scholarly journals Identification of high-risk genes in triple-negative breast cancer by bioinformatics

2021 ◽  
Author(s):  
Lu Xiang ◽  
Caiping Chen ◽  
Guihong Ni ◽  
Min Tao
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Tissue ◽  
Target Genes ◽  
Triple Negative ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Risk Genes ◽  
Online Tool ◽  
Free Survival

Abstract Background Current research has failed to find a target gene for triple-negative breast cancer (TNBC), which has resulted in the treatment for TNBC being less effective than that for other types of breast cancer. Finding high-risk genes for TNBC by bioinformatics may help to identify target genes for TNBC. Methods The gene expression data of 4 chips (GSE7904, GSE31448, GSE45827, GSE65194) which contains of normal breast tissue and TNBC tissue were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) between normal breast tissue and TNBC tissue were identified. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed by the DAVID website. Protein-protein interaction network analysis of DEGs was carried out by the STRING website, and the results were imported into Cytoscape. Then, module analysis was carried out by using the MCODE app. The online tool of the Kaplan-Meier Plotter website was used to analyse associations between relapse-free survival (RFS) and the expression of genes obtained by MCODE, and the metastasis-free survival (MFS) data from GSE58812 were used for survival verification. The difference in the expression of the identified genes was verified by the online tool of the UALCAN website. Results There were 127 upregulated and 293 downregulated genes in the DEGs. The GO and KEGG analysis showed that the DEGs were particularly enriched in mitotic nuclear division, extracellular space, heparin binding, and ECM-receptor interaction. MCODE obtained a total of 47 genes in 4 gene clusters, 29 of which were related to RFS. Survival verification indicated that 14 out of 29 genes were related to MFS, namely, CCNB1, AURKB, KIF20A, BUB1B, DLGAP5, CXCL11, CXCL9, CXCL10, CXCL12, IGF1, FN1, CFD, SGO2 and CDCA5. Conclusions We identified 14 genes as the high-risk genes for TNBC. Further research on these genes may identify the target genes of TNBC.

scholarly journals Identification of High-risk Genes in Triple-negative Breast Cancer by Bioinformatics

2020 ◽  
Author(s):  
Xiang Lu ◽  
Caiping Chen ◽  
GuiHong Ni ◽  
Min Tao
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Tissue ◽  
Target Genes ◽  
Triple Negative ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Risk Genes ◽  
Online Tool ◽  
Free Survival

Abstract Background: Current research has failed to find a target gene for triple-negative breast cancer (TNBC), which has resulted in the treatment for TNBC being less effective than that for other types of breast cancer. Finding high-risk genes for TNBC by bioinformatics may help to identify target genes for TNBC.Methods: The gene expression data of 4 chips (GSE7904, GSE31448, GSE45827, GSE65194) which contains of normal breast tissue and TNBC tissue were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) between normal breast tissue and TNBC tissue were identified. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed by the DAVID website. Protein-protein interaction network analysis of DEGs was carried out by the STRING website, and the results were imported into Cytoscape. Then, module analysis was carried out by using the MCODE app. The online tool of the Kaplan-Meier Plotter website was used to analyse associations between relapse-free survival (RFS) and the expression of genes obtained by MCODE, and the metastasis-free survival (MFS) data from GSE58812 were used for survival verification. The difference in the expression of the identified genes was verified by the online tool of the UALCAN website. Results: There were 127 upregulated and 293 downregulated genes in the DEGs. The GO and KEGG analysis showed that the DEGs were particularly enriched in mitotic nuclear division, extracellular space, heparin binding, and ECM-receptor interaction. MCODE obtained a total of 47 genes in 4 gene clusters, 29 of which were related to RFS. Survival verification indicated that 14 out of 29 genes were related to MFS, namely, CCNB1, AURKB, KIF20A, BUB1B, DLGAP5, CXCL11, CXCL9, CXCL10, CXCL12, IGF1, FN1, CFD, SGO2 and CDCA5. Conclusions: We identified 14 genes as the high-risk genes for TNBC. Further research on these genes may identify the target genes of TNBC.

scholarly journals Genomic Changes in Normal Breast Tissue in Women at Normal Risk or at High Risk for Breast Cancer

2016 ◽  
Vol 10 ◽  
pp. BCBCR.S39384 ◽  
Author(s):  
David N. Danforth
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Breast Tissue ◽  
Sporadic Breast Cancer ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Carcinogenesis ◽  
Molecular Changes ◽  
Breast Tissues

Sporadic breast cancer develops through the accumulation of molecular abnormalities in normal breast tissue, resulting from exposure to estrogens and other carcinogens beginning at adolescence and continuing throughout life. These molecular changes may take a variety of forms, including numerical and structural chromosomal abnormalities, epigenetic changes, and gene expression alterations. To characterize these abnormalities, a review of the literature has been conducted to define the molecular changes in each of the above major genomic categories in normal breast tissue considered to be either at normal risk or at high risk for sporadic breast cancer. This review indicates that normal risk breast tissues (such as reduction mammoplasty) contain evidence of early breast carcinogenesis including loss of heterozygosity, DNA methylation of tumor suppressor and other genes, and telomere shortening. In normal tissues at high risk for breast cancer (such as normal breast tissue adjacent to breast cancer or the contralateral breast), these changes persist, and are increased and accompanied by aneuploidy, increased genomic instability, a wide range of gene expression differences, development of large cancerized fields, and increased proliferation. These changes are consistent with early and long-standing exposure to carcinogens, especially estrogens. A model for the breast carcinogenic pathway in normal risk and high-risk breast tissues is proposed. These findings should clarify our understanding of breast carcinogenesis in normal breast tissue and promote development of improved methods for risk assessment and breast cancer prevention in women.

scholarly journals A Gradient-Based Approach for Breast DCE-MRI Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
L. Losurdo ◽  
T. M. A. Basile ◽  
A. Fanizzi ◽  
R. Bellotti ◽  
U. Bottigli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
High Risk ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Resonance Imaging ◽  
Background Parenchymal Enhancement ◽  
Parenchymal Enhancement ◽  
Tissue Enhancement

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

scholarly journals Accelerated aging in normal breast tissue of women with breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Shoghag Panjarian ◽  
Jozef Madzo ◽  
Kelsey Keith ◽  
Carolyn M. Slater ◽  
Carmen Sapienza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Anomaly Detection ◽  
Breast Tissue ◽  
Preventive Intervention ◽  
Normal Breast Tissue ◽  
Accelerated Aging ◽  
Normal Breast ◽  
Age Related ◽  
Unsupervised Anomaly Detection

Abstract Background DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. Methods We generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation. Results We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer. Conclusions A subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer.

scholarly journals Targeted next-generation sequencing assays using triplet samples of normal breast tissue, primary breast cancer, and recurrent/metastatic lesions

2020 ◽  
Author(s):  
Toshiaki Akahane ◽  
Naoki Kanomata ◽  
Oi Harada ◽  
Tetsumasa Yamashita ◽  
Junichi Kurebayashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Primary Tumor ◽  
Primary Breast Cancer ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Metastatic Breast ◽  
Normal Breast ◽  
Human Breast ◽  
Metastatic Lesions ◽  
Generation Sequencing

Abstract Background: Next-generation sequencing (NGS) has shown that recurrent/metastatic breast cancer lesions may have additional genetic changes compared with the primary tumor. These additional changes may be related to tumor progression and/or drug resistance. However, breast cancer-targeted NGS is not still widely used in clinical practice to compare the genomic profiles of primary breast cancer and recurrent/metastatic lesions.Methods: Triplet samples of genomic DNA were extracted from each patient’s normal breast tissue, primary breast cancer, and recurrent/metastatic lesion(s). A DNA library was constructed using the QIAseq Human Breast Cancer Panel (93 genes, Qiagen) and then sequenced using MiSeq (Illumina). The Qiagen web portal was utilized for data analysis.Results: Successful results for three or four samples (normal breast tissue, primary tumor, and at least one metastatic/recurrent lesion) were obtained for 11 of 35 breast cancer patients with recurrence/metastases (36 samples). We detected shared somatic mutations in all but one patient, who had a germline mutation in TP53. Additional mutations that were detected in recurrent/metastatic lesions compared with primary tumor were in genes including TP53 (three patients) and one case each of ATR, BLM, CBFB, EP300, ERBB2, MUC16, PBRM1, and PIK3CA. Actionable mutations and/or copy number variations (CNVs) were detected in 73% (8/11) of recurrent/metastatic breast cancer lesions.Conclusions: The QIAseq Human Breast Cancer Panel assay showed that recurrent/metastatic breast cancers sometimes acquired additional mutations and CNV. Such additional genomic changes could provide therapeutic target.

scholarly journals DNA Methylation and Breast Cancer Risk: An Epigenome-Wide Study of Normal Breast Tissue and Blood

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3088 ◽  
Author(s):  
Kaoutar Ennour-Idrissi ◽  
Dzevka Dragic ◽  
Elissar Issa ◽  
Annick Michaud ◽  
Sue-Ling Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Epithelium ◽  
Normal Breast Epithelium ◽  
Differentially Methylated Genes

Differential DNA methylation is a potential marker of breast cancer risk. Few studies have investigated DNA methylation changes in normal breast tissue and were largely confounded by cancer field effects. To detect methylation changes in normal breast epithelium that are causally associated with breast cancer occurrence, we used a nested case–control study design based on a prospective cohort of patients diagnosed with a primary invasive hormone receptor-positive breast cancer. Twenty patients diagnosed with a contralateral breast cancer (CBC) were matched (1:1) with 20 patients who did not develop a CBC on relevant risk factors. Differentially methylated Cytosine-phosphate-Guanines (CpGs) and regions in normal breast epithelium were identified using an epigenome-wide DNA methylation assay and robust linear regressions. Analyses were replicated in two independent sets of normal breast tissue and blood. We identified 7315 CpGs (FDR < 0.05), 52 passing strict Bonferroni correction (p < 1.22 × 10−7) and 43 mapping to known genes involved in metabolic diseases with significant enrichment (p < 0.01) of pathways involving fatty acids metabolic processes. Four differentially methylated genes were detected in both site-specific and regions analyses (LHX2, TFAP2B, JAKMIP1, SEPT9), and three genes overlapped all three datasets (POM121L2, KCNQ1, CLEC4C). Once validated, the seven differentially methylated genes distinguishing women who developed and who did not develop a sporadic breast cancer could be used to enhance breast cancer risk-stratification, and allow implementation of targeted screening and preventive strategies that would ultimately improve breast cancer prognosis.

The role of low keV virtual monochromatic imaging in increasing the conspicuity of primary breast cancer in dual-energy spectral thoracic CT examination for staging purposes

2019 ◽  
Vol 61 (2) ◽  
pp. 168-174 ◽  
Author(s):  
Yavuz Metin ◽  
Nurgül Orhan Metin ◽  
Oğuzhan Özdemir ◽  
Filiz Taşçı ◽  
Sibel Kul
Keyword(s):  
Breast Cancer ◽  
Primary Breast Cancer ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Energy Levels ◽  
Iodine Content ◽  
Pectoral Muscle ◽  
Dual Energy ◽  
Normal Breast

Background The additive value of dual-energy spectral computerized tomography (DESCT) in breast cancer imaging is still unknown. Purpose To investigate the role of DESCT in improving the conspicuity of primary breast cancer. Material and Methods Twenty-nine patients who were histopathologically diagnosed with breast cancer and underwent DESCT for staging of lung metastasis were evaluated retrospectively. The visual conspicuity of breast cancer was scored by two readers separately in reconstructed virtual monochromatic images obtained at 40, 60, 80, and 100 keV. A circular region of interest slightly smaller than the maximum contrasted portion of the primary breast cancer was manually placed. Iodine enhancement (HU) and iodine content (mg/mL) values of tumor, normal breast tissue and pectoral muscle, and contrast-to-noise values of images at four different energy levels were calculated. Results The lesion conspicuity score peaked at 40-keV series for both readers and was significantly higher than those at other energy levels (all P < 0.001). Lesion iodine enhancement was highest at 40-keV virtual monochromatic image reconstructions ( P < 0.001). The iodine content was significantly higher in tumor than normal breast tissue, and pectoral muscle ( P < 0.001). The highest contrast-to-noise value was obtained at 60 keV (4.0 ± 2.5), followed by 40 keV (3.9 ± 2.2), without a significant difference ( P = 0.33). Conclusion The conspicuity of primary breast cancer was significantly higher in low keV virtual monochromatic images obtained by DESCT. This gives us hope that DESCT may play an effective role in detecting incidental breast lesions. It also raises the question of whether quantitative values obtained by DESCT can be used for characterization of primary breast lesion.

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