scholarly journals pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers

2020 ◽  
Author(s):  
Vu VH Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Thin Nguyen ◽  
Gregory J Goodall ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Gene Networks ◽  
Supplementary Information ◽  
Cancer Type ◽  
Breast Cancer Patients ◽  
Driver Genes ◽  
Individual Level ◽  
Novel Method ◽  
Cancer Drivers

AbstractMotivationUnravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.ResultsWe propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.Availability and implementationpDriver is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis

2019 ◽  
Vol 21 (2) ◽  
pp. 663-675 ◽  
Author(s):  
Hyung-Yong Kim ◽  
Hee-Joo Choi ◽  
Jeong-Yeon Lee ◽  
Gu Kong
Keyword(s):  
Breast Cancer ◽  
Web Application ◽  
Target Gene ◽  
Biological Significance ◽  
Cancer Type ◽  
Omics Data ◽  
Breast Cancer Patients ◽  
Driver Genes ◽  
Cancer Driver ◽  
Gene Screening

Abstract Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. We developed Cancer Target Gene Screening (CTGS), a web application that provides rapid and user-friendly analysis of multi-omics data sets from a large number of primary breast tumors. It allows the investigation of genomic and epigenomic aberrations, evaluation of transcriptomic profiles and performance of survival analyses and of bivariate correlations between layers of omics data. Notably, the genome-wide screening function of CTGS prioritizes candidate genes of clinical and biological significance among genes with copy number alteration, DNA methylation and dysregulated expression by the integrative analysis of different types of omics data in customized subgroups of breast cancer patients. These features may help in the identification of druggable cancer driver genes in a specific subtype or the clinical condition of human breast cancer. CTGS is available at http://ctgs.biohackers.net.

scholarly journals DriverGroup: A novel method for identifying driver gene groups

2020 ◽  
Author(s):  
Vu VH Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

AbstractMotivationIdentifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.Availability and implementationDriverGroup is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity

2020 ◽  
Vol 36 (9) ◽  
pp. 2755-2762
Author(s):  
Jia-Juan Tu ◽  
Le Ou-Yang ◽  
Hong Yan ◽  
Xiao-Fei Zhang ◽  
Hong Qin
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Tumor Heterogeneity ◽  
Supplementary Information ◽  
Cancer Type ◽  
Cancer Gene ◽  
Expression Data ◽  
Cancer Subtypes

Abstract Motivation Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account. Results In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification. Availability and implementation The source code is available at https://github.com/Zhangxf-ccnu/NETI2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DriverGroup: a novel method for identifying driver gene groups

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i583-i591
Author(s):  
Vu V H Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

Abstract Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. Results We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Loss of sphingosine kinase 1 increases lung metastases in the MMTV-PyMT mouse model of breast cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252311
Author(s):  
Fabiola N. Velazquez ◽  
Leiqing Zhang ◽  
Valentina Viscardi ◽  
Carolena Trocchia ◽  
Yusuf A. Hannun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Human Breast Cancer ◽  
Sphingosine Kinase ◽  
Breast Cancer Progression ◽  
Cancer Type ◽  
Sphingosine Kinase 1 ◽  
Breast Cancer Patients ◽  
Human Breast

Breast cancer is a very heterogeneous disease, and ~30% of breast cancer patients succumb to metastasis, highlighting the need to understand the mechanisms of breast cancer progression in order to identify new molecular targets for treatment. Sphingosine kinase 1 (SK1) has been shown to be upregulated in patients with breast cancer, and several studies have suggested its involvement in breast cancer progression and/or metastasis, mostly based on cell studies. In this work we evaluated the role of SK1 in breast cancer development and metastasis using a transgenic breast cancer model, mouse mammary tumor virus-polyoma middle tumor-antigen (MMTV-PyMT), that closely resembles the characteristics and evolution of human breast cancer. The results show that SK1 deficiency does not alter tumor latency or growth, but significantly increases the number of metastatic lung nodules and the average metastasis size in the lung of MMTV-PyMT mice. Additionally, analysis of Kaplan-Meier plotter of human disease shows that high SK1 mRNA expression can be associated with a better prognosis for breast cancer patients. These results suggest a metastasis-suppressing function for SK1 in the MMTV-PyMT model of breast cancer, and that its role in regulating human breast cancer progression and metastasis may be dependent on the breast cancer type.

Overexpression of microRNA-21 in the Serum of Breast Cancer Patients

MicroRNA ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 58-63
Author(s):  
Batool Savari ◽  
Sohrab Boozarpour ◽  
Maryam Tahmasebi-Birgani ◽  
Hossein Sabouri ◽  
Seyed Mohammad Hosseini
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Tumor Resection ◽  
Tumor Grade ◽  
Healthy Controls ◽  
Breast Cancer Patients ◽  
Serum Samples ◽  
Post Surgery ◽  
Polymerase Chain

Background: Breast cancer is the most common cancer diagnosed in women worldwide. So it seems that there's a good chance of recovery if it's detected in its early stages even before the appearances of symptoms. Recent studies have shown that miRNAs play an important role during cancer progression. These transcripts can be tracked in liquid samples to reveal if cancer exists, for earlier treatment. MicroRNA-21 (miR-21) has been shown to be a key regulator of carcinogenesis, and breast tumor is no exception. Objective: The present study was aimed to track the miR-21 expression level in serum of the breast cancer patients in comparison with that of normal counterparts. Methods: Comparative real-time polymerase chain reaction was applied to determine the levels of expression of miR-21 in the serum samples of 57 participants from which, 42 were the patients with breast cancer including pre-surgery patients (n = 30) and post-surgery patients (n = 12), and the others were the healthy controls (n = 15). Results: MiR-21 was significantly over expressed in the serum of breast cancer patients as compared with healthy controls (P = 0.002). A significant decrease was also observed following tumor resection (P < 0.0001). Moreover, it was found that miR-21 overexpression level was significantly associated with tumor grade (P = 0.004). Conclusion: These findings suggest that miR-21 has the potential to be used as a novel breast cancer biomarker for early detection and prognosis, although further experiments are needed.

scholarly journals Stromal CCL5 Promotes Breast Cancer Progression by Interacting with CCR3 in Tumor Cells

2021 ◽  
Vol 22 (4) ◽  
pp. 1918
Author(s):  
Mio Yamaguchi ◽  
Kiyoshi Takagi ◽  
Koki Narita ◽  
Yasuhiro Miki ◽  
Yoshiaki Onodera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Tumor Progression ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Stromal Cells ◽  
Human Breast Carcinoma ◽  
Breast Cancer Patients ◽  
Breast Carcinomas ◽  
Human Breast

Chemokines secreted from stromal cells have important roles for interactions with carcinoma cells and regulating tumor progression. C-C motif chemokine ligand (CCL) 5 is expressed in various types of stromal cells and associated with tumor progression, interacting with C-C chemokine receptor (CCR) 1, 3 and 5 expressed in tumor cells. However, the expression on CCL5 and its receptors have so far not been well-examined in human breast carcinoma tissues. We therefore immunolocalized CCL5, as well as CCR1, 3 and 5, in 111 human breast carcinoma tissues and correlated them with clinicopathological characteristics. Stromal CCL5 immunoreactivity was significantly correlated with the aggressive phenotype of breast carcinomas. Importantly, this tendency was observed especially in the CCR3-positive group. Furthermore, the risk of recurrence was significantly higher in the patients with breast carcinomas positive for CCL5 and CCR3 but negative for CCR1 and CCR5, as compared with other patients. In summary, the CCL5-CCR3 axis might contribute to a worse prognosis in breast cancer patients, and these findings will contribute to a better understanding of the significance of the CCL5/CCRs axis in breast carcinoma microenvironment.

scholarly journals Antioxidants for the Treatment of Breast Cancer: Are We There Yet?

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 205
Author(s):  
Carmen Griñan-Lison ◽  
Jose L. Blaya-Cánovas ◽  
Araceli López-Tejada ◽  
Marta Ávalos-Moreno ◽  
Alba Navarro-Ocón ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Progression ◽  
Redox Homeostasis ◽  
Breast Carcinogenesis ◽  
Breast Cancer Patients ◽  
Chemopreventive Agents ◽  
Low Degree ◽  
Potential Use

Breast cancer is the most frequent cancer and the leading cause of cancer death in women. Oxidative stress and the generation of reactive oxygen species (ROS) have been related to cancer progression. Compared to their normal counterparts, tumor cells show higher ROS levels and tight regulation of REDOX homeostasis to maintain a low degree of oxidative stress. Traditionally antioxidants have been extensively investigated to counteract breast carcinogenesis and tumor progression as chemopreventive agents; however, there is growing evidence indicating their potential as adjuvants for the treatment of breast cancer. Aimed to elucidate whether antioxidants could be a reality in the management of breast cancer patients, this review focuses on the latest investigations regarding the ambivalent role of antioxidants in the development of breast cancer, with special attention to the results derived from clinical trials, as well as their potential use as plausible agents in combination therapy and their power to ameliorate the side effects attributed to standard therapeutics. Data retrieved herein suggest that antioxidants play an important role in breast cancer prevention and the improvement of therapeutic efficacy; nevertheless, appropriate patient stratification based on “redoxidomics” or tumor subtype is mandatory in order to define the dosage for future standardized and personalized treatments of patients.

scholarly journals Deregulation of protein phosphatase 2A inhibitor SET is associated with malignant progression in breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katsunori Tozuka ◽  
Pattama Wongsirisin ◽  
Shigenori E. Nagai ◽  
Yasuhito Kobayashi ◽  
Miki Kanno ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Protein Phosphatase ◽  
Protein Phosphatase 2A ◽  
Recurrent Breast Cancer ◽  
Stage I ◽  
Breast Cancer Patients ◽  
Potential Biomarker ◽  
Phosphatase 2A ◽  
Mcf 7

AbstractTo understand the mechanism underlying metastasis, identification of a mechanism-based and common biomarker for circulating tumour cells (CTCs) in heterogenous breast cancer is needed. SET, an endogenous inhibitor of protein phosphatase 2A, was overexpressed in all subtypes of invasive breast carcinoma tissues. Treatment with SET-targeted siRNAs reduced the motility of MCF-7 and MDA-MB-231 cells in transwell assay. SET knockdown reduced the number of mammospheres by 60–70% in MCF-7 and MDA-MB-231 cells, which was associated with the downregulation of OCT4 and SLUG. Hence, we analysed the presence of SET-expressing CTCs (SET-CTCs) in 24 breast cancer patients. CTCs were enriched using a size-based method and then immunocytochemically analysed using an anti-SET antibody. SET-CTCs were detected in 6/6 (100%) patients with recurrent breast cancer with a median value of 12 (12 cells/3 mL blood), and in 13/18 (72.2%) patients with stage I–III breast cancer with a median value of 2.5, while the median value of healthy controls was 0. Importantly, high numbers of SET-CTCs were correlated with lymph node metastasis in patients with stage I–III disease. Our results indicate that SET contributes to breast cancer progression and can act as a potential biomarker of CTCs for the detection of metastasis.

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