scholarly journals Identification of Metabolic Alterations in Breast Cancer Using Mass Spectrometry-Based Metabolomic Analysis

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 170
Author(s):  
Sili Fan ◽  
Muhammad Shahid ◽  
Peng Jin ◽  
Arash Asher ◽  
Jayoung Kim
Keyword(s):  
Breast Cancer ◽  
Mass Spectrometry ◽  
Clinical Setting ◽  
Metabolic Networks ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Global Health Issue ◽  
Metabolite Set Enrichment Analysis

Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were further analyzed to identify differentially expressed metabolites in BC and their associated metabolic networks. Additionally, Chemical Similarity Enrichment Analysis (ChemRICH), MetaMapp, and Metabolite Set Enrichment Analysis (MSEA) identified significantly enriched clusters and networks in BC tissues. Since metabolomic signatures hold significant promise in the clinical setting, more effort should be placed on validating potential BC biomarkers based on identifying altered metabolomes.

scholarly journals Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review

Metabolites ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 102 ◽  
Author(s):  
Catarina Silva ◽  
Rosa Perestrelo ◽  
Pedro Silva ◽  
Helena Tomás ◽  
José S. Câmara
Keyword(s):  
Breast Cancer ◽  
Survival Rates ◽  
Diagnostic Tools ◽  
Disease Monitoring ◽  
Major Health ◽  
Cancer Breast ◽  
Different Types ◽  
Genetic And Environmental Factors ◽  
Analytical Platforms ◽  
Major Health Issue

Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.

scholarly journals Advanced Approaches to Breast Cancer Classification and Diagnosis

2021 ◽  
Vol 11 ◽  
Author(s):  
M. Zubair ◽  
S. Wang ◽  
N. Ali
Keyword(s):  
Breast Cancer ◽  
Lipid Membrane ◽  
Positive Impact ◽  
Specific Treatment ◽  
Recurrence Score ◽  
Diagnosis And Treatment ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
International Agency ◽  
Early Screening

The International Agency for Research on Cancer (IARC) has recently reported a 66% increase in the global number of cancer deaths since 1960. In the US alone, about one in eight women is expected to develop invasive breast cancer(s) (breast cancer) at some point in their lifetime. Traditionally, a BC diagnosis includes mammography, ultrasound, and some high-end molecular bioimaging. Unfortunately, these techniques detect BC at a later stage. So early and advanced molecular diagnostic tools are still in demand. In the past decade, various histological and immuno-molecular studies have demonstrated that BC is highly heterogeneous in nature. Its growth pattern, cytological features, and expression of key biomarkers in BC cells including hormonal receptor markers can be utilized to develop advanced diagnostic and therapeutic tools. A cancer cell's progression to malignancy exhibits various vital biomarkers, many of which are still underrepresented in BC diagnosis and treatment. Advances in genetics have also enabled the development of multigene assays to detect genetic heterogeneity in BC. However, thus far, the FDA has approved only four such biomarkers—cancer antigens (CA); CA 15-3, CA 27-29, Human epidermal growth factor receptor 2 (HER2), and circulating tumor cells (CTC) in assessing BC in body fluids. An adequately structured portable-biosensor with its non-invasive and inexpensive point-of-care analysis can quickly detect such biomarkers without significantly compromising its specificity and selectivity. Such advanced techniques are likely to discriminate between BC and a healthy patient by accurately measuring the cell shape, structure, depth, intracellular and extracellular environment, and lipid membrane compositions. Presently, BC treatments include surgery and systemic chemo- and targeted radiation therapy. A biopsied sample is then subjected to various multigene assays to predict the heterogeneity and recurrence score, thus guiding a specific treatment by providing complete information on the BC subtype involved. Thus far, we have seven prognostic multigene signature tests for BC providing a risk profile that can avoid unnecessary treatments in low-risk patients. Many comparative studies on multigene analysis projected the importance of integrating clinicopathological information with genomic-imprint analysis. Current cohort studies such as MINDACT, TAILORx, Trans-aTTOM, and many more, are likely to provide positive impact on long-term patient outcome. This review offers consolidated information on currently available BC diagnosis and treatment options. It further describes advanced biomarkers for the development of state-of-the-art early screening and diagnostic technologies.

scholarly journals Novel exosome biomarker candidates for Alzheimer´s disease unraveled through Mass Spectrometry analysis

2021 ◽  
Author(s):  
Tânia Soares Martins ◽  
Rui Marçalo ◽  
Cristóvão B. da Cruz e Silva ◽  
Dário Trindade ◽  
José Catita ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Cost Effective ◽  
Functional Enrichment ◽  
Mass Spectrometry Analysis ◽  
Precipitation Method ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Spectrometry Analysis ◽  
Wide Range

Abstract Background Exosomes are small extracellular vesicles (EVs) present in human biofluids that can carry disease-specific molecules. Blood-derived exosomes emerged as interesting peripheral sources of biomarkers for a wide range of diseases, and their potential is also being addressed in Alzheimer’s disease (AD) context. There is no effective cure or blood-based molecular diagnostic tools for a first clinical AD screening, which motivates research in this area to identify putative valuable blood-derived disease biomarkers. The ultimate goal is to produce a cost-effective and widely available alternative to the current molecular AD diagnosis that monitors a biomarker triplet in the CSF. Methods In the study here presented, EVs with exosome-like characteristics were isolated from the serum of Controls and AD cases through two distinct methods, precipitation- and column-based methods, followed by mass spectrometry analysis. The resulting proteomes from Controls and AD cases were characterized by Gene Ontology (GO) functional enrichment and multivariate analysis. Putative candidate targets identified were validated in distinct cohorts using antibody-based approaches. Results Both methodologies isolated particles with the expected morphology and size range. Although GO terms were similar for Controls and AD cases exosomes in both methodologies, the multivariate analysis revealed a clear segregation between Controls and AD cases obtained by the precipitation method. Nine significantly different abundant proteins were identified between Controls and AD cases and exosome levels of AACT and C4BPα, two Aβ-binding proteins, were validated in individuals from independent cohorts. Conclusions Serum-derived exosome proteomes were unraveled for the first time in the context of AD through two distinct isolation methodologies, holding disease discriminatory potential. The work carried out gives an important contribution to the identification of novel exosomal biomarker candidates potentially useful as blood-based tools for AD diagnosis.

scholarly journals A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

2019 ◽  
Author(s):  
Maryam Pouryahya ◽  
Jung Hun Oh ◽  
Pedram Javanmard ◽  
James C. Mathews ◽  
Zehor Belkhatir ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Inference ◽  
Survival Rates ◽  
Tumor Hypoxia ◽  
Network Connectivity ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Omics Analysis ◽  
Increased Risk

AbstractThe remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. The effective integration of such data provides a comprehensive view of the molecular complexity of cancer tumors and can significantly improve clinical out-come predictions. In this study, we present a novel network-based integration method of multiomics data as well as a clustering technique involving the Wasserstein (Earth Mover’s) distance from the theory of optimal mass transport. We applied our proposed method of integrative Wasserstein-based clustering (iWCluster) to invasive breast carcinoma from The Cancer Genome Atlas (TCGA) project. The subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation as well as the interaction network connectivity of the gene products. iW-Cluster is substantially more effective in distinguishing clusters with different survival rates as compared to isolated one-dimensional conventional omics analysis. Applying iWCluster to breast cancer TCGA data successfully recovered the known PAM50 molecular subtypes. In addition, iWCluster preserves the gene-specific data, which enables us to interpret the results and perform further analysis of significant genes for a specific cluster. The gene ontology enrichment analysis of significant genes in our substantially low survival sub-group leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. Increased expression of ETS1 is associated with an increased risk of recurrence and worse prognosis in breast cancer. Consequently, we believe iWCluster has the potential to discover novel subtypes by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

Determination of Usnic Acid Responsive miRNAs in Breast Cancer Cell Lines

2019 ◽  
Vol 19 (12) ◽  
pp. 1463-1472 ◽  
Author(s):  
Nil Kiliç ◽  
Yasemin Ö. Islakoğlu ◽  
İlker Büyük ◽  
Bala Gür-Dedeoğlu ◽  
Demet Cansaran-Duman
Keyword(s):  
Breast Cancer ◽  
Hedgehog Signaling ◽  
Treatment Options ◽  
Usnic Acid ◽  
Enrichment Analysis ◽  
Breast Cancer Cell Lines ◽  
Pathway Enrichment Analysis ◽  
Molecular Heterogeneity ◽  
Proliferative Effect ◽  
Mcf 7

Objective: Breast Cancer (BC) is the most common type of cancer diagnosed in women. A common treatment strategy for BC is still not available because of its molecular heterogeneity and resistance is developed in most of the patients through the course of treatment. Therefore, alternative medicine resources as being novel treatment options are needed to be used for the treatment of BC. Usnic Acid (UA) that is one of the secondary metabolites of lichens used for different purposes in the field of medicine and its anti-proliferative effect has been shown in certain cancer types, suggesting its potential use for the treatment. Methods: Anti-proliferative effect of UA in BC cells (MDA-MB-231, MCF-7, BT-474) was identified through MTT analysis. Microarray analysis was performed in cells treated with the effective concentration of UA and UA-responsive miRNAs were detected. Their targets and the pathways that they involve were determined using a miRNA target prediction tool. Results: Microarray experiments showed that 67 miRNAs were specifically responsive to UA in MDA-MB-231 cells while 15 and 8 were specific to BT-474 and MCF-7 cells, respectively. The miRNA targets were mostly found to play role in Hedgehog signaling pathway. TGF-Beta, MAPK and apoptosis pathways were also the prominent ones according to the miRNA enrichment analysis. Conclusion: The current study is important as being the first study in the literature which aimed to explore the UA related miRNAs, their targets and molecular pathways that may have roles in the BC. The results of pathway enrichment analysis and anti-proliferative effects of UA support the idea that UA might be used as a potential alternative therapeutic agent for BC treatment.

scholarly journals Discovery and function exploration of microRNA-155 as a molecular biomarker for early detection of breast cancer

Breast Cancer ◽  
2021 ◽  
Author(s):  
Xuemin Liu ◽  
Qingyu Chang ◽  
Haiqiang Wang ◽  
Hairong Qian ◽  
Yikun Jiang
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Protein Interactions ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Diagnostic Biomarker ◽  
Analysis Model ◽  
Sensitivity Specificity ◽  
Function Enrichment

Abstract Background MicroRNA-155 (miR-155) may function as a diagnostic biomarker of breast cancer (BC). Nevertheless, the available evidence is controversial. Therefore, we performed this study to summarize the global predicting role of miR-155 for early detection of BC and preliminarily explore the functional roles of miR-155 in BC. Methods We first collected published studies and applied the bivariate meta-analysis model to generate the pooled diagnostic parameters of miR-155 in diagnosing BC such as sensitivity, specificity and area under curve (AUC). Then, we applied function enrichment and protein–protein interactions (PPI) analyses to explore the potential mechanisms of miR-155. Results A total of 21 studies were finally included. The results indicated that miR-155 allowed for the discrimination between BC patients and healthy controls with a sensitivity of 0.87 (95% CI 0.78–0.93), specificity of 0.82 (0.72–0.89), and AUC of 0.91 (0.88–0.93). In addition, the overall sensitivity, specificity and AUC for circulating miR-155 were 0.88 (0.76–0.95), 0.83 (0.72–0.90), and 0.92 (0.89–0.94), respectively. Function enrichment analysis revealed several vital ontologies terms and pathways associated with BC occurrence and development. Furthermore, in the PPI network, ten hub genes and two significant modules were identified to be involved in some important pathways associated with the pathogenesis of BC. Conclusions We demonstrated that miR-155 has great potential to facilitate accurate BC detection and may serve as a promising diagnostic biomarker for BC. However, well-designed cohort studies and biological experiments should be implemented to confirm the diagnostic value of miR-155 before it can be applied to routine clinical procedures.

scholarly journals Age-Related Biology of Early-Stage Operable Breast Cancer and Its Impact on Clinical Outcome

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1417
Author(s):  
Binafsha M. Syed ◽  
Andrew R. Green ◽  
Emad A. Rakha ◽  
David A.L. Morgan ◽  
Ian O. Ellis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Histological Grade ◽  
Survival Rates ◽  
Growth Factor Receptor ◽  
Progesterone Receptors ◽  
Biological Characteristics ◽  
Cancer Specific Survival ◽  
Age Related ◽  
Significant Difference

As age advances, breast cancer (BC) tends to change its biological characteristics. This study aimed to explore the natural progression of such changes. The study included 2383 women with clinically T0-2N0-1M0 BC, managed by primary surgery and optimal adjuvant therapy in a dedicated BC facility. Tissue micro-arrays were constructed from their surgical specimens and indirect immunohistochemistry was used for analysis of a large panel (n = 16) of relevant biomarkers. There were significant changes in the pattern of expression of biomarkers related to luminal (oestrogen receptor (ER), progesterone receptors (PgR), human epidermal growth factor receptor (HER-2), E-cadherin, MUC1, bcl2 CK7/8, CK18 and bcl2) and basal (CK5/6, CK14, p53 and Ki67) phenotypes, lymph node stage, histological grade and pathological size when decade-wise comparison was made (p < 0.05). The ages of 40 years and 70 years appeared to be the milestones marking a change of the pattern. There were significantly higher metastasis free and breast cancer specific survival rates among older women with ER positive tumours while there was no significant difference in the ER negative group according to age. Biological characteristics of BC show a pattern of change with advancing age, where 40 years and 70 years appear as important milestones. The pattern suggests <40 years as the phase with aggressive phenotypes, >70 years as the less aggressive phase and 40–70 years being the transitional phase.

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