| Literature DB >> 26199650 |
Gloria Bertoli1, Claudia Cava1, Isabella Castiglioni1.
Abstract
Dysregulation of microRNAs (miRNAs) is involved in the initiation and progression of several human cancers, including breast cancer (BC), as strong evidence has been found that miRNAs can act as oncogenes or tumor suppressor genes. This review presents the state of the art on the role of miRNAs in the diagnosis, prognosis, and therapy of BC. Based on the results obtained in the last decade, some miRNAs are emerging as biomarkers of BC for diagnosis (i.e., miR-9, miR-10b, and miR-17-5p), prognosis (i.e., miR-148a and miR-335), and prediction of therapeutic outcomes (i.e., miR-30c, miR-187, and miR-339-5p) and have important roles in the control of BC hallmark functions such as invasion, metastasis, proliferation, resting death, apoptosis, and genomic instability. Other miRNAs are of interest as new, easily accessible, affordable, non-invasive tools for the personalized management of patients with BC because they are circulating in body fluids (e.g., miR-155 and miR-210). In particular, circulating multiple miRNA profiles are showing better diagnostic and prognostic performance as well as better sensitivity than individual miRNAs in BC. New miRNA-based drugs are also promising therapy for BC (e.g., miR-9, miR-21, miR34a, miR145, and miR150), and other miRNAs are showing a fundamental role in modulation of the response to other non-miRNA treatments, being able to increase their efficacy (e.g., miR-21, miR34a, miR195, miR200c, and miR203 in combination with chemotherapy).Entities:
Keywords: Breast cancer; circulating biomarker; diagnosis; microRNA/miRNA; prediction and therapy.; prognosis; theranostic
Mesh:
Substances:
Year: 2015 PMID: 26199650 PMCID: PMC4508501 DOI: 10.7150/thno.11543
Source DB: PubMed Journal: Theranostics ISSN: 1838-7640 Impact factor: 11.556
Figure 1miRNA biogenesis process. A schematic representation of canonical miRNA biogenesis pathway. Each miRNA is transcribed by RNA polymerase II (pri-miRNA) from genomic DNA within the nucleus; pri-miRNA is recognized by Drosha-DGCR8 and processed to pre-miRNA. Pre-miRNA is exported to the cytoplasm by exportin 5 (XPO5), where it is processed and cleaved by DICER complex to a double strand miRNA (miRNA*-miRNA). The duplex is cleaved, and only the mature miRNA is loaded into the RISC complex. The degree of homology of the miRNA “seed” to the 3′ UTR target sequence of the mRNA determines the mRNA translational repression or degradation.
The main algorithms for computational miRNA-target prediction
| Algorithm | Features | Approach | References |
|---|---|---|---|
| HHMMiR | Seed match, and conservation | HMM | |
| PicTar | Seed match | HMM | |
| MiRFinder | Seed match, and conservation | SVM | |
| RNAmicro | Sequence composition, conservation, and thermodynamic stability | SVM | |
| ProMir | Sequence composition, conservation and thermodynamic stability. | HMM | |
| MiRRim | Sequence composition, conservation, and free energy. | HMM | |
| BayesMiRNAFind | Sequence composition and free energy. | Naïve Bayes Classifier | |
| SSCprofiler | Sequence composition, conservation and free energy. | HMM | |
| Diana-microT | Seed match, conservation, and free energy | Dynamic programming algorithm | |
| TargetScan | Seed match, conservation, and free energy | Combinatorial score | |
| MiRanda | Seed match, conservation, and free energy | Score |
Figure 2Cultured cell lines are transfected with a reporter vector containing firefly (FIR) luciferase gene and the 3′ UTR of the gene of interest (GOI). The level of expression of FIR luciferase is measured in a luminometric assay. Cells are then exposed to the mimic miRNA, which is supposed to enter within the cell and to interact with the 3′ UTR of the GOI. If no interaction between miRNA and the 3′ UTR of GOI happens (a), we could observe no alteration in the level of expression of luciferase, thus no alteration in the emitted chemoluminescence, as FIR gene produced an active, luminescent protein. The complete interaction between the miRNA and the 3′ UTR of the GOI (b) leads to reduced FIR luciferase expression, with a decrease of luminescence levels. Other luminescent genes, such as Renilla (REN) luciferase, are usually used as reference genes for luminescence normalization.
Figure 3Altered steps in miRNA biogenesis lead to cancer. A schematic representation of altered steps of the miRNA biogenesis pathway, commonly deregulated in cancer: 1. miRNA genes contain upstream regulator elements (enhancers/repressors) and promoter regions, indicating that miRNAs are subjected to CpG methylation (CpG promoter met); 2. The alteration in the copy number of miRNA (due to genomic amplification or deletion, activating or repressing mutation, loss of epigenetic silencing and transcriptional activation) could increase the oncogenic miRNAs or decrease the tumor suppressor miRNAs; 3. Alteration in the miRNA processing machinery, i.e. downregulation of Drosha, could decrease the cropping of pri-miR to pre-miR; 4. XPO5 mutation could prevent pre-miR export to the cytoplasm; 5. Mutation of TARBP2 or downregulation of DICER1 decrease mature miRNA levels, causing finally a loss on tumor suppressor miRNAs; 6 and 7. Accumulation of oncogenic miRNAs or loss of tumor suppressor miRNAs could finally lead to cancer development.
Figure 4Contribution of transcription to miRNA level alteration in cancer. Several transcription factors are able to control the level of expression of miRNAs. In particular, as described in the text, SMAD, Myc, ATM, BRCA1/2 and p53 influence miRNA transcription. P53 can regulate onco-suppressor miRNAs, which are involved in the control of p53 turnover. SMAD, ATM, BRCA1/2 and Myc could influence the transcription levels of miRNAs involved in cell plasticity, cell proliferation and survival, and cell invasion control. Moreover, SMAD is also involved in miRNA processing, by Drosha expression levels control. Ex: example of miRNA regulated by transcription factors.
Examples of miRNAs involved in CSC phenotype, and EMT process. This table focuses on few examples of miRNAs described in the text, with a particular attention on their function and the type of cancer where they have been found.
| miRNA annotation | Function | Tumor | Ref. |
|---|---|---|---|
| Regulator of self-renewal, cell proliferation and EMT | Lung, BC | ||
| Symmetric and asymmetric division of CSCs | colon | ||
| Cell cycle control, invasion capacity and metastatic potential | BC | ||
| Stemness and EMT regulation | BC | ||
| EMT, cell migration and invasion control | BC |
BC deregulated miRNAs: an overview.
| miRNA annotations | Samples type | Ref. | |
|---|---|---|---|
| BC cell lines | |||
| BC cell lines | |||
| BC cell lines | |||
| BC cell lines | |||
| BC cell lines | |||
| BC cell lines | |||
| BC cell line; | |||
| BC cell lines | |||
| BC cell lines; TNBC cell lines | |||
| BC cell lines | |||
| BC cell lines | |||
| BC cell lines and xenografted tumor |
TNBC= triple negative BC.
Figure 5Examples of miRNA regulatory networks in BC that promote metastasis. A) Two examples of the role of miR-10b/10b* in the regulation of either migration and invasion (left side) or cell cycle and proliferation (right side) processes. B) Example of let-7 regulatory role in the pro-invasive gene network control.
Circulating and non-circulating miRNAs as BC biomarkers. All the reported miRNAs have been validated on BC patients. For each miRNA, we indicated whether they have a role in diagnosis, prognosis or in prediction of therapy response in BC. For all groups we indicated the biological samples used for the validation, the validation assay, the cohort of data, the main results and the references. This table focused on few examples of single miRNA or miRNA signatures described in the text. GGI=gene expression grade index: TAM=tamoxifen; H= herceptin; N= normal tissue; T= tumor tissues; TNBC= triple negative breast cancer.
| Type of miRNAs | miRNA annotation | Role | Biological Samples | Technique/ | Results | Ref. |
|---|---|---|---|---|---|---|
| Diagnosis | Tissue | Microarray and northern blot/ | 4/13 are downregulated ( | |||
| 133 miRNAs | Diagnosis | Tissue | Microarray / 99 BC vs 5 N and 33 BC cell lines | 31 miRNAs are associated with tumor subtype or clinical pathological fators | ||
| 15 miRNAs | Diagnosis | Tissue | Microarray / 95 BC vs 17 N | ER+; PR+; HER2/ | ||
| 6 BC-miRNAs signature | Diagnosis | Tissue | Microarray/ 363 T vs 177 N | 31% of the total miRNAs varied among T and N tissues; they identified the most commontly altered miRNAs in solid tumors | ||
| Diagnosis | Tissue | TaqMan / | 4-miRNA signature associated with tumor aggressiveness in ER+ BC and miR-210 associated with early relapse in ER- | |||
| Diagnosis | Tissue | Microarray / | 4 miRNA signature | |||
| Diagnosis and prognosis | Tissue | TaqMan/ | Both miRNAs are decreased in BRCA mutant; miR-335 could be used as prognostic marker | |||
| Diagnosis and prognosis | Tissue | Microarray/ 80 high risk vs 80 low risk | 2 upregulated, 'protective' miRNAs (miR-155, miR-493); 2 downregulated risk-associated miRNAs (miR-30e , miR-27a ) | |||
| Prognosis | Tissue | qRT-PCR/ | miR-210 and miR-148a are associated with relapse free survival; | |||
| Prediction of therapy response (TAM) | Tissue | Microarray/ | miR-7 correlates with tumor grade | |||
| Prediction of therapy response (TAM) | Tissue | qRT-PCR/ | Higher miR-30a-3p, miR-30c, and miR-182 are associated with treatment benefits | |||
| Prediction of therapy response (TAM) | Cell lines and tissue | qRT-PCR/ | All miRNAs are upregulated upon anti-estrogen treatment | |||
| Prediction of therapy response (H) | Tissue | Microarray/ 83 BC + H vs adiacent stromal microdissected cells | With SVM technique, they developed a 35 miRNA signature for H treatment response | |||
| Diagnosis | Serum | qRT-PCR/ | miR-155 is increased, both in primary and metastatic BC | |||
| Diagnosis | Blood | qRT-PCR/ | miR-195 is increased +19.25 fold | |||
| Diagnosis and prognosis | Serum | qRT-PCR/ | both miRNAs are increased; miR29a correlates with tumor stage | |||
| Diagnosis and prognosis | Serum | TaqMan / | All are increased in high risk patients | |||
| Diagnosis and prognosis | Blood and serum | RT-PCR/ | All miRNAs are increased in BC patients; | |||
| Diagnosis, prognosis | Serum | RT-PCR/ 33 primary TNBC vs 33 N | 4 miRNA signature predict tumor relapse and overall survival | |||
| Prediction of therapy response (H) | Plasma | TaqMan / | miR-210 is higher in patients with residual BC (+2 fold) | |||
| Prediction of therapy response (taxane) | Serum | RT-PCR/ | miR-155 expression correlates with the treatment course |
Figure 6miRNA biomarkers and BC hallmarks. miRNAs have a role as diagnostic miRNA, prognostic miRNAs (italics), miRNAs predictive of the BC response to therapy (*), or miRNAs with multiple functions (diagnosis, prognosis, prediction of therapy outcome; underlined). Circulating (red) and non-circulating (black) miRNAs of Table 4 are included.