scholarly journals A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription FactorMYBacross Molecular Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
King-Jen Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Essential Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Prognostic Features ◽  
Bioinformatic Tools ◽  
Prognostic Feature ◽  
Relevant Gene

Background. MYBis predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features ofMYBvia a supervised network analysis.Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included.Results. ARNT2is predicted to be the essential gene partner ofMYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature ofMYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members ofMYBare elevated to promote poor prognosis when both levels ofMYBandARNT2decline. BothMYBL1andMYBL2may partially decrease the tumor suppressive activities that are predicted to be up-regulated byMYBandARNT2.Conclusions. The major prognostic feature ofMYBis predicted to be determined by theMYBsubnetwork (41 probes) that is relevant across subtypes.

scholarly journals The Major Prognostic Features of Nuclear ReceptorNR5A2in Infiltrating Ductal Breast Carcinomas

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Li-Yun Chang ◽  
Li-Yu D. Liu ◽  
Don A. Roth ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Transcriptional Networks ◽  
Cancer Subtypes ◽  
Prognostic Features

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptorsNR5A1andNR5A2based upon their associated transcriptional networks.Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology.Results.NR5A2expression is a negative factor in breast cancer prognosis in both ER(−) and ER(−)/ER(+) mixed cohorts. The clinical and cohort significance ofNR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways.NR5A2signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression ofNR5A1predicts favorable prognosis when concurrentNR5A2expression is low. A favorable signature of eight transcription factors mediated byNR5A1was also identified.Conclusions. Correlation of poor prognosis andNR5A2activity is identified byNR5A2-mediated 15-gene signature.NR5A2may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(−) breast tumors. The favorable prognostic feature ofNR5A1is predicted byNR5A1-mediated 8-gene signature.

scholarly journals Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Jianing Tang ◽  
Yongwen Luo ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Proliferation ◽  
Network Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Validation Dataset ◽  
Pcr Analysis ◽  
Normal Tissues

Abstract Background Breast cancer is the mostly diagnosed malignance in female worldwide. However, the mechanisms of its pathogenesis remain largely unknown. Methods In this study, we used weighted gene co-expression network analysis (WGCNA) to identify novel biomarkers associated with the prognosis of breast cancer. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Results A total of 5 modules were identified via the average linkage hierarchical clustering. And a module significantly with the pathological grade was screened out. 33 genes with high connectivity in the clinically significant module were identified as hub genes. Among them, CASC5 and RAD51 were negatively associated with the overall survival and disease-specific survival. Similar results were observed in the validation dataset. Protein levels of CACS5 and RAD51 were also significantly higher in tumor tissues compared with normal tissues based on the analysis of the Human Protein Atlas. Convincingly, qRT-PCR analysis of breast cancer tissues and matched paracancerous tissue demonstrated that CACS5 and RAD51 were significantly upregulated in in breast cancer compared to paracancerous tissues. Further cell proliferation assay indicated that CACS5 and RAD51 depletion decreased cell proliferation capability. Conclusion In conclusion, our findings suggested that CASC5 and RAD51 could serve as biomarkers related to the prognosis of breast cancer and may be helpful for revealing pathogenic mechanism and developing further research.

Network Analysis of Gene Expression Profiles in Breast Cancer Cell Lines

2011 ◽  
pp. 233-242
Author(s):  
T Dewey ◽  
Katie Streicher ◽  
Stephen Ethier ◽  
T Dewey ◽  
Katie Streicher ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Network Analysis ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines

scholarly journals Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Dongzhu Peng ◽  
Bin Gu ◽  
Liming Ruan ◽  
Xingguo Zhang ◽  
Peng Shu
Keyword(s):  
Gastric Cancer ◽  
Network Analysis ◽  
Plasma Cells ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Integrated Analysis ◽  
Prognostic Signature

Background. Gastric cancer (GC) has been divided into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop a prognostic signature by integrating the immune system and molecular modalities involved in the mesenchymal subtype. Methods. The gene expression profiles collected from 6 public datasets were applied to this study, including 1,221 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for GC (IPSGC). Results. We identified six immune genes as key factors of the mesenchymal subtype and established the IPSGC. The IPSGC can significantly divide patients into high- and low-risk groups in terms of overall survival (OS) and relapse-free survival (RFS) in discovery (OS: P<0.001) and 5 independent validation sets (OS range: P=0.05 to P<0.001; RFS range: P=0.03 to P<0.001). Further, in multivariate analysis, the IPSGC remained an independent predictor of prognosis and performed better efficiency compared to clinical characteristics. Moreover, macrophage M2 was significantly enriched in the high-risk group, while plasma cells were enriched in the low-risk group. Conclusions. We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of GC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

scholarly journals A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Erkhembayar Jadamba ◽  
Miyoung Shin
Keyword(s):  
Breast Cancer ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
A Priori ◽  
Gene Expression Profiles ◽  
Biological Data ◽  
Genomic Knowledge ◽  
High Level ◽  
Systematic Framework ◽  
Phenotype Expression

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

scholarly journals An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Nicoletta Dessì ◽  
Barbara Pes
Keyword(s):  
Microarray Data ◽  
Selection Criteria ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Research Area ◽  
Svm Classifier ◽  
Ranking Methods ◽  
Wrapper Approach ◽  
Relevant Gene

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.

Epigenetic Reprogramming and Landscape of Transcriptomic Interactions: Impending Therapeutic Interference of Triple-Negative Breast Cancer in Molecular Medicine

2021 ◽  
Vol 21 ◽  
Author(s):  
Suman Kumar Ray ◽  
Sukhes Mukherjee
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Treatment Decision ◽  
Copy Number Variations ◽  
Molecular Medicine ◽  
Breast Cancers ◽  
Breast Carcinogenesis

: The mechanisms governing the development and progression of cancers are believed to be the consequence of hereditary deformities and epigenetic modifications. Accordingly, epigenetics has become an incredible and progressively explored field of research to discover better prevention and therapy for neoplasia, especially triple-negative breast cancer (TNBC). It represents 15–20% of all invasive breast cancers and will, in general, have bellicose histological highlights and poor clinical outcomes. In the early phases of triple-negative breast carcinogenesis, epigenetic deregulation modifies chromatin structure and influences the plasticity of cells. It up-keeps the oncogenic reprogramming of malignant progenitor cells with the acquisition of unrestrained selfrenewal capacities. Genomic impulsiveness in TNBC prompts mutations, copy number variations, as well as genetic rearrangements, while epigenetic remodeling includes an amendment by DNA methylation, histone modification, and noncoding RNAs of gene expression profiles. It is currently evident that epigenetic mechanisms assume a significant part in the pathogenesis, maintenance, and therapeutic resistance of TNBC. Although TNBC is a heterogeneous malaise that is perplexing to describe and treat, the ongoing explosion of genetic and epigenetic research will help to expand these endeavors. Latest developments in transcriptome analysis have reformed our understanding of human diseases, including TNBC at the molecular medicine level. It is appealing to envision transcriptomic biomarkers to comprehend tumor behavior more readily regarding its cellular microenvironment. Understanding these essential biomarkers and molecular changes will propel our capability to treat TNBC adequately. This review will depict the different aspects of epigenetics and the landscape of transcriptomics in triple-negative breast carcinogenesis and their impending application for diagnosis, prognosis, and treatment decision with the view of molecular medicine.

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