scholarly journals Personalized analysis of breast cancer using sample-specific networks

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9161
Author(s):  
Ke Zhu ◽  
Cong Pian ◽  
Qiong Xiang ◽  
Xin Liu ◽  
Yuanyuan Chen
Keyword(s):  
Breast Cancer ◽  
Expression Profiles ◽  
Single Gene ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Cancer Prognosis ◽  
Specific Gene ◽  
Validation Data ◽  
Data Set ◽  
Gene Interaction Networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

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 RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ashish Saini ◽  
Jingyu Hou ◽  
Wanlei Zhou
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Mortality Rate ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Breast Cancer Subtypes ◽  
Positive Interactions ◽  
Gene Signatures ◽  
Cancer Subtypes ◽  
Gene Interaction Networks

Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposedRRHGEalgorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples.Results. The evaluation on real breast cancer samples showed that ourRRHGEalgorithm achieved higher classification accuracy than the existing approaches.

Identifying functional modules using energy minimization with graph cuts

2021 ◽  
Vol 16 ◽  
Author(s):  
Yuanyuan Chen ◽  
Xiaodan Fan ◽  
Cong Pian
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Energy Minimization ◽  
Expression Profiles ◽  
Gene Interaction ◽  
Graph Cuts ◽  
Interaction Networks ◽  
Functional Modules ◽  
Expression Data ◽  
Gene Interaction Networks

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Method: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.

scholarly journals Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer

BMC Genomics ◽  
2017 ◽  
Vol 18 (S6) ◽  
Author(s):  
Yu-Chiao Chiu ◽  
Li-Ju Wang ◽  
Tzu-Hung Hsiao ◽  
Eric Y. Chuang ◽  
Yidong Chen
Keyword(s):  
Breast Cancer ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Gene Interaction Networks ◽  
Genome Wide

scholarly journals Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

2016 ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Zeyneb Kurt ◽  
Sean Geoffrey Byars ◽  
Taru Tukiainen ◽  
...  
Keyword(s):  
Cholesterol Metabolism ◽  
Meta Analysis ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Specific Gene ◽  
Data Types ◽  
Tissue Specific ◽  
Pathway Network ◽  
Tissue Specific Gene ◽  
Gene Interaction Networks

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

scholarly journals Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

2021 ◽  
Author(s):  
Elisabetta Sciacca ◽  
Anna E.A. Surace ◽  
Salvatore Alaimo ◽  
Alfredo Pulvirenti ◽  
Felice Rivellese ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Rna Sequencing ◽  
Gene Networks ◽  
Prediction Models ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Gene Interactions ◽  
Specific Gene ◽  
Rna Seq ◽  
Gene Interaction Networks

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

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