scholarly journals Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8403 ◽  
Author(s):  
Yue Li ◽  
Xiaoyan Zhou ◽  
Jiali Liu ◽  
Yang Yin ◽  
Xiaohong Yuan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Diagnostic Value ◽  
Value Assessment ◽  
Breast Cancer Patients ◽  
Wild Type ◽  
Ppi Network ◽  
Normal Tissues

Background BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein–protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. Results The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. Conclusion Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.

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.

Targeting PSG1 to enhance chemotherapeutic efficacy: new application for anti-coagulant the dicumarol

2016 ◽  
Vol 130 (24) ◽  
pp. 2267-2276 ◽  
Author(s):  
Dong-xu He ◽  
Feng Gu ◽  
Jian Wu ◽  
Xiao-Ting Gu ◽  
Chun-Xiao Lu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transforming Growth Factor Β ◽  
Clinical Samples ◽  
Breast Cancer Patients

Chemotherapeutic response is critical for the successful treatment and good prognosis in cancer patients. In this study, we analysed the gene expression profiles of preoperative samples from oestrogen receptor (ER)-negative breast cancer patients with different responses to taxane-anthracycline-based (TA-based) chemotherapy, and identified a group of genes that was predictive. Pregnancy specific beta-1-glycoprotein 1 (PSG1) played a central role within signalling pathways of these genes. Inhibiting PSG1 can effectively reduce chemoresistance via a transforming growth factor-β (TGF-β)-related pathway in ER-negative breast cancer cells. Drug screening then identified dicumarol (DCM) to target the PSG1 and inhibit chemoresistance to TA-based chemotherapy in vitro, in vivo, and in clinical samples. Taken together, this study highlights PSG1 as an important mediator of chemoresistance, whose effect could be diminished by DCM.

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 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 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.

Gene expression profiles of circulating tumor cells in metastatic breast cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10504-10504
Author(s):  
Bianca Mostert ◽  
Anieta M Sieuwerts ◽  
Jaco Kraan ◽  
Joan Bolt-de Vries ◽  
Dieter Peeters ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Gene Profile

10504 Background: A circulating tumor cell (CTC) count is an established prognostic factor in metastatic breast cancer. Besides enumeration, CTC characterization promises to further improve outcome prediction and treatment guidance. We have previously shown the feasibility of measuring the expression of a panel of 96 clinically relevant genes in CTCs in a leukocyte background, and in the current study, we determined the prognostic value of CTC gene expression profiling in metastatic breast cancer. Methods: CTCs were isolated and enumerated from blood of 130 metastatic breast cancer patients prior to start of first-line systemic, endocrine or chemotherapeutic, therapy. Of these, 103 were evaluable for mRNA gene expression levels measured by quantitative RT-PCR in relation to time to treatment switch (TTS). Separate prognostic CTC gene profiles were generated by leave-one-out cross validation for all patients and for patients with ≥5 CTCs per 7.5 mL blood, and cut-offs were chosen to ensure optimal prediction of patients who might benefit from an early therapy switch. Results: In the total cohort, of whom 56% received chemotherapeutic and 44% endocrine therapy, baseline CTC count (≥5 versus <5 CTCs/7.5 mL blood) predicted for TTS (Hazard Ratio (HR) 2.92 [95% Confidence Interval (CI) 1.71 – 4.95] P <0.0001). A 16-gene CTC profile for all patients and a separate 9-gene CTC profile applicable for patients with ≥5 CTCs were identified, which distinguished those patients with TTS or death within 9 months from those with a more favorable outcome. Test performance for both profiles was favorable; the 16-gene profile had 90% sensitivity, 38% specificity, 50% positive predictive value (PPV) and 85% negative predictive value (NPV), and the 9-gene profile performed slightly better at 92% sensitivity, 52% specificity, 66% PPV and 87% NPV. In multivariate Cox regression analysis, the 16-gene profile was the only factor independently associated with TTS (HR 3.15 [95%CI 1.35 – 7.33] P 0.008). Conclusions: Two CTC gene expression profiles were discovered, which provide prognostic value in metastatic breast cancer patients. This study further underscores the potential of molecular characterization of CTCs.

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