scholarly journals In Silico Evaluation of Natural Compounds for an Acidic Extracellular Environment in Human Breast Cancer

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2673
Author(s):  
YoungJoon Park ◽  
Jaekwang Jeong ◽  
Shin Seong ◽  
Wonnam Kim
Keyword(s):  
Breast Cancer ◽  
Target Genes ◽  
Information Gain ◽  
Therapeutic Potential ◽  
Expression Profiles ◽  
Survival Rates ◽  
Gene Expression Profiles ◽  
Natural Compounds ◽  
The Cancer Genome Atlas ◽  
Potential Candidate

The survival rates for breast cancer (BC) have improved in recent years, but resistance, metastasis, and recurrence still remain major therapeutic challenges for BC. The acidic tumor microenvironment (TME) has attracted attention because of its association with tumorigenesis, metastasis, drug resistance, and immune surveillance. In this study, we evaluated natural compounds from traditional herbal medicine used to treat cancer that selectively target genes regulated by extracellular acidosis. We integrated four transcriptomic data including BC prognostic data from The Cancer Genome Atlas database, gene expression profiles of MCF-7 cells treated with 102 natural compounds, patterns of gene profiles by acidic condition, and single-cell RNA-sequencing from BC patient samples. Bruceine D (BD) was predicted as having the highest therapeutic potential, having an information gain (IG) score of 0.24, to regulate reprogrammed genes driven by acidosis affecting the survival of BC patients. BD showed the highest IG on EMT (IG score: 0.11) and invasion (IG score: 0.1) compared to the other phenotypes with the CancerSEA database. BD also demonstrated therapeutic potential by interfering with the tumor cell–TME interactions by reducing the amyloid beta precursor protein and CD44 expression. Therefore, BD is a potential candidate to target the acidic TME induced metastatic process in BC.

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.

scholarly journals Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xue Wang ◽  
Chundi Gao ◽  
Fubin Feng ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Competing Endogenous Rnas ◽  
Tcga Dataset

Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. Conclusion. This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.

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 Identification of Novel Prognostic Risk Signature of Breast Cancer Based on Ferroptosis-Related Genes

2022 ◽  
Author(s):  
Nan Wang ◽  
Yuanting Gu ◽  
Lin Li ◽  
Jiangrui Chi ◽  
Xinwei Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Risk ◽  
Clinical Characteristics ◽  
Expression Profiles ◽  
Tumor Therapy ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Biological Behavior ◽  
Prognostic Indicators ◽  
The Relationship

Abstract Ferroptosis is a non-small molecule-induced form of tumor cell apoptosis, which has been shown to regulate the biological behavior of tumors. Therefore, genes controlling ferroptosis may be promising candidate biomarkers for tumor therapy. In this study, we investigate the function of genes associated with ferroptosis in breast cancer (BC) and systematically evaluate the relationship between ferroptosis-related gene expression profiles and prognosis in BC patients based on the Cancer Genome Atlas RNA-sequencing dataset (TCGA). By using the non-negative matrix factorization clustering method, 1,203 breast cancer samples were clustered into two clearly divided subgroups based on the expression of 237 ferroptosis-related genes. The least absolute shrinkage and selection operator (LASSO) was used to develop risk profiles for five genes, and then these five genes were verified by the polymerase chain reaction (PCR). The relationship between genetic risk characteristics and clinical characteristics of BC is described. The results show that the genetic risk signature associated with clinical characteristics can be used as independent prognostic indicators for BC patients.

Regulation of the Effect of Physical Activity Through MicroRNAs in Breast Cancer

2021 ◽  
Author(s):  
Bok Sil Hong
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Cancer Progression ◽  
Target Genes ◽  
Expression Profiles ◽  
Breast Cancer Progression ◽  
The Cancer Genome Atlas ◽  
Circulating Micrornas ◽  
Tumor Stages ◽  
Cancer Genome Atlas

AbstractPhysical activity and exercise can induce beneficial molecular and biological regulations that have been associated with an incidence of various diseases, including breast cancer. Recent studies demonstrated that the potential links between physical activity-induced circulating microRNAs (miRNAs) and cancer risk and progression. Here, we investigated whether altered miRNAs by exercise could influence breast cancer progression. After primary searching in PubMed and reviewing the full-text papers, candidate miRNAs altered by exercise in breast cancer were identified. Analysis of expression profiles and clinical outcomes of altered miRNAs using The Cancer Genome Atlas datasets showed altered miRNAs expressions were significantly associated with the patient's prognosis, whereas prognostic values of each miRNA varied in different stages and subtypes. In addition, altered miRNAs profiles regulated various target genes and key signaling pathways in tumorigenesis, including pathways in cancer and the PI3K-Akt signaling pathway; however, miRNAs regulated the expression of target genes differently according to tumor stages and subtypes. These results indicate that circulating miRNAs are promising noninvasive stable biomarkers for early detection, diagnosis, prognosis, and monitoring the response to clinical therapies of breast cancer. Moreover, stages and subtype-stratified approaches for breast cancer progression would be needed to evaluate the prognostic value of miRNAs for biomarkers and therapeutic targets.

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.

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