Two-gene expression ratio as predictor for breast cancer treated with tamoxifen: evidence from meta-analysis

The addition of adjuvant chemotherapy to hormonal therapy is often considered questionable in patients with estrogen receptor-positive early breast cancer. Low risk of disease relapse after endocrine treatment alone and/or a low sensitivity to chemotherapy are reasons behind not all patients benefit from chemotherapy. Most of the patients could be exposed to unnecessary treatment- related adverse events and health care costs when treatment decision-making is based only on classical clinical histological features. Gene expression profile has been developed to refine physician’s decision-making process and to tailor personalized treatment to patients. In particular, these tests are designed to spare patients the side effects of unnecessary treatment, and ensure that adjuvant chemotherapy is correctly recommended to patients with early breast cancer. In this review, we will discuss the main diagnostic tests and their potential clinical applications (Oncotype DX, MammaPrint, PAM50/Prosigna, EndoPredict, MapQuant Dx, IHC4, and Theros-Breast Cancer Gene Expression Ratio Assay).

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

Breast Cancer Research and Treatment ◽

10.1007/s10549-008-0038-x ◽

2008 ◽

Vol 113 (2) ◽

pp. 251-252 ◽

Cited By ~ 3

Author(s):

Mads Thomassen ◽

Qihua Tan ◽

Torben A. Kruse

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Candidate Genes ◽

Cancer Metastasis ◽

Meta Analysis ◽

Breast Cancer Metastasis

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Gene expression ratio as a predictive determinant of nelarabine chemosensitivity in T-lymphoblastic leukemia/lymphoma

Pediatric Blood & Cancer ◽

10.1002/pbc.26214 ◽

2016 ◽

Vol 64 (2) ◽

pp. 250-253

Author(s):

Pornpun Sripornsawan ◽

Yasuhiro Okamoto ◽

Takuro Nishikawa ◽

Yuichi Kodama ◽

Yuni Yamaki ◽

...

Keyword(s):

Gene Expression ◽

Lymphoblastic Leukemia ◽

Expression Ratio ◽

Gene Expression Ratio

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High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.650488 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoli Hu ◽

Yang Liu ◽

Zhitong Bing ◽

Qian Ye ◽

Chengcheng Li

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Systematic Review ◽

Cancer Patients ◽

Poor Prognosis ◽

Data Extraction ◽

Meta Analysis ◽

Gene Expression Omnibus ◽

Breast Cancer Patients ◽

Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P < 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

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Microarray Image Analysis and Gene Expression Ratio Statistics

Computational and Statistical Approaches to Genomics ◽

10.1007/0-306-47825-0_1 ◽

2005 ◽

pp. 1-21

Author(s):

Yidong Chen ◽

Edward R. Dougherty ◽

Michael L. Bittner ◽

Paul Meltzer ◽

Jeffery Trent

Keyword(s):

Gene Expression ◽

Image Analysis ◽

Expression Ratio ◽

Microarray Image ◽

Gene Expression Ratio

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Prospective validation in epithelial tumors of a gene expression predictor of liver metastasis derived from uveal melanoma

Scientific Reports ◽

10.1038/s41598-019-52841-y ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Petros Tsantoulis ◽

Mauro Delorenzi ◽

Ivan Bièche ◽

Sophie Vacher ◽

Pascale Mariani ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Colorectal Cancer ◽

Liver Metastasis ◽

Uveal Melanoma ◽

Meta Analysis ◽

Prognostic Score ◽

Penalized Regression ◽

Breast Cancer Patients ◽

Public Data

AbstractPredicting the risk of liver metastasis can have important prognostic and therapeutic implications, given the availability of liver-directed therapy. Uveal melanoma has a striking predisposition for liver metastasis despite the absence of anatomical proximity. Understanding its biology may uncover factors promoting liver metastasis in other malignancies. We quantified gene expression by RNAseq in 76 uveal melanomas and combined with public data in a meta-analysis of 196 patients. The meta-analysis of uveal melanoma gene expression identified 63 genes which remained prognostic after adjustment for chromosome 3 status. Two genes, PTP4A3 and JPH1, were selected by L1-penalized regression and combined in a prognostic score. The score predicted liver-specific relapse in a public pan-cancer dataset and in two public colorectal cancer datasets. The score varied between colorectal consensus molecular subtypes (CMS), as did the risk of liver relapse, which was lowest in CMS1. Additional prospective validation was done by real-time PCR in 463 breast cancer patients. The score was significantly correlated with liver relapse in hormone receptor positive tumors. In conclusion, the expression of PTP4A3 and JPH1 correlates with risk of liver metastasis in colorectal cancer and breast cancer. The underlying biological mechanism is an interesting area for further research.

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