scholarly journals Deconvolution of DNA methylation signatures identifies common differentially methylated gene regions on 1p36 across breast cancer subtypes

2017 ◽  
Author(s):  
Alexander J. Titus ◽  
Gregory P. Way ◽  
Kevin C. Johnson ◽  
Brock C. Christensen
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Complex Disease ◽  
Molecular Subtype ◽  
Early Stage ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Disease Heterogeneity ◽  
Cancer Subtypes ◽  
Differentially Methylated Gene

ABSTRACTBreast cancer is a complex disease and studying DNA methylation (DNAm) in tumors is complicated by disease heterogeneity. We compared DNAm in breast tumors with normal-adjacent breast samples from The Cancer Genome Atlas (TCGA). We constructed models stratified by tumor stage and PAM50 molecular subtype and performed cell-type reference-free deconvolution on each model. We identified nineteen differentially methylated gene regions (DMGRs) in early stage tumors across eleven genes (AGRN, C1orf170, FAM41C, FLJ39609, HES4, ISG15, KLHL17, NOC2L, PLEKHN1, SAMD11, WASH5P). These regions were consistently differentially methylated in every subtype and all implicated genes are localized on chromosome 1p36.3. We also validated seventeen DMGRs in an independent data set. Identification and validation of shared DNAm alterations across tumor subtypes in early stage tumors advances our understanding of common biology underlying breast carcinogenesis and may contribute to biomarker development. We also provide evidence on the importance and potential function of 1p36 in cancer.

scholarly journals The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes

2020 ◽  
Vol 21 (18) ◽  
pp. 6690 ◽  
Author(s):  
Anna Maria Grimaldi ◽  
Federica Conte ◽  
Katia Pane ◽  
Giulia Fiscon ◽  
Peppino Mirabelli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Complex Disease ◽  
Ex Vivo ◽  
The Cancer Genome Atlas ◽  
Disease Genes ◽  
Network Medicine ◽  
New Paradigm ◽  
Data Set ◽  
Disease Heterogeneity ◽  
Tumor Subtypes

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein–protein interaction modules based on “hub genes”, called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

Comparison of breast cancer subtypes detected by mammography screening and tumors diagnosed with palpable masses.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e11028-e11028
Author(s):  
Cesar Gomez Raposo ◽  
Ines Suarez-Garcia ◽  
Miriam Lopez-Gomez ◽  
Isabel Esteban ◽  
Monica Andreu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mammography Screening ◽  
Molecular Subtype ◽  
Early Stage ◽  
Breast Cancer Subtypes ◽  
Categorical Variables ◽  
Her2 Positive ◽  
Cancer Subtypes ◽  
Number Of Patients ◽  
Er Positive

e11028 Background: Recent studies have reported a greater mean breast density associated with estrogen receptor (ER)-positive disease than with ER-negative tumors, but controversial results have been published. The purpose of our study was to compare the difference in the distribution of breast cancer subtypes detected either by mammography screening or tumors diagnosed with clinically palpable masses in our centre. Methods: Between June 2008 and December 2011 157 patients with age between 40 and 70 years-old were diagnosed of early stage breast cancer. Tumors were classified in three major breast cancer subtypes by immunohistochemistry: TN (ie, ER, PR and Her2 negative), Her2-positive (ie, Her2 positive; ER and PR may be positive or negative), and ER-positive/Her2 negative (ie, ER positive, Her2 negative, PR may be positive or negative) types. Tumor characteristics and type of diagnosis (mammography screening or palpable masses) were obtained by retrospective chart review. Associations between categorical variables were evaluated with the X2 test. Results: Seventy patients (44.6%) were diagnosed of breast cancer with screening mammography, whereas 85 (54.1%) cases were detected by clinical suspected masses. Distribution according to molecular subtype defined by IHC were as follows: 123 (78.3%) ER-positive/Her2-negative tumors, 15 (9.6%) TN tumors, and 19 (12.1%) Her2-positive tumors. Less Her2-positive and TN tumors were significantly diagnosed with mammography screening (p=0.039 and p=0,020, respectively). No statistical differences were showed between ER-positive/Her2-negative tumors diagnosed with mammography screening and clinical palpable masses (p=0.78). Conclusions: The results presented in our study suggest that biologically aggressive subtypes of breast cancer are less frequently diagnosed with mammography screening in comparison with ER-positive/Her-2 negative tumors. Nevertheless, given the limited number of patients included and the bias related to retrospective studies these results must be interpreted with caution.

scholarly journals Highly connected, non-redundant microRNA functional control in breast cancer molecular subtypes

2021 ◽  
Vol 11 (4) ◽  
pp. 20200073 ◽  
Author(s):  
Guillermo de Anda-Jáuregui ◽  
Jesús Espinal-Enríquez ◽  
Enrique Hernández-Lemus
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Patterns ◽  
Network Biology ◽  
The Cancer Genome Atlas ◽  
Biological Functions ◽  
Cancer Subtypes ◽  
Potential Control ◽  
Functional Studies

Breast cancer is a complex, heterogeneous disease at the phenotypic and molecular level. In particular, the transcriptional regulatory programs are known to be significantly affected and such transcriptional alterations are able to capture some of the heterogeneity of the disease, leading to the emergence of breast cancer molecular subtypes. Recently, it has been found that network biology approaches to decipher such abnormal gene regulation programs, for instance by means of gene co-expression networks, have been able to recapitulate the differences between breast cancer subtypes providing elements to further understand their functional origins and consequences. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding transcripts such as microRNAs (miRs). As is known, miRs play relevant roles in the establishment of normal and anomalous transcription processes. Commodore miRs (cdre-miRs) have been defined as miRs that, based on their connectivity and redundancy in co-expression networks, are potential control elements of biological functions. In this work, we reconstructed miR–gene co-expression networks for each breast cancer molecular subtype, from high throughput data in 424 samples from the Cancer Genome Atlas consortium. We identified cdre-miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR was linked to a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer such as angiogenesis, cell adhesion, cell cycle and regulation of apoptosis. The relevance of such cdre-miRs as actionable molecular targets in breast cancer is still to be determined from functional studies.

scholarly journals The MS-lincRNA landscape reveals a novel lincRNA BCLIN25 that contributes to tumorigenesis by upregulating ERBB2 expression via epigenetic modification and RNA–RNA interactions in breast cancer

2019 ◽  
Vol 10 (12) ◽  
Author(s):  
Shouping Xu ◽  
Hongbo Liu ◽  
Lin Wan ◽  
Weijia Zhang ◽  
Qin Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Subtype ◽  
Epigenetic Modification ◽  
Molecular Classification ◽  
Underlying Mechanism ◽  
The Cancer Genome Atlas ◽  
Cancer Subtypes ◽  
Erbb2 Expression

AbstractThe landscape of molecular subtype-specific long intergenic noncoding RNAs (MS-lincRNAs) in breast cancer has not been elucidated. No study has investigated the biological function of BCLIN25, serving as a novel HER2 subtype-specific lincRNA, in human disease, especially in malignancy. Moreover, the mechanism of BCLIN25 in the regulation of ERBB2 expression remains unknown. Our present study aimed to investigate the role and underlying mechanism of BCLIN25 in the regulation of ERBB2 expression. The transcriptional landscape across five subtypes of breast cancer was investigated using RNA sequencing. Integrative transcriptomic analysis was performed to identify the landscape of novel lincRNAs. Next, WEKA was used to identify lincRNA-based subtype classification and MS-lincRNAs for breast cancer. The MS-lincRNAs were validated in 250 breast cancer samples in our cohort and datasets from The Cancer Genome Atlas and Gene Expression Omnibus. Furthermore, BCLIN25 was selected, and its role in tumorigenesis was examined in vitro and in vivo. Finally, the mechanism by which BCLIN25 regulates ERBB2 expression was investigated in detail. A total of 715 novel lincRNAs were differentially expressed across five breast cancer subtypes. Next, lincRNA-based subtype classifications and MS-lincRNAs were identified and validated using our breast cancer samples and public datasets. BCLIN25 was found to contribute to tumorigenesis in vitro and in vivo. Mechanistically, BCLIN25 was shown to increase the expression of ERBB2 by enhancing promoter CpG methylation of miR-125b, leading to miR-125b downregulation. In turn, ERBB2 mRNA degradation was found to be abolished due to decreased binding of miR-125b to the 3’-untranslated region (UTR) of ERBB2. These findings reveal the role of novel lincRNAs in breast cancer and provide a comprehensive landscape of breast cancer MS-lincRNAs, which may complement the current molecular classification system in breast cancer.

scholarly journals Epigenetic Regulation of Immunotherapy Response in Triple-Negative Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4139
Author(s):  
Pere Llinàs-Arias ◽  
Sandra Íñiguez-Muñoz ◽  
Kelly McCann ◽  
Leonie Voorwerk ◽  
Javier I. J. Orozco ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Immune Escape ◽  
Checkpoint Inhibitors ◽  
Early Stage ◽  
Regulatory Elements ◽  
Her2 Overexpression ◽  
Breast Cancers ◽  
Cancer Subtypes

Triple-negative breast cancer (TNBC) is defined by the absence of estrogen receptor and progesterone receptor and human epidermal growth factor receptor 2 (HER2) overexpression. This malignancy, representing 15–20% of breast cancers, is a clinical challenge due to the lack of targeted treatments, higher intrinsic aggressiveness, and worse outcomes than other breast cancer subtypes. Immune checkpoint inhibitors have shown promising efficacy for early-stage and advanced TNBC, but this seems limited to a subgroup of patients. Understanding the underlying mechanisms that determine immunotherapy efficiency is essential to identifying which TNBC patients will respond to immunotherapy-based treatments and help to develop new therapeutic strategies. Emerging evidence supports that epigenetic alterations, including aberrant chromatin architecture conformation and the modulation of gene regulatory elements, are critical mechanisms for immune escape. These alterations are particularly interesting since they can be reverted through the inhibition of epigenetic regulators. For that reason, several recent studies suggest that the combination of epigenetic drugs and immunotherapeutic agents can boost anticancer immune responses. In this review, we focused on the contribution of epigenetics to the crosstalk between immune and cancer cells, its relevance on immunotherapy response in TNBC, and the potential benefits of combined treatments.

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

scholarly journals Genomic Signatures in Luminal Breast Cancer

Breast Care ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 355-365
Author(s):  
Julian Puppe ◽  
Tabea Seifert ◽  
Christian Eichler ◽  
Henryk Pilch ◽  
Peter Mallmann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Throughput Sequencing ◽  
Early Stage ◽  
Intrinsic Subtype ◽  
Luminal Breast Cancer ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Molecular Tests ◽  
Immunohistochemical Markers

Background: Breast cancer is a very heterogeneous disease and luminal breast carcinomas represent the hormone receptor-positive tumors among all breast cancer subtypes. In this context, multigene signatures were developed to gain further prognostic and predictive information beyond clinical parameters and traditional immunohistochemical markers. Summary: For early breast cancer patients these molecular tools can guide clinicians to decide on the extension of endocrine therapy to avoid over- and undertreatment by adjuvant chemotherapy. Beside the predictive and prognostic value, a few genomic tests are also able to provide intrinsic subtype classification. In this review, we compare the most frequently used and commercially available molecular tests (OncotypeDX®, MammaPrint®, Prosigna®, EndoPredict®, and Breast Cancer IndexSM). Moreover, we discuss the clinical utility of molecular profiling for advanced breast cancer of the luminal subtype. Key Messages: Multigene assays can help to de-escalate systemic therapy in early-stage breast cancer. Only the Oncotype DX® and MammaPrint®test are validated by entirely prospective and randomized phase 3 trials. More clinical evidence is needed to support the use of genomic tests in node-positive disease. Recent developments in high-throughput sequencing technology will provide further insights to understand the heterogeneity of luminal breast cancers in early-stage and metastatic disease.

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