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 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 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.

scholarly journals TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

2018 ◽  
Author(s):  
Amartya Singh ◽  
Gyan Bhanot ◽  
Hossein Khiabanian
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Disease Heterogeneity ◽  
Transcriptional Profiles ◽  
Biologically Relevant ◽  
Tumor Subtypes ◽  
Stromal Tissue ◽  
Copy Number Changes

ABSTRACTBackgroundTraditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes’ expression profiles to identify similarly altered signatures.ResultsTuBA’s predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens.ConclusionTuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

Identification and Validation of a Novel 16-Gene Prognostic Signature for Patients with Breast Cancer

2021 ◽  
Author(s):  
Zhenhua Zhong ◽  
Wenqiang Jiang ◽  
Jing Zhang ◽  
Zhanwen Li ◽  
Fengfeng Fan
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Rna Degradation ◽  
The Cancer Genome Atlas ◽  
Gene Score ◽  
Disease Heterogeneity ◽  
Cox Regression Analysis ◽  
Risk Of Death

Abstract Background: Despite increased early diagnosis and improved treatment in breast cancer (BRCA) patients, prognosis prediction is still a challenging task due to the disease heterogeneity. This study was to identify a novel gene signature that can accurately evaluate BRCA patient survival. Methods: The gene expression and clinical data of BRCA patients were collected from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases. Genes associated with prognosis were determined by Kaplan–Meier survival analysis and multivariate Cox regression analysis. A prognostic 16-gene score was established with linear combination of 16 genes. The prognostic value of the signature was validated in the METABRIC dataset. Gene expression analysis was performed to investigate the diagnostic values of 16 genes. Results: The 16-gene score was associated with shortened overall survival in BRCA patients independently of clinicopathological characteristics. The signalling pathways of cell cycle, oocyte meiosis, RNA degradation, progesterone mediated oocyte maturation and DNA replication were the top five most enriched pathways in the high 16-gene score group. The 16-gene nomogram incorporating the survival‐related clinical factors showed improved prediction accuracies for 1-year, 3-year and 5‐year survival (area under curve [AUC] = 0.91, 0.79 and 0.77 respectively). MORN3, IGJ, DERL1 exhibited high accuracy in differentiating BRCA tissues from normal breast tissues (AUC > 0.80 for all cases). Conclusions: The 16-gene profile provides novel insights into the identification of BRCA with a high risk of death, which eventually guides treatment decision making.

scholarly journals Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression

2021 ◽  
Vol 11 ◽  
Author(s):  
Shengnan Wang ◽  
Lingyu Ma ◽  
Ziyuan Wang ◽  
Huiwen He ◽  
Huilin Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lactate Dehydrogenase ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Cellular Migration ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Tumor Associated Macrophages ◽  
Cancer Stemness ◽  
Data Set

Increasing evidence reveals that breast cancer stem cells (BCSCs) subtypes with distinct properties are regulated by their abnormal metabolic changes; however, the specific molecular mechanism and its relationship with tumor microenvironment (TME) are not clear. In this study, we explored the mechanism of lactate dehydrogenase A (LDHA), a crucial glycolytic enzyme, in maintaining cancer stemness and BCSCs plasticity, and promoting the interaction of BCSCs with tumor associated macrophages (TAMs). Firstly, the expression of LDHA in breast cancer tissues was much higher than that in adjacent tissues and correlated with the clinical progression and prognosis of breast cancer patients based on The Cancer Genome Atlas (TCGA) data set. Moreover, the orthotopic tumor growth and pulmonary metastasis were remarkable inhibited in mice inoculated with 4T1-shLdha cells. Secondly, the properties of cancer stemness were significantly suppressed in MDA-MB-231-shLDHA or A549-shLDHA cancer cells, including the decrease of ALDH+ cells proportion, the repression of sphere formation and cellular migration, and the reduction of stemness genes (SOX2, OCT4, and NANOG) expression. However, the proportion of ALDH+ cells (epithelial-like BCSCs, E-BCSCs) was increased and the proportion of CD44+ CD24− cells (mesenchyme-like BCSCs, M-BCSCs) was decreased after LDHA silencing, suggesting a regulatory role of LDHA in E-BCSCs/M-BCSCs transformation in mouse breast cancer cells. Thirdly, the expression of epithelial marker E-cadherin, proved to interact with LDHA, was obviously increased in LDHA-silencing cancer cells. The recruitment of TAMs and the secretion of CCL2 were dramatically reduced after LDHA was knocked down in vitro and in vivo. Taken together, LDHA mediates a vicious cycle of mutual promotion between BCSCs plasticity and TAMs infiltration, which may provide an effective treatment strategy by targeting LDHA for breast cancer patients.

scholarly journals An Immune-Related Gene Prognostic Index for Triple-Negative Breast Cancer Integrates Multiple Aspects of Tumor-Immune Microenvironment

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5342
Author(s):  
Xiaowei Wang ◽  
Wenjia Su ◽  
Dabei Tang ◽  
Jing Jing ◽  
Jing Xiong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Data Set ◽  
Cox Regression Analysis

Tumor-immune cell compositions and immune checkpoints comprehensively affect TNBC outcomes. With the significantly improved survival rate of TNBC patients treated with ICI therapies, a biomarker integrating multiple aspects of TIME may have prognostic value for improving the efficacy of ICI therapy. Immune-related hub genes were identified with weighted gene co-expression network analysis and differential gene expression assay using The Cancer Genome Atlas TNBC data set (n = 115). IRGPI was constructed with Cox regression analysis. Immune cell compositions and TIL status were analyzed with CIBERSORT and TIDE. The discovery was validated with the Molecular Taxonomy of Breast Cancer International Consortium data set (n = 196) and a patient cohort from our hospital. Tumor expression or serum concentrations of CCL5, CCL25, or PD-L1 were determined with immunohistochemistry or ELISA. The constructed IRGPI was composed of CCL5 and CCL25 genes and was negatively associated with the patient’s survival. IRGPI also predicts the compositions of M0 and M2 macrophages, memory B cells, CD8+ T cells, activated memory CD4 T cells, and the exclusion and dysfunction of TILs, as well as PD-1 and PD-L1 expression of TNBC. IRGPI is a promising biomarker for predicting the prognosis and multiple immune characteristics of TNBC.

scholarly journals Immunotherapy in HER2-positive breast cancer: state of the art and future perspectives

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
E. Krasniqi ◽  
G. Barchiesi ◽  
L. Pizzuti ◽  
M. Mazzotta ◽  
A. Venuti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune System ◽  
Immune Checkpoint ◽  
Complex Disease ◽  
Checkpoint Inhibitors ◽  
Tumor Infiltrating Lymphocytes ◽  
Considerable Proportion ◽  
New Paradigm ◽  
Her2 Positive ◽  
Anti Cancer

Abstract Breast cancer (BC) is a complex disease with primary or acquired incurability characteristics in a significant part of patients. Immunotherapeutical agents represent an emerging option for breast cancer treatment, including the human epidermal growth factor 2 positive (HER2+) subtype. The immune system holds the ability to spontaneously implement a defensive response against HER2+ BC cells through complex mechanisms which can be exploited to modulate this response for obtaining a clinical benefit. Initial immune system modulating strategies consisted mostly in vaccine therapies, which are still being investigated and improved. However, the entrance of trastuzumab into the scenery of HER2+ BC treatment was the real game changing event, which embodied a dominant immune-mediated mechanism. More recently, the advent of the immune checkpoint inhibitors has caused a new paradigm shift for immuno-oncology, with promising initial results also for HER2+ BC. Breast cancer has been traditionally considered poorly immunogenic, being characterized by relatively low tumor mutation burden (TMB). Nevertheless, recent evidence has revealed high tumor infiltrating lymphocytes (TILs) and programmed cell death-ligand 1 (PD-L1) expression in a considerable proportion of HER2+ BC patients. This may translate into a higher potential to elicit anti-cancer response and, therefore, wider possibilities for the use and implementation of immunotherapy in this subset of BC patients. We are herein presenting and critically discussing the most representative evidence concerning immunotherapy in HER2+ BC cancer, both singularly and in combination with therapeutic agents acting throughout HER2-block, immune checkpoint inhibition and anti-cancer vaccines. The reader will be also provided with hints concerning potential future projection of the most promising immutherapeutic agents and approaches for the disease of interest.

Integrated Quantitative Analysis of the Phosphoproteome and Transcriptome in Tamoxifen-resistant Breast Cancer*

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Breast Cancer ◽  
High Rate ◽  
System Level ◽  
Clinical Samples ◽  
Transcriptome Data ◽  
Data Set ◽  
Reporter Assays ◽  
Treated Breast ◽  
Mcf 7 ◽  
Resistant Cells

Quantitative phosphoproteome and transcriptome analysisof ligand-stimulated MCF-7 human breast cancer cells wasperformed to understand the mechanisms of tamoxifen resistanceat a system level. Phosphoproteome data revealed thatWT cells were more enriched with phospho-proteins thantamoxifen-resistant cells after stimulation with ligands.Surprisingly, decreased phosphorylation after ligand perturbationwas more common than increased phosphorylation.In particular, 17?-estradiol induced down-regulation inWT cells at a very high rate. 17?-Estradiol and the ErbBligand heregulin induced almost equal numbers of up-regulatedphospho-proteins in WT cells. Pathway and motifactivity analyses using transcriptome data additionallysuggested that deregulated activation of GSK3? (glycogensynthasekinase 3?) and MAPK1/3 signaling might be associatedwith altered activation of cAMP-responsive elementbindingprotein and AP-1 transcription factors intamoxifen-resistant cells, and this hypothesis was validatedby reporter assays. An examination of clinical samples revealedthat inhibitory phosphorylation of GSK3? at serine 9was significantly lower in tamoxifen-treated breast cancerpatients that eventually had relapses, implying that activationof GSK3? may be associated with the tamoxifen-resistantphenotype. Thus, the combined phosphoproteomeand transcriptome data set analyses revealed distinct signal

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