scholarly journals E3 Ubiquitin Ligase NEDD4L Negatively Regulates Breast Cancer Metastasis By Downregulating SNAI2

2021 ◽  
Author(s):  
Baile Zuo ◽  
Ying Lan ◽  
Xiaoyan Li ◽  
Liping Zhao ◽  
Kexin Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ubiquitin Ligase ◽  
Prognostic Model ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Cox Regression ◽  
E3 Ubiquitin Ligase ◽  
Enrichment Analysis ◽  
Breast Cancer Metastasis ◽  
Independent Predictive Factor

Abstract Background Accumulating evidence demonstrated that the abnormal expression of E3-ubiquitin ligase NEDD4L plays an important role in the biological process of carcinomas. However, its role in breast cancer (BRCA) remains elusive. Methods The expression of NEDD4L was analyzed using BRCA datasets from public database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for enrichment analysis. Both transwell assay and pulmonary metastasis model of BRCA were used to detect metastatic ability of cells. Western blotting, immunohistochemistry and immunofluorescence assay were used to detect the protein expression of target genes. The riskScore and nomogram were used to evaluate the prognosis of patients. Results NEDD4L was significantly downregulated in cancer tissues and positively correlated with the overall survival of patients. Knocking down NEDD4L could enhance the migration and metastasis ability of BRCA cells. SP1 promotes NEDD4L expression, resulting in SNAI2 downregulation in BRCA. A NEDD4L related to the prognostic model developed by LASSO Cox regression could be an independent predictive factor for BRCA. A nomogram combining riskScore and clinical indicators was established to evaluate the prognosis of BRCA quantitatively. Conclusions This study first reveals the role of the SP1/NEDD4L/SNAI2 axis in BRCA and establishes a reliable prognostic model, which provides a novel target and basis for clinical treatment and prognosis evaluation of breast cancer.

scholarly journals In silico Analysis of Combinatorial microRNA Activity Reveals Target Genes and Pathways Associated with Breast Cancer Metastasis

2011 ◽  
Vol 10 ◽  
pp. CIN.S6631 ◽  
Author(s):  
Alan A. Dombkowski ◽  
Zakia Sultana ◽  
Douglas B. Craig ◽  
Hasan Jamil
Keyword(s):  
Breast Cancer ◽  
Cancer Metastasis ◽  
Target Genes ◽  
In Silico Analysis ◽  
Metastatic Potential ◽  
Breast Cancer Metastasis ◽  
Computational Method ◽  
Cytoskeleton Organization ◽  
Cellular Processes ◽  
Therapeutic Development

Aberrant microRNA activity has been reported in many diseases, and studies often find numerous microRNAs concurrently dysregulated. Most target genes have binding sites for multiple microRNAs, and mounting evidence indicates that it is important to consider their combinatorial effect on target gene repression. A recent study associated the coincident loss of expression of six microRNAs with metastatic potential in breast cancer. Here, we used a new computational method, miR-AT!, to investigate combinatorial activity among this group of microRNAs. We found that the set of transcripts having multiple target sites for these microRNAs was significantly enriched with genes involved in cellular processes commonly perturbed in metastatic tumors: cell cycle regulation, cytoskeleton organization, and cell adhesion. Network analysis revealed numerous target genes upstream of cyclin D1 and c-Myc, indicating that the collective loss of the six microRNAs may have a focal effect on these two key regulatory nodes. A number of genes previously implicated in cancer metastasis are among the predicted combinatorial targets, including TGFB1, ARPC3, and RANKL. In summary, our analysis reveals extensive combinatorial interactions that have notable implications for their potential role in breast cancer metastasis and in therapeutic development.

scholarly journals 4543 Glucocorticoid Receptors are essential effectors of TGFβ signaling in Triple Negative Breast Cancer

2020 ◽  
Vol 4 (s1) ◽  
pp. 7-8
Author(s):  
Carlos Jesus Perez Kerkvliet ◽  
Amy R Dwyer ◽  
Caroline Diep ◽  
Robert Oakley ◽  
Christopher Liddle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
P38 Mapk ◽  
Cell Biology ◽  
Glucocorticoid Receptors ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Mapk Signaling ◽  
Breast Cancer Metastasis ◽  
Tnbc Cell

OBJECTIVES/GOALS: The glucocorticoid receptor (GR) is a ubiquitous steroid hormone receptor that is emerging as a mediator of breast cancer metastasis. We aim to better understand the biology associated with phospho-GR species in TNBC and their contribution to tumor progression. METHODS/STUDY POPULATION: To better understand how p-S134 GR may impact TNBC cell biology, we probed GR regulation by soluble factors that are rich within the tumor microenvironment (TME), such as TGFβ. TNBC cells harboring endogenous wild-type or S134A-GR species were created by CRISPR/Cas knock-in and subjected to in vitro assays of advanced cancer behavior. RNA-Seq was employed to identify pS134-GR target genes that are uniquely regulated by TGFβ in the absence of exogenously added GR ligands. Direct regulation of selected TGFβ-induced pS134-GR target genes was validated accordingly. Bioinformatics tools were used to probe publicly available TNBC patient data sets for expression of a pS134-GR 24-gene signature. RESULTS/ANTICIPATED RESULTS: In the absence of GR ligands, GR is transcriptionally activated via p38-MAPK-dependent phosphorylation of Ser134 upon exposure of TNBC cells to TME-derived agents (TGFβ, HGF). The ligand-independent pS134-GR transcriptome primarily encompasses gene sets associated with TNBC cell survival and migration/invasion. Accordingly, pS134-GR was essential for TGFβ-induced TNBC cell migration, anchorage-independent growth in soft-agar, and tumorsphere formation, an in vitro readout of breast cancer stemness properties. Finally, a 24-gene pSer134-GR-dependent signature induced by TGFβ1 predicts shortened survival in breast cancer. We expect to find similar results using an in-house tissue microarray. DISCUSSION/SIGNIFICANCE OF IMPACT: Phospho-S134-GR is a critical downstream mediator of p38 MAPK signaling and TNBC migration, survival, and stemness properties. Our studies define GR as a required effector of TGFβ1 signaling and nominate pS134-GR as a biomarker of elevated risk of breast cancer dissemination.

Factors associated with mortality after breast cancer metastasis.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 174-174
Author(s):  
S. Y. Jung ◽  
M. Q. Rosenzweig ◽  
S. M. Sereika ◽  
F. Linkov ◽  
A. Brufsky ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Metastatic Breast Cancer ◽  
Cancer Patients ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Breast Cancer Metastasis ◽  
Her2 Status ◽  
Breast Cancer Patients

174 Background: It is generally accepted that patients with breast cancer metastases have poor survival. Metastatic breast cancer patients can be considered a heterogeneous population with a varied clinical course, which underscores the need for accurate prediction of survival based on prognostic factors. The purpose of the present study was to identify factors related to survival in breast cancer patients after diagnosis with metastatic disease. Methods: A total of 557 patients with breast cancer metastasis diagnosis seen at one large urban practice have been followed up between January 1, 1999 and June 30, 2008. Demographic, tumor characteristics, clinical factors as predictors of survival were analyzed using Cox regression model. Results: The median survival length was 40 months (range 1-114 months) with 269 (48.3%) alive and 288 (51.7%) dead. This study demonstrated that hypertension, estrogen receptor (ER) and/or progesterone receptor (PR) status, human epidermal growth factor receptor-2 (HER2) status, number of metastatic sites, and body mass index (BMI) at diagnosis with metastatic breast cancer were the most relevant prognostic factors for survival after metastasis. Conclusions: Findings of this study may form a foundation for the corpus of knowledge explaining the outcome differences in treatment of patients with metastatic breast cancer, potentially helping to create tailored counseling and personalized treatment approaches for this vulnerable group. [Table: see text]

scholarly journals Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Nan Wang ◽  
Yuanting Gu ◽  
Jiangrui Chi ◽  
Xinwei Liu ◽  
Youyi Xiong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Network ◽  
Prognostic Model ◽  
Triple Negative ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Gene Set Enrichment ◽  
Qrt Pcr

Background: Triple-negative breast cancer (TNBC) is a special subtype of breast cancer with poor prognosis. DNA damage response (DDR) is one of the hallmarks of this cancer. However, the association of DDR genes with the prognosis of TNBC is still unclear.Methods: We identified differentially expressed genes (DEGs) between normal and TNBC samples from The Cancer Genome Atlas (TCGA). DDR genes were obtained from the Molecular Signatures Database through six DDR gene sets. After the expression of six differential genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR), we then overlapped the DEGs with DDR genes. Based on univariate and LASSO Cox regression analyses, a prognostic model was constructed to predict overall survival (OS). Kaplan–Meier analysis and receiver operating characteristic curve were used to assess the performance of the prognostic model. Cox regression analysis was applied to identify independent prognostic factors in TNBC. The Human Protein Atlas was used to study the immunohistochemical data of six DEGs. The prognostic model was validated using an independent dataset. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA regulatory network.Results: Twenty-three differentially expressed DDR genes were detected between TNBC and normal samples. The six-gene prognostic model we developed was shown to be related to OS in TNBC using univariate and LASSO Cox regression analyses. All the six DEGs were identified as significantly up-regulated in the tumor samples compared to the normal samples in qRT-PCR. The GSEA analysis indicated that the genes in the high-risk group were mainly correlated with leukocyte migration, cytokine interaction, oxidative phosphorylation, autoimmune diseases, and coagulation cascade. The mutation data revealed the mutated genes were different. The gene-TF regulatory network showed that Replication Factor C subunit 4 occupied the dominant position.Conclusion: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.

scholarly journals E2F1 Drives Breast Cancer Metastasis by Regulating the Target Gene FGF13 and Altering Cell Migration

2019 ◽  
Author(s):  
Daniel P. Hollern ◽  
Matthew R. Swiatnicki ◽  
Jonathan P. Rennhack ◽  
Sean A. Misek ◽  
Brooke C. Matson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Migration ◽  
Pulmonary Metastasis ◽  
Target Gene ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Cancer Cell Line ◽  
Breast Cancer Metastasis ◽  
E2f Transcription Factors

ABSTRACTIn prior work we demonstrated that loss of E2F transcription factors inhibits metastasis. Here we address the mechanisms for this phenotype and identify the E2F regulated genes that coordinate tumor cell metastasis. Transcriptomic profiling of E2F1 knockout tumors identified a role for E2F1 as a master regulator of a suite of pro-metastatic genes, but also uncovered E2F1 target genes with an unknown role in pulmonary metastasis. High expression of one of these genes, Fgf13, is associated with early human breast cancer metastasis in a clinical dataset. Together these data led to the hypothesis that Fgf13 is critical for breast cancer metastasis, and that upregulation of Fgf13 may partially explain how E2F1 promotes breast cancer metastasis. To test this hypothesis we ablated Fgf13 via CRISPR. Deletion of Fgf13 in a MMTV-PyMT breast cancer cell line reduces the frequency of pulmonary metastasis. In addition, loss of Fgf13 reduced in vitro cell migration, suggesting that Fgf13 may be critical for tumor cells to invade out of and escape the primary tumor. The significance of this work is twofold: we have both uncovered genomic features by which E2F1 regulates metastasis and we have identified new pro-metastatic functions for the E2F1 target gene Fgf13.

scholarly journals Evaluation of the TRIP13 level in breast cancer and insights into potential molecular pathways

2020 ◽  
Author(s):  
Jin Lan ◽  
Jingzhan Huang ◽  
Yuan Gao ◽  
Jingbo Sun ◽  
Longshan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Therapeutic Target ◽  
Cancer Metastasis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Breast Cancer Metastasis ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Kaplan Meier

Abstract Background: TRIP13 is a member belonging to a large AAA+ ATPases protein super family. Emerging evidences had shown that TRIP13 may serve as an oncogene However, the function of TRIP13 in BC has not yet been elucidated. Methods: By utilizing the multiple database across BC, we presented the expression of TRIP13 in BC tissue and normal control. We then verified the expression of TRIP13 in patients with BC by immunohistochemical (IHC) staining. Kaplan-Meier plots were used to perform the survival analysis. Further, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and PPI (protein-protein interaction) network were performed to explore the biological function and potential regular pathway of TRIP13 in BC. Results: The multiple database and immunohistochemical (IHC) showed that higher TRIP13 expression in BC tissue compared to normal tissue. TRIP13 was highly exprssed in lung metastasis lesion compared with primary tumor in our BALB/C mice 4T1 BC models. Kaplan-Meier plots also revealed that high TRIP13 expression correlated to poor survival in patients with BC. Moreover, GSEA analysis revealed that TRIP13 was primarily enriched in the processes of cell division and proliferation. Finally 10 hub genes with a high score of connectivity were filtered from the PPI network, including MAD2L1, CDC20, CDC5L, CDK1, CCNA2, BUB1B, RAD51, SPO11, KIF11 and AURKB. Conclusion: High TRIP13 expression predicted poor prognosis and contributed tumor growth and metastasis in the BC. Thus, ARL3 may be a prognostic marker and therapeutic target for glioma. TRIP13 may be a favorable biomarker and effective therapeutic target for BC. Keywords: TRIP13; breast cancer; metastasis; bioinformatic analysis, prognosis

scholarly journals Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics

2021 ◽  
Author(s):  
Nan Wang ◽  
Yuanting Gu ◽  
Jiangrui Chi ◽  
Xinwei Liu ◽  
Youyi Xiong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Damage ◽  
Regulatory Network ◽  
Prognostic Model ◽  
Triple Negative ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Gene Set Enrichment

Abstract Background: Triple-negative breast cancer (TNBC) is a special subtype of breast cancer with poor prognosis. DNA damage response (DDR) is one of the hallmarks of this cancer. However, the association of DDR genes with the prognosis of TNBC is still unclear.Methods: We identified differentially expressed genes (DEGs) between normal and TNBC samples from The Cancer Genome Atlas (TCGA). DDR genes were obtained from the Molecular Signatures Database (MSigDB) through six DDR gene sets. We then overlapped the DEGs with DDR genes. Based on univariate and LASSO Cox regression analyses, a prognostic model was constructed to predict overall survival (OS). Kaplan–Meier (K–M) analysis and receiver operating characteristic (ROC) curve were used to assess the performance of the prognostic model. Cox regression analysis was applied to identify independent prognostic factors in TNBC. The prognostic model was validated using an independent dataset. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis (ssGSEA) was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA (ceRNA) regulatory network.Results: 23 differentially expressed DDR genes were detected between TNBC and normal samples. The six-gene prognostic model we developed was shown to be related to OS in TNBC using univariate and LASSO Cox regression analyses. By drawing ROC curve and KM curve, we determined the effectiveness of the risk model. The prognostic value of the six-gene prognostic model was further validated using the GSE58812 dataset. The GSEA analysis indicated that the genes in the high-risk group were mainly correlated with leukocyte migration, cytokine interaction with cytokine receptors, oxidative phosphorylation, autoimmune diseases, and coagulation cascade. The mutation data revealed that the mutation frequency of the two groups was the same, while the mutated genes were different. The gene-TF regulatory network showed that Replication Factor C subunit 4 (RFC4) occupied the dominant position.Conclusion: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.

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