scholarly journals MKL1 is a key biomarker correlated with immune infiltrates and chemosensitivity in breast cancer

2021 ◽  
Author(s):  
Yijia Hua ◽  
Mengzhu Yang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Immune Cells ◽  
Cancer Biology ◽  
Drug Sensitivity ◽  
Regulation Of Gene Expression ◽  
Sensitivity Analyses ◽  
Breast Cancer Dataset ◽  
Potential Biomarker ◽  
Low Expression

Megakaryocytic leukemia 1 (MKL1) acts as a transcription factor in the regulation of the immune system and is associated with cancer biology. However, its function in the infiltrating immune cells in breast cancer has not been explored. Our study aimed to analyze the expression of MKL1 in The Cancer Genome Atlas (TCGA) breast cancer dataset. The aim of this study was to evaluate the correlations between MKL1 expression, infiltrating immune cells, and immune control genes. Enriched signaling pathways and drug sensitivity analyses were also performed. Our results indicate that high MKL1 expression could predict better survival in breast cancer patients. MKL1 expression was associated with the expression and function of different immune cells, including T cells, B cells, natural killer (NK) cells, macrophages, neutrophils and dendritic cells (DCs). The chromatin modifying enzymes, cellular senescence, epigenetic regulation of gene expression, estrogen-dependent gene expression, and chromosome maintenance were differentially enriched in MKL1 low expression phenotype. Patients in the high MKL1 expression group showed sensitivity to paclitaxel, while those in the low expression group showed potential sensitivity for cisplatin and docetaxel. In conclusion, MKL1 might act as a potential biomarker of prognostic value for immune infiltration and drug sensitivity in breast cancer.

scholarly journals Network analysis allows to unravel breast cancer molecular features and to identify novel targets

2019 ◽  
Author(s):  
Aurora Savino ◽  
Lidia Avalle ◽  
Emanuele Monteleone ◽  
Irene Miglio ◽  
Alberto Griffa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Biology ◽  
Gene Networks ◽  
High Reliability ◽  
Expression Patterns ◽  
Transcriptional Networks ◽  
Specific Gene ◽  
Breast Cancer Dataset ◽  
Molecular Features

AbstractThe behaviour of complex biological systems is determined by the orchestrated activity of many components interacting with each other, and can be investigated by networks. In particular, gene co-expression networks have been widely used in the past years thanks to the increasing availability of huge gene expression databases. Breast cancer is a heterogeneous disease usually classified either according to immunohistochemical features or by expression profiling, which identifies the 5 subtypes luminal A, luminal B, basal-like, HER2-positive and normal-like. Basal-like tumours are the most aggressive subtype, for which so far no targeted therapy is available.Making use of the WGCNA clustering method to reconstruct breast cancer transcriptional networks from the METABRIC breast cancer dataset, we developed a platform to address specific questions related to breast cancer biology. In particular, we obtained gene modules significantly correlated with survival and age of onset, useful to understand how molecular features and gene expression patterns are organized in breast cancer. We next generated subtype-specific gene networks and in particular identified two modules that are significantly more connected in basal-like breast cancer with respect to all other subtypes, suggesting relevant biological functions. We demonstrate that network centrality (kWithin) is a suitable measure to identify relevant genes, since we could show that it correlates with clinical features and that it provides a mean to select potential upstream regulators of a module with high reliability. Finally, we showed the feasibility of adding meaning to the networks by combining them with independently obtained data related to activated pathways.In conclusion, our platform allows to identify groups of genes highly relevant in breast cancer and possibly amenable to drug targeting, due to their ability to regulate survival-related gene networks. This approach could be successfully extended to other BC subtypes, and to all tumor types for which enough expression data are available.

scholarly journals A Novel Role for Cathepsin S as a Potential Biomarker in Triple Negative Breast Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Richard D. A. Wilkinson ◽  
Roberta E. Burden ◽  
Sara H. McDowell ◽  
Darragh G. McArt ◽  
Stephen McQuaid ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Damage ◽  
Adjuvant Therapy ◽  
Dna Damage Repair ◽  
Cathepsin S ◽  
Damage Repair ◽  
Potential Biomarker ◽  
Repair Pathways ◽  
Improved Outcome

Cathepsin S (CTSS) has previously been implicated in a number of cancer types, where it is associated with poor clinical features and outcome. To date, patient outcome in breast cancer has not been examined with respect to this protease. Here, we carried out immunohistochemical (IHC) staining of CTSS using a breast cancer tissue microarray in patients who received adjuvant therapy. We scored CTSS expression in the epithelial and stromal compartments and evaluated the association of CTSS expression with matched clinical outcome data. We observed differences in outcome based on CTSS expression, with stromal-derived CTSS expression correlating with a poor outcome and epithelial CTSS expression associated with an improved outcome. Further subtype characterisation revealed high epithelial CTSS expression in TNBC patients with improved outcome, which remained consistent across two independent TMA cohorts. Furtherin silicogene expression analysis, using both in-house and publicly available datasets, confirmed these observations and suggested high CTSS expression may also be beneficial to outcome in ER-/HER2+ cancer. Furthermore, high CTSS expression was associated with the BL1 Lehmann subgroup, which is characterised by defects in DNA damage repair pathways and correlates with improved outcome. Finally, analysis of matching IHC analysis reveals an increased M1 (tumour destructive) polarisation in macrophage in patients exhibiting high epithelial CTSS expression. In conclusion, our observations suggest epithelial CTSS expression may be prognostic of improved outcome in TNBC. Improved outcome observed with HER2+ at the gene expression level furthermore suggests CTSS may be prognostic of improved outcome in ER- cancers as a whole. Lastly, from the context of these patients receiving adjuvant therapy and as a result of its association with BL1 subgroup CTSS may be elevated in patients with defects in DNA damage repair pathways, indicating it may be predictive of tumour sensitivity to DNA damaging agents.

scholarly journals MMP1, MMP9, and COX2 Expressions in Promonocytes Are Induced by Breast Cancer Cells and Correlate with Collagen Degradation, Transformation-Like Morphological Changes in MCF-10A Acini, and Tumor Aggressiveness

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
G. K. Chimal-Ramírez ◽  
N. A. Espinoza-Sánchez ◽  
D. Utrera-Barillas ◽  
L. Benítez-Bribiesca ◽  
J. R. Velázquez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Morphological Changes ◽  
Tumor Stroma ◽  
Collagen Degradation ◽  
Tumor Aggressiveness ◽  
Soluble Factors ◽  
Immune Effector

Tumor-associated immune cells often lack immune effector activities, and instead they present protumoral functions. To understand how tumors promote this immunological switch, invasive and noninvasive breast cancer cell (BRC) lines were cocultured with a promonocytic cell line in a Matrigel-based 3D system. We hypothesized that if communication exists between tumor and immune cells, coculturing would result in augmented expression of genes associated with tumor malignancy. Upregulation of proteasesMMP1andMMP9and inflammatoryCOX2genes was found likely in response to soluble factors. Interestingly, changes were more apparent in promonocytes and correlated with the aggressiveness of the BRC line. Increased gene expression was confirmed by collagen degradation assays and immunocytochemistry of prostaglandin 2, a product of COX2 activity. Untransformed MCF-10A cells were then used as a sensor of soluble factors with transformation-like capabilities, finding that acini formed in the presence of supernatants of the highly aggressive BRC/promonocyte cocultures often exhibited total loss of the normal architecture. These data support that tumor cells can modify immune cell gene expression and tumor aggressiveness may importantly reside in this capacity. Modeling interactions in the tumor stroma will allow the identification of genes useful as cancer prognostic markers and therapy targets.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

Health economic analysis of breast cancer index in patients with ER+, LN- breast cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11504-e11504
Author(s):  
Gary Gustavsen ◽  
Brock Schroeder ◽  
Patrick Kennedy ◽  
Kristin Ciriello Pothier ◽  
Catherine A. Schnabel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Adjuvant Chemotherapy ◽  
Endocrine Therapy ◽  
Cost Savings ◽  
Cost Utility ◽  
Sensitivity Analyses ◽  
Breast Cancer Index ◽  
Extended Endocrine Therapy ◽  
The Cost

e11504 Background: Numerous studies have demonstrated the cost utility of gene expression-based assessment of recurrence risk in breast cancer. Cost savings rely primarily on decreased use of adjuvant chemotherapy in patients predicted to be low-risk. Breast Cancer Index (BCI) is a gene expression-based test that significantly predicts overall risk of recurrence, late (≥5y) recurrence and likelihood of benefit from extended (≥5y) endocrine therapy in patients with ER+, LN- breast cancer. This study evaluated the potential cost utility of BCI from a US third-party payer perspective. Methods: A fact-based economic model was developed which projected the cost and effectiveness of BCI in a hypothetical population of patients with ER+, LN- breast cancer compared to standard clinicopathologic diagnostic modalities. Patients flowed through the model based on patterns of care and BCI data. Costs associated with adjuvant chemotherapy, toxicity, follow-up, endocrine therapy, and recurrence were modeled over 10 yrs. Model inputs were based primarily on published literature, and supplemented by interviews with disease experts and payers. Sensitivity analyses were performed around key inputs to estimate effects on the model. Results: Use of BCI is projected to be cost saving in this patient population, with a net cost savings of $4,005 per patient tested after accounting for BCI cost. Gross cost savings were projected to be achieved through targeted use of adjuvant chemotherapy ($5,785), reduced recurrence in patients receiving extended endocrine therapy based on BCI ($2,350), and reduced recurrence in previously non-compliant patients ($370). Sensitivity analyses demonstrated that results were most sensitive to chemotherapy utilization in low- and intermediate-risk patients, cost of adjuvant chemotherapy, cost of recurrence, and percentage of patients classified as low risk. Conclusions: BCI is projected to be cost saving in an ER+, LN-, breast cancer patient population. Cost savings are achieved through projected impact on adjuvant chemotherapy use, extended endocrine therapy use, and endocrine therapy compliance. These findings require validation in additional cohorts, including studies of real-world clinical practice.

scholarly journals LIN28A modulates splicing and gene expression programs in breast cancer cells

2015 ◽  
pp. MCB.00426-15 ◽  
Author(s):  
Jun Yang ◽  
Brian D. Bennett ◽  
Shujun Luo ◽  
Kaoru Inoue ◽  
Sara A. Grimm ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Cells ◽  
Cancer Biology ◽  
Breast Cancer Cells ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Breast Cancer Subtype ◽  
Computational Method ◽  
Hnrnp A1

LIN28 is an evolutionarily conserved RNA-binding protein with critical functions in developmental timing and cancer. However molecular mechanisms underlying LIN28's oncogenic properties are yet to be described. RIP-Seq analysis revealed significant LIN28 binding within 843 mRNAs in breast cancer cells. Many of the LIN28 bound mRNAs are implicated in the regulation of RNA and cell metabolism. We identify heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1), a protein with multiple roles in mRNA metabolism, as a LIN28 interacting partner. Subsequently, we use a custom computational method to identify differentially spliced gene isoforms in LIN28 and hnRNP A1 siRNA-treated cells. Results reveal these proteins regulate alternative splicing and steady state mRNA expression of genes implicated in aspects of breast cancer biology. Notably, cells lacking LIN28 undergo significant isoform switching of the ENAH gene, resulting in a decrease in the expression of ENAH exon 11a isoform. Expression of ENAH isoform 11a has been shown to be elevated in breast cancers that express HER2. Intriguingly, analysis of publicly available TCGA array data reveals LIN28 expression is significantly different in HER2 compared to other breast cancer subtypes. Collectively, our data suggests that LIN28 may regulate splicing and gene expression programs that drive breast cancer subtype phenotypes.

scholarly journals The Proteomic Profile of Deleted in Breast Cancer 1 (DBC1) Interactions Points to a Multifaceted Regulation of Gene Expression

2015 ◽  
Vol 15 (3) ◽  
pp. 791-809 ◽  
Author(s):  
Sophie S. B. Giguère ◽  
Amanda J. Guise ◽  
Pierre M. Jean Beltran ◽  
Preeti M. Joshi ◽  
Todd M. Greco ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regulation Of Gene Expression ◽  
Proteomic Profile

Regulation of gene expression in T-47D human breast cancer cells by progestins and antiprogestins

1994 ◽  
Vol 9 (suppl 1) ◽  
pp. 174-180 ◽  
Author(s):  
L. C. Murphy ◽  
M. Alkhalaf ◽  
H. Dotzlaw ◽  
A. Coutts ◽  
B. Haddad-Alkhalaf
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Cells ◽  
Human Breast Cancer ◽  
Breast Cancer Cells ◽  
Regulation Of Gene Expression ◽  
Human Breast Cancer Cells ◽  
Human Breast

scholarly journals Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Wei Hu ◽  
Mingyue Li ◽  
Qi Zhang ◽  
Chuan Liu ◽  
Xinmei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Copy Number ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Breast Cancer Dataset ◽  
Cox Analysis

Abstract Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

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