Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.

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Metformin May Block the Way Breast Cancer Metastasis: A Meta-analysis and Mechanism Review

10.21203/rs.3.rs-56326/v1 ◽

2020 ◽

Author(s):

Ming Yang ◽

Yueyuan Wang ◽

Zhihao Zhang ◽

Jingyu Peng ◽

Xiao Xie ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Distant Metastasis ◽

Retrospective Cohort ◽

Cancer Metastasis ◽

Meta Analysis ◽

Random Effect ◽

Breast Cancer Metastasis ◽

Cochrane Library ◽

Breast Cancer Patients

Abstract Background Metformin, which is cheap and easy to get, is a first-line anti-hyperglycemia drug. Recently, its anti-tumor effect has been revealed. Here we performed a meta-analysis to summarize previous studies and a narrative review to gather the mechanisms involved in the potential relationship. Methods We searched related articles in database of Pubmed, EMbase, Web of science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang and Sinomed and obtained 8 clinic trials that investigated the connection between metformin and breast cancer metastasis, containing 2 randomized controlled trials (RCTs) and 6 retrospective cohort studies. We evaluated each retrospective cohort study by Newcastle-Ottawa Scale (NOS), while RCT by Chcorane Risk of Bias tool. Pooled hazard ratios (HRs), risk ratios (RRs) and we calculated associated 95% confidence intervals (CIs) with a random-effect, generic inverse variance method. We also collected the possible mechanisms of cancer metastasis inhibition from metformin. Results A total of 8 studies containing 13919 breast cancer patients without distant metastasis before they got anticancer treatment. The result showed that adjuvant metformin in treatment of local breast cancer facilitated to suppress metastasis (HR = 0.69, 95% CI = 0.57–0.82, p < 0.0001, I2 = 0%), and the result was consistent with the subgroup of breast cancer patients with type 2 diabetes mellitus (T2DM) (HR = 0.68, 95% CI = 0.57–0.82, p < 0.0001, I2 = 0%). Conclusion The meta-analysis suggested metformin might repress the metastasis and be benefit to distant metastasis-free survival (DMFS) when added to systemic breast cancer therapy, supporting anti-tumor effects of metformin on breast cancer.

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2–26 Identification and Characterization of Optimal Gene Expression Markers for Detection of Breast Cancer Metastasis

Breast Diseases A Year Book Quarterly ◽

10.1016/s1043-321x(06)80438-8 ◽

2006 ◽

Vol 17 (2) ◽

pp. 161-162

Author(s):

S. Krishnamurthy

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Metastasis ◽

Breast Cancer Metastasis ◽

Identification And Characterization

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Gene expression profiles of breast cancer metastasis according to organ site

Molecular Oncology ◽

10.1002/1878-0261.13021 ◽

2021 ◽

Author(s):

Fara Brasó‐Maristany ◽

Laia Paré ◽

Nuria Chic ◽

Olga Martínez‐Sáez ◽

Tomás Pascual ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Metastasis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Breast Cancer Metastasis ◽

Organ Site

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Metastasis-specific gene expression in autochthonous and allograft mouse mammary tumor models: stratification and identification of targetable signatures

10.1101/2019.12.20.885210 ◽

2019 ◽

Author(s):

Christina Ross ◽

Karol Szczepanek ◽

Maxwell lee ◽

Howard Yang ◽

Cody J. Peer ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Line ◽

Mouse Models ◽

Primary Tumor ◽

Cancer Metastasis ◽

Breast Cancer Metastasis ◽

Specific Gene ◽

Tail Vein ◽

Specific Gene Expression

AbstractBreast cancer is a leading cause of cancer-related death of women in the U.S., which is ultimately due to metastasis rather than primary tumor burden. Therefore, increased understanding of metastasis to develop novel therapies is critical in reducing breast cancer-related mortality. Indeed, a major hurdle in advancing metastasis-targeted intervention is the genotypic and phenotypic heterogeneity that exists between primary and secondary lesions. To identify targetable metastasis-specific gene expression profiles we performed RNA sequencing of breast cancer metastasis mouse models. We analyzed metastases from models of various oncogenic drivers and routes, including orthotopic injection, tail vein injection, intracardiac injection, and genetically engineered mouse models (GEMMs). Herein, we analyzed samples from 176 mice and tissue culture samples, resulting in 338 samples total. Using these data, we contrasted the different breast cancer metastasis models, and also identified common, targetable metastasis specific gene expression signatures.Principal component analysis revealed that mouse model (Allograft v. GEMM) rather than tissue source (PT v metastatic nodule) shaped the transcriptomes of our samples. Allograft models exhibited more mesenchymal-like gene expression than GEMM models, and primary culturing of GEMM-derived metastatic tissue induced more mesenchymal-like gene expression. Furthermore, metastasis-specific gene expression differed between tail vein and orthotopic injection models of the same cell line, the degree of which was cell line dependent. Finally, we examined metastasis-specific gene expression common to models of spontaneous metastasis (orthotopic injection and GEMMs). Pathway analysis identified upregulation of the sildenafil response, and nicotine degradation pathways. The influence of these pathways on metastatic spread was assessed by treatment of allograft models with clinically relevant dosing of sildenafil or nicotine. Sildenafil significantly reduced pulmonary metastasis while nicotine administration significantly increased metastasis, and neither regimen altered primary tumor mass. Taken together our data reveals critical differences between pre-clinical models of metastatic breast cancer. Additionally, this strategy has identified clinically targetable metastasis-specific pathways integral to metastatic spread.

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