"Detection of circulating miRNAs in serum from patients with breast cancer and their association with the presence of metastatic disease".
Author(s):
E. van Schooneveld, M. Wouters, I. Van der Auwera, D. Peeters, P. Huget, P. A. van Dam, P. B. Vermeulen, S. J. Van Laere, L. Y. Dirix,
Translational Cancer Research Group Antwerp, GZA Hospitals Sint-Augustinus;
Translational Cancer Research Group Antwerp, GZA Hospitals Sint-Augustinus,
Antwerp, Belgium; Translational Cancer Research Group Antwerp, GZA Hospitals
Sint-Augustinus/Department of Medical Oncology, University of Antwerp,
Antwerp, Belgium; Sint-Augustinus, Antwerp, Belgium
Abstract:
Background: miRNAs are small non-coding RNAs involved in the regulation of gene expression. Dysregulation of miRNAs is associated with tumorigenesis. In this study we have evaluated the clinical utility of circulating miRNAs as biomarkers for the detection and staging of breast cancer.
Methods: miRNAs were extracted from a set of 84 surgical tissue samples from breast cancer patients and 8 tissue samples from breast reductive surgery. 768 pre-amplified miRNAs were profiled using the TaqMan Low-Density arrays. After data normalization, principal component analysis (PCA) was used to investigate global differences in miRNA expression between cancerous and normal samples. Using fold-change analysis, the most discriminating miRNAs between both tissue types were selected to be analyzed on serum samples from 20 healthy volunteers and 75 patients with different stages of breast cancer (localized breast cancer (N=4), treated (N=55) and untreated (N=16) metastatic breast cancer). miRNAs were extracted and the selected miRNAs were analyzed in duplo using qRT-PCR.
Results: PCA demonstrated major differences in miRNA expression between tissue samples from breast cancer patients and tissue samples from breast reductive surgery (p<0.0001). Generally, miRNA expression in tumor samples is repressed when compared to healthy controls (p=0.045). The most discriminating miRNAs by fold-change (miR-215, miR-299-5p, miR-411, and miR-452) were selected for further analysis on serum samples. 3/4 miRNAs revealed a differential expression profile between serum samples from cancer patients and healthy controls (miR-411: p=0.004; miR-452: p=0.001 and miR-299-5P: p=0.001). For all these miRNAs, the greatest difference in expression was observed between healthy controls and untreated metastatic breast cancer patients.
Conclusions: Our study provides a basis for the establishment of miRNAs as biomarkers for the detection and eventually staging of breast cancer through blood-borne testing. We identified a set of putative biomarkers of breast cancer and demonstrated that altered levels of these miRNAs in serum from breast cancer patients are associated with metastatic disease.
References:
1. Sieuwerts AM, et al,
"mRNA and microRNA Expression Profiles in Circulating Tumor Cells
and Primary Tumors of Metastatic Breast Cancer Patients".
Clinical Cancer Research June 1, 2011 17; 3600-3619.
http://clincancerres.aacrjournals.org/content/17/11/3600.long
2. Kosaka N, Iguchi H, Yoshioka Y, Takeshita F, Matsuki Y, and Ochiya
T,
"Secretory Mechanisms
and Intercellular Transfer of MicroRNAs in Living Cells?"
3. Inui M, Martello G, and Piccolo S,
"MicroRNA
control of signal transduction".
4. Frenster JH, and Hovsepian JA,
"Reprogramming
the human cancer cell nucleus".
1. Each cell retains all of its embryonic genes for a lifetime.
2. Controls for embryonic genes are often absent in adults.
3. Uncontrolled embryonic genes can replicate wildly.
4. Replicating genes participate in intra-cellular competition.
5. The basis for gene competition is selective transcription.
6. MicroRNAs can reprogram embryomic transcription.
7. Gene reprogramming can produce normal phenotypes.
8. Normal phenotypes can by-pass chromosomal lesions.
9. MicroRNA therapy may need to be permanent.
10. Transplantation of microRNAs could be preferred.
1. Pathways within cell genomes involve a flow of information.
2. Information can flow by direct contact or by third parties.
3. Direct contact within whole genomes is difficult to regulate.
4. DNA-DNA direct contects are influenced by agents.
5. Nuclear agents include hydrophilic ionic and hydrophobic conforming ligands.
6. Third parties within genomes involve RNAs and proteins.
7. RNAs and proteins are easy to regulate or reverse.
8. Information can be shared, lost, or transformed.
9. System information can be hidden during system isolation.
10. Local information can be permanently lost during system entropy.
http://www.cancerbiophysics.net/
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