scholarly journals A Histone Acetylation Modulator Gene Signature for Classification and Prognosis of Breast Cancer

2021 ◽  
Vol 28 (1) ◽  
pp. 928-939
Author(s):  
Mengping Long ◽  
Wei Hou ◽  
Yiqiang Liu ◽  
Taobo Hu
Keyword(s):  
Breast Cancer ◽  
Histone Acetylation ◽  
Molecular Subtypes ◽  
Gene Signature ◽  
Data Sources ◽  
Differentially Expressed ◽  
Breast Cancers ◽  
Acetylation Level ◽  
Close Relationship ◽  
Basal Like Breast Cancer

Regulators of histone acetylation are promising epigenetic targets for therapy in breast cancer. In this study, we comprehensively analyzed the expression of histone acetylation modulator genes in breast cancer using TCGA data sources. A gene signature composed of eight histone acetylation modulators (HAMs) was found to be effective for the classification and prognosis of breast cancers, especially in the HER2-enriched and basal-like molecular subtypes. The eight genes consist of two histone acetylation writers (GTF3C4 and CLOCK), two erasers (HDAC2 and SIRT7) and four readers (BRD4, BRD7, SP100, and BRWD3). Both histone acetylation writer genes and eraser genes were found to be differentially expressed between the two groups indicating a close relationship exists between overall histone acetylation level and prognosis of breast cancer in HER2-enriched and basal-like breast cancer.

scholarly journals Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jane Bayani ◽  
Coralie Poncet ◽  
Cheryl Crozier ◽  
Anouk Neven ◽  
Tammy Piper ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Male Breast Cancer ◽  
Univariate Analysis ◽  
Gene Signature ◽  
Male Breast ◽  
Risk Scores ◽  
Breast Cancers ◽  
Academic Risk ◽  
Genomic Grade Index ◽  
Prognostic Utility

AbstractMale breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

scholarly journals Triple negative aggressive phenotype controlled by miR-135b and miR-365: new theranostics candidates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gloria Bertoli ◽  
Claudia Cava ◽  
Fabio Corsi ◽  
Francesca Piccotti ◽  
Cristina Martelli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Triple Negative ◽  
Differentially Expressed ◽  
Tumor Control ◽  
Breast Cancers ◽  
Basal Breast Cancer ◽  
Therapeutic Molecules ◽  
Invasive Capacity ◽  
Tnbc Subtype

AbstractTriple negative breast cancer (TNBC) accounts for about a fifth of all breast cancers and includes a diverse group of cancers. The heterogeneity of TNBC and the lack of target receptors on the cell surface make it difficult to develop specific therapeutic treatments. These aspects cause the high negative prognosis of patients with this type of tumor. The analysis of the molecular profiles of TNBC samples has allowed a better characterization of this tumor, supporting the search for new reliable diagnostic markers. To this end, we have developed a bioinformatic approach to integrate networks of genes differentially expressed in basal breast cancer compared to healthy tissues, with miRNAs able to regulate their expression. We studied the role of these miRNAs in TNBC subtype cell lines. We therefore identified two miRNAs, namely miR-135b and miR-365, with a central role in regulating the altered functional pathways in basal breast cancer. These two miRNAs are differentially expressed in human TNBC immunohistochemistry-selected tissues, and their modulation has been shown to play a role in the proliferation of tumor control and its migratory and invasive capacity in TNBC subtype cell lines. From the perspective of personalized medicine, we managed to modulate the expression of the two miRNAs in organotypic cultures, suggesting their possible use as diagnostic and therapeutic molecules. miR-135b and miR-365 have a key role in TNBC, controlling proliferation and invasion. Their detection could be helpful in TNBC diagnosis, while their modulation could become a new therapeutic tool for TNBC.

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

scholarly journals Transcriptome profiles of stem-like cells from primary breast cancers allow identification of ITGA7 as a predictive marker of chemotherapy response

2021 ◽  
Author(s):  
Noha Gwili ◽  
Stacey J. Jones ◽  
Waleed Al Amri ◽  
Ian M. Carr ◽  
Sarah Harris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Predictive Marker ◽  
Therapy Resistance ◽  
Differentially Expressed ◽  
Molecular Profile ◽  
Chemotherapy Response ◽  
Breast Cancers

Abstract Background Breast cancer stem cells (BCSCs) are drivers of therapy-resistance, therefore are responsible for poor survival. Molecular signatures of BCSCs from primary cancers remain undefined. Here, we identify the consistent transcriptome of primary BCSCs shared across breast cancer subtypes, and we examine the clinical relevance of ITGA7, one of the genes differentially expressed in BCSCs. Methods Primary BCSCs were assessed using immunohistochemistry and fluorescently labelled using Aldefluor (n = 17). Transcriptomes of fluorescently sorted BCSCs and matched non-stem cancer cells were determined using RNA-seq (n = 6). ITGA7 expression was examined in breast cancers using immunohistochemistry (n = 305), and its functional role was tested using siRNA in breast cancer cells. Results Proportions of BCSCs varied from 0 to 9.4%. 38 genes were significantly differentially expressed in BCSCs; genes were enriched for functions in vessel morphogenesis, motility, and metabolism. ITGA7 was found to be significantly downregulated in BCSCs, and low expression significantly correlated with reduced survival in patients treated with chemotherapy, and with chemoresistance in breast cancer cells in vitro. Conclusions This study is the first to define the molecular profile of BCSCs from a range of primary breast cancers. ITGA7 acts as a predictive marker for chemotherapy response, in accordance with its downregulation in BCSCs.

scholarly journals A clinically relevant gene signature in triple negative and basal-like breast cancer

2011 ◽  
Vol 13 (5) ◽  
Author(s):  
Achim Rody ◽  
Thomas Karn ◽  
Cornelia Liedtke ◽  
Lajos Pusztai ◽  
Eugen Ruckhaeberle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Gene Signature ◽  
Basal Like Breast Cancer ◽  
Relevant Gene

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Selective Effects of Thioridazine on Self-Renewal of Basal-Like Breast Cancer Cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Matthew Tegowski ◽  
Cheng Fan ◽  
Albert S. Baldwin
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cells ◽  
Cell Types ◽  
Breast Cancer Cell Lines ◽  
Breast Cancers ◽  
Self Renewal ◽  
Basal Like Breast Cancer

AbstractSeveral recent publications demonstrated that DRD2-targeting antipsychotics such as thioridazine induce proliferation arrest and apoptosis in diverse cancer cell types including those derived from brain, lung, colon, and breast. While most studies show that 10–20 µM thioridazine leads to reduced proliferation or increased apoptosis, here we show that lower doses of thioridazine (1–2 µM) target the self-renewal of basal-like breast cancer cells, but not breast cancer cells of other subtypes. We also show that all breast cancer cell lines tested express DRD2 mRNA and protein, regardless of thioridazine sensitivity. Further, DRD2 stimulation with quinpirole, a DRD2 agonist, promotes self-renewal, even in cell lines in which thioridazine does not inhibit self-renewal. This suggests that DRD2 is capable of promoting self-renewal in these cell lines, but that it is not active. Further, we show that dopamine can be detected in human and mouse breast tumor samples. This observation suggests that dopamine receptors may be activated in breast cancers, and is the first time to our knowledge that dopamine has been directly detected in human breast tumors, which could inform future investigation into DRD2 as a therapeutic target for breast cancer.

scholarly journals Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics

2020 ◽  
Vol 5 (44) ◽  
pp. eaay6017 ◽  
Author(s):  
Hamad Alshetaiwi ◽  
Nicholas Pervolarakis ◽  
Laura Lynn McIntyre ◽  
Dennis Ma ◽  
Quy Nguyen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Suppressor Cells ◽  
Gene Signature ◽  
Specific Gene ◽  
Myeloid Derived Suppressor Cells ◽  
Breast Cancers ◽  
Molecular Features ◽  
Immunosuppressive Cell ◽  
Tumor Virus

Myeloid-derived suppressor cells (MDSCs) are innate immune cells that acquire the capacity to suppress adaptive immune responses during cancer. It remains elusive how MDSCs differ from their normal myeloid counterparts, which limits our ability to specifically detect and therapeutically target MDSCs during cancer. Here, we sought to determine the molecular features of breast cancer–associated MDSCs using the widely studied mouse model based on the mouse mammary tumor virus (MMTV) promoter–driven expression of the polyomavirus middle T oncoprotein (MMTV-PyMT). To identify MDSCs in an unbiased manner, we used single-cell RNA sequencing to compare MDSC-containing splenic myeloid cells from breast tumor–bearing mice with wild-type controls. Our computational analysis of 14,646 single-cell transcriptomes revealed that MDSCs emerge through an aberrant neutrophil maturation trajectory in the spleen that confers them an immunosuppressive cell state. We establish the MDSC-specific gene signature and identify CD84 as a surface marker for improved detection and enrichment of MDSCs in breast cancers.

scholarly journals Screening of a Novel Upregulated lncRNA, A2M-AS1, That Promotes Invasion and Migration and Signifies Poor Prognosis in Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Fang ◽  
Hu Caixia ◽  
Zhang Xiufen ◽  
Guo Zijian ◽  
Lihua Li
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Human Breast ◽  
Free Survival ◽  
Invasion And Migration ◽  
Basal Like Breast Cancer ◽  
And Migration ◽  
Cancer Tissues

Understanding of prognostic factors and therapeutic targets for breast cancer is imperative for guidance of patient care. We studied 1203 tumour samples from the Gene Expression Omnibus (GEO) to evaluate potential genes related to breast cancer. R software was used to analyse differentially expressed long noncoding RNAs (lncRNAs) in the RNA microarray expression profiles GSE45827 and GSE65216 and to identify a series of differentially expressed lncRNAs associated with human breast cancer. Of these lncRNAs, A2M-AS1, a lncRNA that has not been previously reported, was significantly upregulated in human breast cancer tissues compared with adjacent nontumour tissues. Importantly, A2M-AS1 upregulation was significantly associated with ER-negative, HER2-positive, and basal-like breast cancer and with poor recurrence-free survival and metastasis-free survival in breast cancer patients. After validating these results in 96 collected human breast cancer tissues and 64 paired adjacent noncancerous tissues, we further investigated the roles of A2M-AS1 in human ER-negative and basal-like breast cancer cells. The results revealed that A2M-AS1 significantly promotes human breast cancer cell proliferation, invasion, and migration. Additionally, bioinformatics analysis of genes coexpressed with A2M-AS1 in the context of human breast cancer combined with qRT-PCR and Western blot assays revealed that A2M-AS1 exerts regulatory effects on downstream factors in the cell adhesion molecule pathway, including CD2 and SELL. These results imply that A2M-AS1 might be a promising candidate prognostic factor and therapeutic target for breast cancer.

Sign in / Sign up

Export Citation Format

Share Document