Abstract
Background: In order to characterize the various subtypes of breast cancer more precisely and improve patients selection for breast conserving therapy (BCT), molecular profiling has gained importance over the past two decades. MicroRNAs, which are small non-coding RNAs, can potentially regulate numerous downstream target molecules and thereby interfere in carcinogenesis and treatment response via multiple pathways. The aim of the current two-phase study was to investigate whether hsa-miR-375-signaling through RASD1 could predict local control (LC) in early breast cancer. Results: The patient and treatment characteristics of 81 individuals were similarly distributed between relapse (n = 27) and control groups (n = 54). In the pilot phase, the primary tumors of 28 patients were analyzed with microarray technology. Of the more than 70,000 genes on the chip, 104 potential hsa-miR-375 target molecules were found to have a lower expression level in relapse patients compared to controls (p-value < 0.2). For RASD1, a hsa-miR-375 binding site was predicted by an in silico search in five mRNA-miRNA databases and mechanistically proven in previous pre-clinical studies. Its expression levels were markedly lower in relapse patients than in controls (p-value of 0.058). In a second phase, this finding could be validated in an independent set of 53 patients using ddPCR. Patients with enhanced levels of hsa-miR-375 compared to RASD1 had a higher probability of local relapse than those with the inverse expression pattern of the two markers (log-rank test, p-value = 0.069). Conclusion: This two-phase study demonstrates that hsa-miR-375/RASD1 signaling is able to predict local control in early breast cancer patients, which—to our knowledge—is the first clinical report on a miR combined with one of its downstream target proteins predicting LC in breast cancer.
| Original language | English |
|---|---|
| Article number | 1404 |
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | Genes |
| Volume | 11 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 26 Nov 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Fields of science
- 305907 Medical statistics
- 202017 Embedded systems
- 202036 Sensor systems
- 101004 Biomathematics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
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- 101018 Statistics
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- 101028 Mathematical modelling
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- 101031 Approximation theory
- 102 Computer Sciences
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- 102013 Human-computer interaction
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- 102032 Computational intelligence
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- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 202035 Robotics
- 202037 Signal processing
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
JKU Focus areas
- Digital Transformation
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