansen ◽  
Teun M. van den Berg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients

scholarly journals Gene Expression Profiling of Evening Fatigue in Women Undergoing Chemotherapy for Breast Cancer

2016 ◽  
Vol 18 (4) ◽  
pp. 370-385 ◽  
Author(s):  
Kord M. Kober ◽  
Laura Dunn ◽  
Judy Mastick ◽  
Bruce Cooper ◽  
Dale Langford ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sickness Behavior ◽  
Cell Types ◽  
Breast Cancer Patients ◽  
Oncology Patients ◽  
Public Data ◽  
Comorbidity Scores

Moderate-to-severe fatigue occurs in up to 94% of oncology patients undergoing active treatment. Current interventions for fatigue are not efficacious. A major impediment to the development of effective treatments is a lack of understanding of the fundamental mechanisms underlying fatigue. In the current study, differences in phenotypic characteristics and gene expression profiles were evaluated in a sample of breast cancer patients undergoing chemotherapy (CTX) who reported low ( n = 19) and high ( n = 25) levels of evening fatigue. Compared to the low group, patients in the high evening fatigue group reported lower functional status scores, higher comorbidity scores, and fewer prior cancer treatments. One gene was identified as upregulated and 11 as downregulated in the high evening fatigue group. Gene set analysis found 24 downregulated and 94 simultaneously up- and downregulated pathways between the two fatigue groups. Transcript origin analysis found that differential expression (DE) originated primarily from monocytes and dendritic cell types. Query of public data sources found 18 gene expression experiments with similar DE profiles. Our analyses revealed that inflammation, neurotransmitter regulation, and energy metabolism are likely mechanisms associated with evening fatigue severity; that CTX may contribute to fatigue seen in oncology patients; and that the patterns of gene expression may be shared with other models of fatigue (e.g., physical exercise and pathogen-induced sickness behavior). These results suggest that the mechanisms that underlie fatigue in oncology patients are multifactorial.

scholarly journals Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mark Burton ◽  
Mads Thomassen ◽  
Qihua Tan ◽  
Torben A. Kruse
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression Data ◽  
Classification Methods ◽  
Breast Cancer Patients ◽  
Microarray Gene Expression

Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

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