Identification of Biomarkers for Breast Cancer Using Databases

Lee, Eun‐Hye; Moon, Aree

doi:10.15430/jcp.2016.21.4.235

Cited by 23 publications

(16 citation statements)

References 76 publications

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“…Hundreds of other biomarkers have been investigated in breast cancer for potential diagnostic, prognostic, and therapeutic implications. Functional classification of these biomarkers includes growth and proliferation (Ki-67, survivin, NGAL), invasion and metastasis (p53, MMP-9, SK1, DcR3, COX2, EZH2, microRNAs miR-105, and miR126), epithelial–mesenchymal transition (EMT) (WNT5A/B, Pea3), immune response (PD-L1), therapy resistance (HER2Δ16, pSTS3, KLK10), survival (miR-574-3p, miR-660-5p, PIWIL3, PIWIL4), and many others ( 35 ). The magnitude of the effect of tumor heterogeneity on biomarker expression or its clinical significance remains uncertain.…”

Section: Intertumor Heterogeneitymentioning

confidence: 99%

Tumor Heterogeneity in Breast Cancer

2017

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Breast cancer is a heterogeneous disease and differs greatly among different patients (intertumor heterogeneity) and even within each individual tumor (intratumor heterogeneity). Clinical and morphologic intertumor heterogeneity is reflected by staging systems and histopathologic classification of breast cancer. Heterogeneity in the expression of established prognostic and predictive biomarkers, hormone receptors, and human epidermal growth factor receptor 2 oncoprotein is the basis for targeted treatment. Molecular classifications are indicators of genetic tumor heterogeneity, which is probed with multigene assays and can lead to improved stratification into low- and high-risk groups for personalized therapy. Intratumor heterogeneity occurs at the morphologic, genomic, transcriptomic, and proteomic levels, creating diagnostic and therapeutic challenges. Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Despite the improved knowledge of the complex genetic and phenotypic features underpinning tumor heterogeneity, there has been only limited advancement in diagnostic, prognostic, or predictive strategies for breast cancer. The current guidelines for reporting of biomarkers aim to maximize patient eligibility for targeted therapy, but do not take into account intratumor heterogeneity. The molecular classification of breast cancer is not implemented in routine clinical practice. Additional studies and in-depth analysis are required to understand the clinical significance of rapidly accumulating data. This review highlights inter- and intratumor heterogeneity of breast carcinoma with special emphasis on pathologic findings, and provides insights into the clinical significance of molecular and cellular mechanisms of heterogeneity.

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Section: Intertumor Heterogeneitymentioning

confidence: 99%

Tumor Heterogeneity in Breast Cancer

2017

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“…In addition, GEO (Gene Expression Omnibus) molecular data sets also provide a large number of clinical cancer-related gene expression data [8,9]. These available datasets help to identify the prognosis-associated oncogenes for breast cancer cases [10].…”

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confidence: 99%

WITHDRAWN: Identification of death-associated protein-like 1 (DAPL1) as a novel prognostic biomarker of breast cancer

Zhang

Liang

2020

Preprint

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Background: It is meaningful to identify the potential clinical prognosis-associated oncogenes for cases with breast cancer, considering the complicated pathogenesis of breast cancer.Methods: We first utilized the bioinformatics approach to investigate the role of the DAPL1 (death-associated protein-like 1) in breast cancer, based on the available datasets of TCGA and GEO.Results: DAPL1 is lowly expressed in breast cancer tissues compared with the normal tissues. For the breast cancer cases of the TCGA-BRCA cohort, we observed a correlation between lowly expressed DAPL1 gene and poor clinical prognosis of overall survival ( P =0.0028). Based on the survival data of GEO, the low DAPL1 expression was associated with a poor prognosis of distant metastasis free survival ( P =0.0023), and relapse free survival ( P =0.0065). DAPL1 expression was linked to the mutation status or copy number variation o f several genes, such as MAP3K1 , NUP98 , and CCDC59. The infiltration level of immune cells (e.g., M1 macrophage, Follicular B helper T cells, etc.) may be involved in the etiology of breast cancer. Based on the DAPL1 -correlated genes, GSEA, GO, and KEGG analysis data indicated the association between DAPL1 expression and a series of biological issues, such as DNA packaging complex, DNA repair complex, nucleotide excision repair, ubiquitin-like protein binding, and ubiquitin proteasome pathway. We also identified several DAPL1 -associated phosphorylation kinases, such as MAPK, PRKACA, and GSK3B.Conclusions: DAPL1 gene is first identified as a prognosis biomarker of breast cancer, and the underlying molecular mechanism involves protein phosphorylation, immune cell infiltration, and DNA repair or protein ubiquitin-associated cellular pathways.

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“…The Cancer Genome Atlas (TCGA) provides valuable RNA expression data from diseased patients which assists scientists to identify novel molecular targets and cancer biomarkers [ 30 ]. Here, we correlated the changes in protein expression obtained from our experimental model with RNA data of HCC patients and asked for those genes with a significant impact on HCC patient’s overall survival [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Transforming Growth Factor-β Drives the Transendothelial Migration of Hepatocellular Carcinoma Cells

Koudelková

Costina

Weber

et al. 2017

IJMS

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The entry of malignant hepatocytes into blood vessels is a key step in the dissemination and metastasis of hepatocellular carcinoma (HCC). The identification of molecular mechanisms involved in the transmigration of malignant hepatocytes through the endothelial barrier is of high relevance for therapeutic intervention and metastasis prevention. In this study, we employed a model of hepatocellular transmigration that mimics vascular invasion using hepatic sinusoidal endothelial cells and malignant hepatocytes evincing a mesenchymal-like, invasive phenotype by transforming growth factor (TGF)-β. Labelling of respective cell populations with various stable isotopes and subsequent mass spectrometry analyses allowed the “real-time” detection of molecular changes in both transmigrating hepatocytes and endothelial cells. Interestingly, the proteome profiling revealed 36 and 559 regulated proteins in hepatocytes and endothelial cells, respectively, indicating significant changes during active transmigration that mostly depends on cell–cell interaction rather than on TGF-β alone. Importantly, matching these in vitro findings with HCC patient data revealed a panel of common molecular alterations including peroxiredoxin-3, epoxide hydrolase, transgelin-2 and collectin 12 that are clinically relevant for the patient’s survival. We conclude that hepatocellular plasticity induced by TGF-β is crucially involved in blood vessel invasion of HCC cells.

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Identification of Biomarkers for Breast Cancer Using Databases

Cited by 23 publications

References 76 publications

Tumor Heterogeneity in Breast Cancer

Tumor Heterogeneity in Breast Cancer

WITHDRAWN: Identification of death-associated protein-like 1 (DAPL1) as a novel prognostic biomarker of breast cancer

Transforming Growth Factor-β Drives the Transendothelial Migration of Hepatocellular Carcinoma Cells

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