scholarly journals Discovering lncRNA Mediated Sponge Interactions in Breast Cancer Molecular Subtypes

2017 ◽  
Author(s):  
Gulden Olgun ◽  
Ozgur Sahin ◽  
Oznur Tastan
Keyword(s):  
Breast Cancer ◽  
Hox Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Breast Cancer Subtypes ◽  
Integrative Approach ◽  
Cancer Subtypes ◽  
Significant Difference

AbstractMotivationLong non-coding RNAs(lncRNAs) can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as competing endogenous RNAs (ceRNAs) or as sponges. Previous studies identified lncRNA-mediated sponge interactions in various cancers including the breast cancer. However, breast cancer subtypes are quite distinct in terms of their molecular profiles; therefore, ceRNAs are expected to be subtype-specific as well.ResultsTo find lncRNA-mediated ceRNA interactions in breast cancer subtypes, we develop an integrative approach. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions. Functional enrichment of mRNAs that participate in these interactions highlights distinct biological processes for different subtypes. Interestingly, some of the ceRNAs also reside in close proximity in the genome; for example, those involving HOX genes, HOTAIR, miR-196a-1 and miR-196a-2. We also discover subtype-specific sponge interactions with high prognostic potential. For instance, when grouping is based on the expression patterns of specific sponge interactions, patients differ significantly in their survival distributions. If on the other hand, patients are grouped based on the individual RNA expression profiles of the sponge participants, they do not exhibit a significant difference in survival. These results can help shed light on subtype-specific mechanisms of breast cancer, and the methodology developed herein can help uncover sponges in other diseases.

scholarly journals Logic-based Analysis of Gene Expression Data Predicts Pathway Crosstalk between TNF, TGFB1 and EGF in Basal-like Breast Cancer

2019 ◽  
Author(s):  
Kyuri Jo ◽  
Beatriz Santos Buitrago ◽  
Minsu Kim ◽  
Sungmin Rhee ◽  
Carolyn Talcott ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathways ◽  
Expression Profiles ◽  
Formal System ◽  
Gene Expression Profiles ◽  
Breast Cancer Subtypes ◽  
Post Translational Modifications ◽  
Cancer Subtypes ◽  
Pathway Crosstalk

AbstractFor breast cancer, clinically important subtypes are well characterised at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterise biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences.We present a logic-based approach to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Proposed method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential pathway crosstalk via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes.AvailabilityAnalysis result is available at http://epigenomics.snu.ac.kr/PL/

scholarly journals Sample-specific perturbation of gene interactions identifies breast cancer subtypes

2020 ◽  
Author(s):  
Yuanyuan Chen ◽  
Yu Gu ◽  
Zixi Hu ◽  
Xiao Sun
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Biological Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Interaction ◽  
Breast Cancer Subtypes ◽  
Gene Interaction Network ◽  
Cancer Subtypes

Abstract Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

Abstract A25: Differences in microRNA expression patterns in breast cancer subtypes

2012 ◽  
Vol 72 (2 Supplement) ◽  
pp. A25-A25
Author(s):  
Sandra L. Romero-Cordoba ◽  
Rosa G. Rebollar-Vega ◽  
Valeria Quintanar-Jurado ◽  
Veronica Bautista-Pina ◽  
Sergio Rodriguez-Cuevas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Expression Patterns ◽  
Microrna Expression ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes

scholarly journals Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Wei Dai ◽  
Wenhao Yue ◽  
Wei Peng ◽  
Xiaodong Fu ◽  
Li Liu ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Convolutional Network ◽  
Similarity Network ◽  
Cancer Subtypes ◽  
Subtype Classification ◽  
Convolution Model ◽  
Cancer Subtype ◽  
Residual Graph

Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.

An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomic, Interactomic, and Transcriptomic Data

2017 ◽  
Vol 24 (8) ◽  
pp. 756-766 ◽  
Author(s):  
Forough Firoozbakht ◽  
Iman Rezaeian ◽  
Michele D'agnillo ◽  
Lisa Porter ◽  
Luis Rueda ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtypes ◽  
Integrative Approach ◽  
Cancer Subtypes ◽  
Transcriptomic Data ◽  
Network Biomarkers

scholarly journals Gene Expression Profiles of Multiple Synchronous Lesions in Lung Adenocarcinoma

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3484
Author(s):  
Jisun Lim ◽  
Yeon Bi Han ◽  
Soo Young Park ◽  
Soyeon Ahn ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Killer Cell ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Stepwise Change

Many studies support a stepwise continuum of morphologic changes between atypical adenomatous hyperplasia (AAH) and lung adenocarcinoma (ADC). Here we characterized gene expression patterns and the association of differentially expressed genes and immune tumor microenvironment behaviors in AAH to ADC during ADC development. Tumor tissues from nine patients with ADC and synchronous multiple ground glass nodules/lesions (GGN/Ls) were analyzed using RNA sequencing. Using clustering, we identified genes differentially and sequentially expressed in AAH and ADC compared to normal tissues. Functional enrichment analysis using gene ontology terms was performed, and the fraction of immune cell types was estimated. We identified up-regulated genes (ACSL5 and SERINC2) with a stepwise change of expression from AAH to ADC and validated those expressions by quantitative PCR and immunohistochemistry. The immune cell profiles revealed increased B cell activities and decreased natural killer cell activities in AAH and ADC. A stepwise change of differential expression during ADC development revealed potential effects on immune function in synchronous precursors and in tumor lesions in patients with lung cancer.

scholarly journals Analysis of gene expression patterns in triple negative breast cancer: MERTK and WHSC1L1.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Black Women ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Primary Tumors

Triple negative breast cancer (TNBC) shares overlap with the basal molecular subtype of breast cancer and is more frequently diagnosed in African-American (black) women for reasons not understood (1,2). To understand genes whose expression may be of pertinence to the development or progression of triple negative breast cancer, we mined published microarray data (3,4) comparing global gene expression profiles of TNBC histology groups, identifying genes whose expression changed the least between among TNBCs, suggesting that these genes may be important for TNBC biology. We identified the MER proto-oncogene tyrosine kinase MERTK and the Wolf-Hirschhorn syndrome candidate 1-like 1 WHSC1L1 among the genes whose expression differed the least when comparing TNBC cases and subtypes. In another dataset, MERTK and WHSCL1 were found to be differentially expressed in TNBC when comparing primary tumors of the breast to normal breast tissue. Kaplan-Meier survival analysis revealed that expression levels of MERTK and WHSCL1 correlated with survival outcomes in human breast cancer, and that this correlation differed based on race of the patient. MERTK and WHSCL1 may be of relevance in understanding the etiology or progression of triple negative breast cancer.

Comprehensive HOX Gene Expression Profiles Are Associated with Major Cytogenetic Prognostic Categories in AML.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2989-2989
Author(s):  
Harry A. Drabkin ◽  
Vivian Ruvolo ◽  
Sharvari Gadgil ◽  
Wenjing Chen ◽  
Chan Zeng ◽  
...  
Keyword(s):  
Cell Fate ◽  
Hox Genes ◽  
De Novo ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Model Systems ◽  
Acute Leukemias ◽  
Hox Gene ◽  
Cd34 Cells

Abstract The homeodomain genes comprise a set of transcription factors that determine cell fate by regulating proliferation, development, and apoptosis. Humans have 39 class I homeodomain genes (HOX) that occur in four clusters (HOXA, HOXB, HOXC, and HOXD). During development HOX expression takes place according to the position of a gene within its cluster and the position of the cell along the anterior - posterior axis. Some HOX genes are expressed in adult tissues, where they are thought to regulate the regenerative differentiation of cells. If one were to view leukemia as a disorder of regenerative hematopoesis, one could hypothesize that dysregulation of HOX expression promotes leukemogenesis. The role of some homeodomain genes in acute leukemia has been especially well studied. In mouse model systems, overexpression of HOXA7, HOXA9 and Meis1 lead to AML. Chromosomal translocations targeting HOX and other homeodomain genes are associated with acute myeloid and lymphoid leukemias. Previous results have suggested that HOX expression patterns might define certain AML subsets. In the present study, we analyzed the expression of 40 homeodomain genes, among them 25 of the HOXA-D genes, in leukemic enriched samples from 66 patients with de novo AML and in sorted CD34+ cells derived from four healthy bone marrow donors. Also, in order to integrate any effects of mutations in FLT3, C/EBPa, and nucleophosmin (NPM) on HOX expression, we assessed the presence of mutations in these three genes. Our results demonstrate that HOX expression patterns are intimately linked to particular cytogenetic abnormalities. The most striking overall findings were the overexpression of HOXA and HOXB genes in AMLs with NPM mutations, the similarity of HOX expression in AMLs with unfavorable cytogenetics to that of AMLs with intermediate cytogenetics, and the downregulation of HOXA genes in core binding factor (CBF) AMLs. Moreover, AMLs with translocations involving CBFbeta had distinctly higher expression of HOXB2, HOXB3, HOXB4, and Meis 1 than did patients with translocations involving CBFalpha. Some HOX genes displayed no heterogeneity of expression and are thus likely unrelated to leukemogenesis. Other genes, particularly HOXA and HOXB genes, displayed marked heterogeneity of expression and thus may have a role in leukemogenesis. However, every AML had substantial differences in the expression of at least one HOX gene compared to normal CD34+ cells. In addition, levels of HOX expression distinguished within individual cytogenetic groups certain subsets, including cases with inv(16) and cases that phenotypically resembled NPM mutations. Based on these results and the causative nature of HOX deregulation in some acute leukemias, we postulate that the HOX expression patterns exemplified here may be responsible for some (or many) of the biologic differences observed among the major cytogenetic prognostic groups.

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