scholarly journals Identification of Potential Prognostic Biomarkers for Breast Cancer Based on lncRNA-TF-Associated ceRNA Network and Functional Module

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinrong Li ◽  
Junquan Zhu ◽  
Jian Qiu
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Risk Group ◽  
Normal Sample ◽  
Valuable Insight ◽  
Functional Modules ◽  
Cerna Network ◽  
Topological Features ◽  
Tumor Stages ◽  
Motif Searching

Breast cancer leads to most of cancer deaths among women worldwide. Systematically analyzing the competing endogenous RNA (ceRNA) network and their functional modules may provide valuable insight into the pathogenesis of breast cancer. In this study, we constructed a lncRNA-TF-associated ceRNA network via combining all the significant lncRNA-TF ceRNA pairs and TF-TF PPI pairs. We computed important topological features of the network, such as degree and average path length. Hub nodes in the lncRNA-TF-associated ceRNA network were extracted to detect differential expression in different subtypes and tumor stages of breast cancer. MCODE was used for identifying the closely connected modules from the ceRNA network. Survival analysis was further used for evaluating whether the modules had prognosis effects on breast cancer. TF motif searching analysis was performed for investigating the binding potentials between lncRNAs and TFs. As a result, a lncRNA-TF-associated ceRNA network in breast cancer was constructed, which had a scale-free property. Hub nodes such as MDM4, ZNF410, AC0842-19, and CTB-89H12 were differentially expressed between cancer and normal sample in different subtypes and tumor stages. Two closely connected modules were identified to significantly classify patients into a low-risk group and high-risk group with different clinical outcomes. TF motif searching analysis suggested that TFs, such as NFAT5, might bind to the promoter and enhancer regions of hub lncRNAs and function in breast cancer biology. The results demonstrated that the synergistic, competitive lncRNA-TF ceRNA network and their functional modules played important roles in the biological processes and molecular functions of breast cancer.

scholarly journals Development a Prognostic Model Integrating lncRNA/ mRNA novel Biomarkers Identified by Bioinformatics Analysis and Experiments in Breast Cancer

2021 ◽  
Author(s):  
Jinrong Wei ◽  
Qianshu Dou ◽  
Futing Ba ◽  
Guo-Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Breast Cancers ◽  
Rt Pcr ◽  
Cerna Network ◽  
Prognosis Model

Abstract Purpose: The purpose of this study is to established a prognosis model based on the expression profiles of lncRNAs and mRNAs for breast cancers.Methods: Single Variable Cox Proportional Risk Regression analysis and difference analysis were applied to screen survival-related and differently expressed lncRNAs and mRNAs between tumor and normal tissues from TCGA data. GO and KEGG analysis were applied for top 30 survival-related genes. LncRNA/mRNA co-expressed network was constructed based on correlation analysis. LASSO analysis and Multivariate Stepwise Cox Regression analysis were applied to establish the prognosis model. RT-PCR experiments were applied to verify the correctness of the analysis results. Relative components of the TME in breast cancers with high and low risk groups were analysed by xCell and Cox proportional risk regression analysis. The ceRNA network was constructed by calculating the Pearson correlation coefficient (PCC) for miRNA-mRNA and miRNA-lncRNA using paired miRNA, mRNA, and lncRNA expression profile data.Results:Venn diagrams showed that there were 60 genes and 54 lncRNAs that were differently expressed and related with survival. Through lncRNA/mRNA co-expressed network construction, 19 lncRNA and 16 mRNA hub genes were gained. The genes were then included in LASSO and multivariate Cox proportional hazard regression analysis, and finally, 3 lncRNAs (LINC01497, LINC02766, LINC02528) and 2 mRNAs (C20orf85, CST1) were selected as prognosis predictive genes. According to the median risk score of the 5 candidates, patients were divided into high-risk group and low-risk group. The results of RT-PCR were consistent with the analysis results. The proportions of Adipocytes, Endothelial cells, HSCs, Fibroblasts were significantly lower in low risk score tissues compared with the high risk score tissues, while the proportions of M1 macrophages, MSCs, Th2 cells were significantly higher. A lncRNA-miRNA-mRNA ceRNA network containing 3 lncRNAs, 2 mRNAs, and 158 miRNAs was finally constructed, preliminarily revealed a proper mechanism of the 5 molecules playing important roles in breast cancer progression and prognosis prediction.Conclusion: We found that LINC01497, LINC02766, LINC02528 and C20orf85, CST1 may serve as a powerful prognostic tool to optimize the prognosis evaluation system of breast cancer.

scholarly journals Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Wenjie Shi ◽  
Daojun Hu ◽  
Sen Lin ◽  
Rui Zhuo
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Protein Levels ◽  
Cerna Network

Background. The purpose of this study was to investigate the regulatory mechanisms of ceRNAs in breast cancer (BC) and construct a new five-mRNA prognostic signature. Methods. The ceRNA network was constructed by different RNAs screened by the edgeR package. The BC prognostic signature was built based on the Cox regression analysis. The log-rank method was used to analyse the survival rate of BC patients with different risk scores. The expression of the 5 genes was verified by the GSE81540 dataset and CPTAC database. Results. A total of 41 BC-adjacent tissues and 473 BC tissues were included in this study. A total of 2,966 differentially expressed lncRNAs, 5,370 differentially expressed mRNAs, and 359 differentially expressed miRNAs were screened. The ceRNA network was constructed using 13 lncRNAs, 267 mRNAs, and 35 miRNAs. Kaplan-Meier (K-M) methods showed that two lncRNAs (AC037487.1 and MIR22HG) are related to prognosis. Five mRNAs (VPS28, COL17A1, HSF1, PUF60, and SMOC1) in the ceRNA network were used to establish a prognostic signature. Survival analysis showed that the prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p=0.0022). ROC analysis showed that this signature has a good diagnostic ability (AUC=0.77). Compared with clinical features, this signature was also an independent prognostic factor (HR: 1.206, 95% CI 1.108−1.311; p<0.001). External verification results showed that the expression of the 5 mRNAs differed between the normal and tumour groups at the chip and protein levels (p<0.001). Conclusions. These ceRNAs may play a key role in the development of BC, and the new 5-mRNA prognostic signature can improve the prediction of survival for BC patients.

scholarly journals Identification of a novel lncRNA‑miRNA‑mRNA competing endogenous RNA network associated with prognosis of breast cancer

2020 ◽  
Author(s):  
Xiaohui Wan ◽  
Shuhong Hao ◽  
Chunmei Hu ◽  
Rongfeng Qu
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Cox Regression ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Target Mrna ◽  
Cerna Network

Abstract Background: Breast cancer is one of the most common malignant tumors. Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer has aroused great interext. However, research on the pathogenesis and biomarkers of breast cancer is still limited. Thus, this study is aimed to identify the competing endogenous RNA (ceRNA) network related to prognosis of patients with breast cancer. Methods: The RNA SEQ data and corresponding clinical information were downloaded from the Cancer Genome Atlas (TCGA) database, and the difference analysis was performed after data quality control. The similarity between two groups of genes with traits in the network was analyzed by weighted correlation network analysis (WGCNA) . Next, the interaction among lncRNA, miRNA, and mRNA was predicted using miRcode, TargetScan, miRDB, and miRTarBase database, and the lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, the survival model of target mRNA was established by Cox regression analysis. Results: In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to the breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed with 27 lncRNA, 14 miRNAs and 4 mRNAs. The AUC of four survival models of target mRNA (ZC3H12B + HRH1 + TMEM132C + PAG1) was 0.609, which suggested the sensitivity and specificity of prognosis prediction of breast cancer. Conclusion: This study provides insight into the ceRNA network involved in breast cancer biology, which significantly associated with gene regulation and prognosis of breast cancer.

Identification of biomarkers and functional modules from genomic data in stage-wise breast cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Athira K ◽  
Vrinda C ◽  
Sunil Kumar P V ◽  
Gopakumar G
Keyword(s):  
Breast Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Predictive Biomarkers ◽  
Stage I ◽  
Stage Ii ◽  
Stage Iii ◽  
Functional Modules ◽  
Incidence And Mortality ◽  
Multiple Stages

Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer. Objective: The objective of this study is to detect functionally significant modules from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer. Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer. Result and Discussion: predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer. Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.

The Impact of Translational Research in Breast Cancer Care: Can we Improve the Therapeutic Scenario?

2018 ◽  
Vol 18 (6) ◽  
pp. 832-836
Author(s):  
Giuseppe Buono ◽  
Francesco Schettini ◽  
Francesco Perri ◽  
Grazia Arpino ◽  
Roberto Bianco ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Translational Research ◽  
Cell Biology ◽  
Cancer Biology ◽  
Hormone Receptors ◽  
Treatment Strategies ◽  
Growth Factor Receptor ◽  
Molecular Heterogeneity ◽  
Therapeutic Implications ◽  
The Impact

Traditionally, breast cancer (BC) is divided into different subtypes defined by immunohistochemistry (IHC) according to the expression of hormone receptors and overexpression/amplification of human epidermal growth factor receptor 2 (HER2), with crucial therapeutic implications. In the last few years, the definition of different BC molecular subgroups within the IHC-defined subtypes and the identification of the important role that molecular heterogeneity can play in tumor progression and treatment resistance have inspired the search for personalized therapeutic approaches. In this scenario, translational research represents a key strategy to apply knowledge from cancer biology to the clinical setting, through the study of all the tumors “omics”, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics. Importantly, the introduction of new high-throughput technologies, such as next generation sequencing (NGS) for the study of cancer genome and transcriptome, greatly amplifies the potential and the applications of translational research in the oncology field. Moreover, the introduction of new experimental approaches, such as liquid biopsy, as well as new-concept clinical trials, such as biomarker-driven adaptive studies, may represent a turning point for BC translational research. </P><P> It is likely that translational research will have in the near future a significant impact on BC care, especially by giving us the possibility to dissect the complexity of tumor cell biology and develop new personalized treatment strategies.

Clinical impact of LncRNA XIST and LncRNA NEAT1 for diagnosis of high-risk group breast cancer patients

2021 ◽  
pp. 100709
Author(s):  
Menha Swellam ◽  
Hekmat M EL Magdoub ◽  
May A Shawki ◽  
Marwa Adel ◽  
Mona M Hefny ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
Clinical Impact ◽  
Breast Cancer Patients ◽  
Lncrna Xist ◽  
Lncrna Neat1

scholarly journals Collagen molecular phenotypic switch between non-neoplastic and neoplastic canine mammary tissues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masahiko Terajima ◽  
Yuki Taga ◽  
Becky K. Brisson ◽  
Amy C. Durham ◽  
Kotaro Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mammary Gland ◽  
Mammary Carcinoma ◽  
Cancer Biology ◽  
Type I Collagen ◽  
Critical Role ◽  
Premature Death ◽  
Mammary Tissue ◽  
Type I ◽  
Cross Links

AbstractIn spite of major advances over the past several decades in diagnosis and treatment, breast cancer remains a global cause of morbidity and premature death for both human and veterinary patients. Due to multiple shared clinicopathological features, dogs provide an excellent model of human breast cancer, thus, a comparative oncology approach may advance our understanding of breast cancer biology and improve patient outcomes. Despite an increasing awareness of the critical role of fibrillar collagens in breast cancer biology, tumor-permissive collagen features are still ill-defined. Here, we characterize the molecular and morphological phenotypes of type I collagen in canine mammary gland tumors. Canine mammary carcinoma samples contained longer collagen fibers as well as a greater population of wider fibers compared to non-neoplastic and adenoma samples. Furthermore, the total number of collagen cross-links enriched in the stable hydroxylysine-aldehyde derived cross-links was significantly increased in neoplastic mammary gland samples compared to non-neoplastic mammary gland tissue. The mass spectrometric analyses of type I collagen revealed that in malignant mammary tumor samples, lysine residues, in particular those in the telopeptides, were markedly over-hydroxylated in comparison to non-neoplastic mammary tissue. The extent of glycosylation of hydroxylysine residues was comparable among the groups. Consistent with these data, expression levels of genes encoding lysyl hydroxylase 2 (LH2) and its molecular chaperone FK506-binding protein 65 were both significantly increased in neoplastic samples. These alterations likely lead to an increase in the LH2-mediated stable collagen cross-links in mammary carcinoma that may promote tumor cell metastasis in these patients.

scholarly journals LOXL2 Inhibitors and Breast Cancer Progression

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 312
Author(s):  
Sandra Ferreira ◽  
Nuno Saraiva ◽  
Patrícia Rijo ◽  
Ana S. Fernandes
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Cancer Biology ◽  
Breast Cancer Cells ◽  
Lysyl Oxidase ◽  
Breast Cancer Progression ◽  
Special Focus ◽  
Amine Oxidases ◽  
Cross Link ◽  
Promising Strategy

LOX (lysyl oxidase) and lysyl oxidase like-1–4 (LOXL 1–4) are amine oxidases, which catalyze cross-linking reactions of elastin and collagen in the connective tissue. These amine oxidases also allow the cross-link of collagen and elastin in the extracellular matrix of tumors, facilitating the process of cell migration and the formation of metastases. LOXL2 is of particular interest in cancer biology as it is highly expressed in some tumors. This protein also promotes oncogenic transformation and affects the proliferation of breast cancer cells. LOX and LOXL2 inhibition have thus been suggested as a promising strategy to prevent metastasis and invasion of breast cancer. BAPN (β-aminopropionitrile) was the first compound described as a LOX inhibitor and was obtained from a natural source. However, novel synthetic compounds that act as LOX/LOXL2 selective inhibitors or as dual LOX/LOX-L inhibitors have been recently developed. In this review, we describe LOX enzymes and their role in promoting cancer development and metastases, with a special focus on LOXL2 and breast cancer progression. Moreover, the recent advances in the development of LOXL2 inhibitors are also addressed. Overall, this work contextualizes and explores the importance of LOXL2 inhibition as a promising novel complementary and effective therapeutic approach for breast cancer treatment.

scholarly journals Establishment and characterization of a new spontaneously immortalized ER−/PR−/HER2+ human breast cancer cell line, DHSF-BR16

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefania Nobili ◽  
Antonella Mannini ◽  
Astrid Parenti ◽  
Chiara Raggi ◽  
Andrea Lapucci ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Breast Cancer Cell Line ◽  
Cancer Biology ◽  
Cancer Cell Line ◽  
Cancer Tissue ◽  
Endoplasmic Reticulum Membrane ◽  
Mcf 7

AbstractInvasive ductal carcinoma (IDC) constitutes the most frequent malignant cancer endangering women’s health. In this study, a new spontaneously immortalized breast cancer cell line, DHSF-BR16 cells, was isolated from the primary IDC of a 74-years old female patient, treated with neoadjuvant chemotherapy and disease-free 5-years after adjuvant chemotherapy. Primary breast cancer tissue surgically removed was classified as ER−/PR−/HER2+, and the same phenotype was maintained by DHSF-BR16 cells. We examined DHSF-BR16 cell morphology and relevant biological and molecular markers, as well as their response to anticancer drugs commonly used for breast cancer treatment. MCF-7 cells were used for comparison purposes. The DHSF-BR16 cells showed the ability to form spheroids and migrate. Furthermore, DHSF-BR16 cells showed a mixed stemness phenotype (i.e. CD44+/CD24−/low), high levels of cytokeratin 7, moderate levels of cytokeratin 8 and 18, EpCAM and E-Cadh. Transcriptome analysis showed 2071 differentially expressed genes between DHSF-BR16 and MCF-7 cells (logFC > 2, p-adj < 0.01). Several genes were highly upregulated or downregulated in the new cell line (log2 scale fold change magnitude within − 9.6 to + 12.13). A spontaneous immortalization signature, mainly represented by extracellular exosomes-, plasma membrane- and endoplasmic reticulum membrane pathways (GO database) as well as by metabolic pathways (KEGG database) was observed in DHSF-BR16 cells. Also, these cells were more resistant to anthracyclines compared with MCF-7 cells. Overall, DHSF-BR16 cell line represents a relevant model useful to investigate cancer biology, to identify both novel prognostic and drug response predictive biomarkers as well as to assess new therapeutic strategies.

scholarly journals Aromatase inhibitors in breast cancer.

2004 ◽  
Vol 11 (2) ◽  
pp. 179-189 ◽  
Author(s):  
P E L√∏nning
Keyword(s):  
Breast Cancer ◽  
Aromatase Inhibitors ◽  
Cancer Biology ◽  
Postmenopausal Breast Cancer ◽  
Phase Iii ◽  
Cross Resistance ◽  
Consistent Difference ◽  
Clinical Effects ◽  
Endocrine Regulation

The development of aromatase inhibitors for breast cancer therapy is a result of successful translational research exploring the biochemical effects of different compounds in vivo. Studies assessing plasma oestrogen levels as well as in vivo aromatase inhibition have revealed a consistent difference with respect to biochemical efficacy between the third generation compounds (anastrozole, letrozole and exemestane) and the previous, first and second generation drugs, corresponding to the improved clinical effects of these compounds as outlined in large phase III studies. Thus, endocrine evaluation has been found to be a valid surrogate parameter for clinical efficacy. Moreover, the results from these studies have added important biological information to our understanding of endocrine regulation of breast cancer. Based on the clinical results so far, aromatase inhibitors are believed to play a key role in future adjuvant therapy of postmenopausal breast cancer patients and potentially also for breast cancer prevention. Interesting findings such as the lack of cross-resistance between steroidal and non-steroidal compounds should be further explored, as this may add additional information to our understanding of breast cancer biology.

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