To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:2609

Relationship: 2609

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Increase, Mutations leads to Increase,miRNA levels

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
DNA damage and mutations leading to Metastatic Breast Cancer adjacent Moderate Moderate Usha Adiga (send email) Under development: Not open for comment. Do not cite

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
human and other cells in culture human and other cells in culture High NCBI
mice Mus sp. High NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Female High

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
Not Otherwise Specified Not Specified

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Upstream event: increased, mutations

Downstream event: increased miRNA

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

Evidences suggest that transcription pathway for miRNAs is regulated in the DNA damage response (DDR). The tumour suppressor p53 is a well-known transcription factor that is activated in response to DNA damage and causes cell growth arrest, promotes apoptosis, inhibits angiogenesis, and mediates DNA repair (Meek et al., 2009). Global miRNA expression investigations identified the miRNA components of p53 transcriptional pathways and demonstrated that a cohort of miRNAs are up-regulated in a p53-dependent manner following DNA damage. The miR-34 family (miR-34a, miR-34b/c) was the first transcriptional target of p53 to be discovered. p53 directly transactivates miR-15a/16-1, miR-29, miR-107, miR-145, miR-192, miR-194, miR-215, and miR-605 in addition to the miR-34 family.

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

Various transcription factors regulate miRNA expression.The p53 protein also functions as a transcriptional repressor by binding to miRNA promoters and preventing the recruitment of transcriptional activators.For example, p53 prevents the TATA-binding protein from binding to the TAATA site in the promoter of the miR-17-92 cluster gene, suppressing transcription. Under hypoxic conditions, the miR-17-92 cluster is suppressed by a p53-dependent mechanism, making cells more susceptible to hypoxia-induced death (Yan et al.,2009). Cells can amplify the p53 signal by fine-tuning the p53 signalling pathway and the miRNA network, which improves cell sensitivity to external signals.

Other DNA damage-responsive transcription factors, such as NF-kB, E2F, and Myc, are also involved in miRNA  transcription regulation. Both E2F and Myc promote miR-17-92 cluster transcription, which reduces E2F and Myc expression, establishing an autoregulatory negative feedback loop. Furthermore, Myc-induced miRNAs have an impact on cell proliferation and cell fate in Myc-mediated cells (Kim et al.,2010). Myc-induced miR-20a, for example, targets cdkn1a, a gene that encodes a negative regulator of cell-cycle progression, while Myc-induced miR-221 and miR-222 target the CDKN1b and CDKN1c genes, respectively, that trigger cell-cycle arrest. Little is currently known about how miRNA gene expression is transcriptionally regulated due to a lack of basic information about miRNA gene structure. Global prediction and verification of promoter regions of miRNA genes would allow us to further explore the functional interaction of transcriptional machinery and epigenetic miRNA regulation. p53 regulates miRNA maturation not only during transcription but also during processing of initial miRNA transcripts, resulting in crosstalk between the p53 network and the DDR's miRNA biogenesis machinery. Many p53-regulated miRNAs target proteins in the DDR, such as cell cycle progression and apoptosis, to affect the DDR.

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

In response to stressors like as ionising radiation, miRNAs are differently regulated. When exposed to IR, miRNA expression is frequently disrupted. Some miRNAs are induced by IR, while others are suppressed, a decision that is likely based on the target genes implicated. This figure summarises the miRNAs mentioned in this review whose expression changes in response to IR. Lists of miRNAs whose induction or repression has been detected are on the left and right, respectively. MiRNAs are in the middle, and both induction and repression have been found in many cell types. The centre contains the biggest group of miRNAs, demonstrating how diverse the miRNA profile can be from one cell type to the next. Bold miRNAs play a role in several parts of the DDR.

Induced miRNAs were reported by some studies (Cha et al.,2009;Chaudhary et al 201; Chaudhary et al 2012; Chaudhary et al 2013;Kwon et al.,2013;Mueller et al.,2013;Shin et al.,2009;Sokolov et al.,2012;Wagner et al.,2010),whereas repressed miRNAs are observed in some(Cha et al., 2009,Chaudhari et al.,2010).Both induction and repression of some miRNA were seen in different cell types and results are inconclusive (Cha et al.,2009;Chaudhary et al 201; Chaudhary et al 2012; Chaudhary et al 2013;Kwon et al.,2013;Mueller et al.,2013;Shin et al.,2009;Sokolov et al.,2012;Wagner et al.,2010; Kraemer et al., 2011; Moskwa et al.,2011;Niemoeller et al.,2011;Sokolov et al., 2012;Wagner et al.,2010).This inconsistency could be due to different doses of stressor.

DNA damage response influences miRNA expression, at the same time miRNA can also influence DDR, cell cycle etc.The miR-34 family produces a cell-cycle arrest in the G1 phase and slows cell-cycle progression by targeting multiple cell cycle regulators when ectopically produced, implying tumor-suppressing potential. The miR-34 family, for example, specifically targets and inhibits cyclin-dependent kinase 4 (CDK4), CDK6, E2F3, Myc, and NMYC (Chang  et al.,2007,He et al., 2007).

MiRNA expression can be influenced by DNA damage and mutation, but miRNA can also regulate DNA damage response and cell cycle.By suppressing the transcripts of numerous genes that govern cell-cycle checkpoints or metabolism, these p53-induced miRNAs contribute to cell-cycle arrest (Su et al.,2010;Georges et al.,2008;Hermeking et al.,2012;Klein et al., 2010; Liu et al.,2011; Suh et al.,2011). Wip1 phosphatase, a master inhibitor in the DDR that inhibits the activation and stability of p53, is targeted and repressed by miR-16 and miR-29, resulting in p53 induction (Ugalde et al.,2011; Zhang et al.,2010). Cellcycle arrest is induced by ectopic expression of miR-192/215, which targets a number of genes that regulate the G1/S and G2/M checkpoints (Bulavin et al.,2004).

The oncogene c-Myc is directly targeted by miR-145, implying that p53 suppresses cMyc activities through regulating miRNA expression (Sachdeva et al.,2009, Suh et al.,2011). p53-induced miRNAs, interestingly, influence p53 activity in a positive feedback loop (Han et al.,2012, Hermeking et al.,2012). SIRT1 acetylation and activation are increased when miR-34 inhibits it (Yamakuchi et al., 2008). Mdm2 expression is directly inhibited by miR-192, miR-194, miR-215, and MiR-605, while Wip1 is inhibited by miR-29, resulting in higher p53 levels and activity. (Braun et al.,2008, Pichiorri et al.,2010, Xiao et al., 2011).

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

Activity of pri-miR-218, pri-miR-16-1, pri-miR-21, and pri- miR-199a had significantly increased binding with Drosha (2.5- to 3.2-fold) after DNA damage (Zhang et al.,2011)

Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

It has been noted that, within hours of DNA damage,miRNA expression were induced(Wan et al.,2013).

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

miRNA expression profiles are influenced by a variety of DNA damaging stressors. Pothof et al. were the first to notice differences in miRNA expression in cell-cycle checkpoints and DNA repair in UV-treated cells (Pothof et al., 2009). Other DNA damaging agents, such as cisplatin, doxorubicin, IR, and NCS, were used to examine miRNA expression profiles in cells (Galluzzi et al., 2010, Saleh et al.,2011;Suzuki et al.,2009). Different levels of DNA damage appear to activate different groups of miRNAs, implying that miRNAs regulate the DDR through a mechanism that is dependent on the type and severity of the DNA damage.

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Not specific ones available.

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

Not specific through any particular life stage or gender.

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Abdelfattah, N., Rajamanickam, S., Panneerdoss, S., Timilsina, S., Yadav, P., Onyeagucha, B. C., ... & Rao, M. K. (2018). MiR-584-5p potentiates vincristine and radiation response by inducing spindle defects and DNA damage in medulloblastoma. Nature communications9(1), 1-19.

Braun, C. J., Zhang, X., Savelyeva, I., Wolff, S., Moll, U. M., Schepeler, T., ... & Dobbelstein, M. (2008). p53-Responsive micrornas 192 and 215 are capable of inducing cell cycle arrest. Cancer research68(24), 10094-10104.

Bulkowska, M., Rybicka, A., Senses, K. M., Ulewicz, K., Witt, K., Szymanska, J., ... & Krol, M. (2017). MicroRNA expression patterns in canine mammary cancer show significant differences between metastatic and non-metastatic tumours. BMC cancer17(1), 1-17.

Bulavin, D. V., Phillips, C., Nannenga, B., Timofeev, O., Donehower, L. A., Anderson, C. W., ... & Fornace, A. J. (2004). Inactivation of the Wip1 phosphatase inhibits mammary tumorigenesis through p38 MAPK–mediated activation of the p16 Ink4a-p19 Arf pathway. Nature genetics36(4), 343-350.

 Cha, H. J., Shin, S., Yoo, H., Lee, E. M., Bae, S., Yang, K. H., ... & An, S. (2009). Identification of ionizing radiation-responsive microRNAs in the IM9 human B lymphoblastic cell line. International journal of oncology34(6), 1661-1668.

 Chaudhry, M. A., & Omaruddin, R. A. (2012). Differential regulation of microRNA expression in irradiated and bystander cells. Molecular Biology46(4), 569-578.

Chaudhry, M. A., Omaruddin, R. A., Brumbaugh, C. D., Tariq, M. A., & Pourmand, N. (2013). Identification of radiation-induced microRNA transcriptome by next-generation massively parallel sequencing. Journal of radiation research54(5), 808-822..

 Chaudhry, M. A., Omaruddin, R. A., Kreger, B., De Toledo, S. M., & Azzam, E. I. (2012). Micro RNA responses to chronic or acute exposures to low dose ionizing radiation. Molecular biology reports39(7), 7549-7558.

Chaudhry, M. A., Sachdeva, H., & Omaruddin, R. A. (2010). Radiation-induced micro-RNA modulation in glioblastoma cells differing in DNA-repair pathways. DNA and cell biology29(9), 553-561..

Chang, T. C., Wentzel, E. A., Kent, O. A., Ramachandran, K., Mullendore, M., Lee, K. H., ... & Mendell, J. T. (2007). Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Molecular cell26(5), 745-752.

Galluzzi, L., Morselli, E., Vitale, I., Kepp, O., Senovilla, L., Criollo, A., ... & Kroemer, G. (2010). miR-181a and miR-630 regulate cisplatin-induced cancer cell death. Cancer research70(5), 1793-1803.

Georges, S. A., Biery, M. C., Kim, S. Y., Schelter, J. M., Guo, J., Chang, A. N., ... & Chau, B. N. (2008). Coordinated regulation of cell cycle transcripts by p53-Inducible microRNAs, miR-192 and miR-215. Cancer research68(24), 10105-10112.

Han, C., Wan, G., Langley, R. R., Zhang, X., & Lu, X. (2012). Crosstalk between the DNA damage response pathway and microRNAs. Cellular and molecular life sciences69(17), 2895-2906.

Han, C., Liu, Y., Wan, G., Choi, H. J., Zhao, L., Ivan, C., ... & Lu, X. (2014). The RNA-binding protein DDX1 promotes primary microRNA maturation and inhibits ovarian tumor progression. Cell reports8(5), 1447-1460.

Hermeking, H. (2012). MicroRNAs in the p53 network: micromanagement of tumour suppression. Nature reviews cancer12(9), 613-626.

He, L., He, X., Lim, L. P., De Stanchina, E., Xuan, Z., Liang, Y., ... & Hannon, G. J. (2007). A microRNA component of the p53 tumour suppressor network. Nature447(7148), 1130-1134.

Kim, J. W., Mori, S., & Nevins, J. R. (2010). Myc-induced microRNAs integrate Myc-mediated cell proliferation and cell fate. Cancer research70(12), 4820-4828.

Klein, U., Lia, M., Crespo, M., Siegel, R., Shen, Q., Mo, T., ... & Dalla-Favera, R. (2010). The DLEU2/miR-15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia. Cancer cell17(1), 28-40.

Kraemer, A., Anastasov, N., Angermeier, M., Winkler, K., Atkinson, M. J., & Moertl, S. (2011). MicroRNA-mediated processes are essential for the cellular radiation response. Radiation research176(5), 575-586.

Kwon, J. E., Kim, B. Y., Kwak, S. Y., Bae, I. H., & Han, Y. H. (2013). Ionizing radiation-inducible microRNA miR-193a-3p induces apoptosis by directly targeting Mcl-1. Apoptosis18(7), 896-909.

Liu, M., Lang, N., Chen, X., Tang, Q., Liu, S., Huang, J., ... & Bi, F. (2011). miR-185 targets RhoA and Cdc42 expression and inhibits the proliferation potential of human colorectal cells. Cancer letters301(2), 151-160.

Liu, Z., Zhang, C., Khodadadi-Jamayran, A., Dang, L., Han, X., Kim, K., ... & Zhao, R. (2017). Canonical microRNAs enable differentiation, protect against DNA damage, and promote cholesterol biosynthesis in neural stem cells. Stem cells and development26(3), 177-188.

Meek, D. W. (2009). Tumour suppression by p53: a role for the DNA damage response?. Nature Reviews Cancer9(10), 714-723.

Moskwa, P., Buffa, F. M., Pan, Y., Panchakshari, R., Gottipati, P., Muschel, R. J., ... & Chowdhury, D. (2011). miR-182-mediated downregulation of BRCA1 impacts DNA repair and sensitivity to PARP inhibitors. Molecular cell41(2), 210-220.

Mueller, A. C., Sun, D., & Dutta, A. (2013). The miR-99 family regulates the DNA damage response through its target SNF2H. Oncogene32(9), 1164-1172.

Niemoeller, O. M., Niyazi, M., Corradini, S., Zehentmayr, F., Li, M., Lauber, K., & Belka, C. (2011). MicroRNA expression profiles in human cancer cells after ionizing radiation. Radiation oncology6(1), 1-5.

Pichiorri, F., Suh, S. S., Rocci, A., De Luca, L., Taccioli, C., Santhanam, R., ... & Croce, C. M. (2010). Downregulation of p53-inducible microRNAs 192, 194, and 215 impairs the p53/MDM2 autoregulatory loop in multiple myeloma development. Cancer cell18(4), 367-381.

Pothof, J., Verkaik, N. S., Van Ijcken, W., Wiemer, E. A., Ta, V. T., Van Der Horst, G. T., ... & Persengiev, S. P. (2009). MicroRNA‐mediated gene silencing modulates the UVinduced DNAdamage response. The EMBO journal28(14), 2090-2099.

Riaz, M., van Jaarsveld, M. T., Hollestelle, A., Prager-van der Smissen, W. J., Heine, A. A., Boersma, A. W., ... & Martens, J. W. (2013). miRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs. Breast cancer research15(2), 1-17.

Shin, S., Cha, H. J., Lee, E. M., Lee, S. J., Seo, S. K., Jin, H. O., ... & An, S. (2009). Alteration of miRNA profiles by ionizing radiation in A549 human non-small cell lung cancer cells. International journal of oncology35(1), 81-86.

 Sokolov, M. V., Panyutin, I. V., & Neumann, R. D. (2012). Unraveling the global microRNAome responses to ionizing radiation in human embryonic stem cells. PloS one7(2), e31028.

 Su, X., Chakravarti, D., Cho, M. S., Liu, L., Gi, Y. J., Lin, Y. L., ... & Flores, E. R. (2010). TAp63 suppresses metastasis through coordinate regulation of Dicer and miRNAs. Nature467(7318), 986-990.

Suh, S. O., Chen, Y., Zaman, M. S., Hirata, H., Yamamura, S., Shahryari, V., ... & Dahiya, R. (2011). MicroRNA-145 is regulated by DNA methylation and p53 gene mutation in prostate cancer. Carcinogenesis32(5), 772-778.

Sachdeva, M., Zhu, S., Wu, F., Wu, H., Walia, V., Kumar, S., ... & Mo, Y. Y. (2009). p53 represses c-Myc through induction of the tumor suppressor miR-145. Proceedings of the National Academy of Sciences106(9), 3207-3212.

Suh, S. O., Chen, Y., Zaman, M. S., Hirata, H., Yamamura, S., Shahryari, V., ... & Dahiya, R. (2011). MicroRNA-145 is regulated by DNA methylation and p53 gene mutation in prostate cancer. Carcinogenesis32(5), 772-778.

Saleh, A. D., Savage, J. E., Cao, L., Soule, B. P., Ly, D., DeGraff, W., ... & Simone, N. L. (2011). Cellular stress induced alterations in microRNA let-7a and let-7b expression are dependent on p53. PloS one6(10), e24429.

Suzuki, H. I., Yamagata, K., Sugimoto, K., Iwamoto, T., Kato, S., & Miyazono, K. (2009). Modulation of microRNA processing by p53. Nature460(7254), 529-533.

Ugalde, A. P., Ramsay, A. J., De La Rosa, J., Varela, I., Mariño, G., Cadiñanos, J., ... & López‐Otín, C. (2011). Aging and chronic DNA damage response activate a regulatory pathway involving miR29 and p53. The EMBO journal30(11), 2219-2232.

van Jaarsveld, M. T., Wouters, M. D., Boersma, A. W., Smid, M., van IJcken, W. F., Mathijssen, R. H., ... & Pothof, J. (2014). DNA damage responsive microRNAs misexpressed in human cancer modulate therapy sensitivity. Molecular oncology8(3), 458-468.

Van der Auwera, I., Limame, R., Van Dam, P., Vermeulen, P. B., Dirix, L. Y., & Van Laere, S. J. (2010). Integrated miRNA and mRNA expression profiling of the inflammatory breast cancer subtype. British journal of cancer103(4), 532-541.

Wan, G., Zhang, X., Langley, R. R., Liu, Y., Hu, X., Han, C., ... & Lu, X. (2013). DNA-damage-induced nuclear export of precursor microRNAs is regulated by the ATM-AKT pathway. Cell reports3(6), 2100-2112.

Wagner-Ecker, M., Schwager, C., Wirkner, U., Abdollahi, A., & Huber, P. E. (2010). MicroRNA expression after ionizing radiation in human endothelial cells. Radiation oncology5(1), 1-10.

Xiao, J., Lin, H., Luo, X., Luo, X., & Wang, Z. (2011). miR‐605 joins p53 network to form a p53: miR605: Mdm2 positive feedback loop in response to stress. The EMBO journal30(3), 524-532.

Yamakuchi, M., Ferlito, M., & Lowenstein, C. J. (2008). miR-34a repression of SIRT1 regulates apoptosis. Proceedings of the National Academy of Sciences105(36), 13421-13426.

Yan, H. L., Xue, G., Mei, Q., Wang, Y. Z., Ding, F. X., Liu, M. F., ... & Sun, S. H. (2009). Repression of the miR‐1792 cluster by p53 has an important function in hypoxiainduced apoptosis. The EMBO journal28(18), 2719-2732.

Zhang, X., Wan, G., Mlotshwa, S., Vance, V., Berger, F. G., Chen, H., & Lu, X. (2010). Oncogenic Wip1 phosphatase is inhibited by miR-16 in the DNA damage signaling pathway. Cancer research70(18), 7176-7186.

Zhang, X., Wan, G., Berger, F. G., He, X., & Lu, X. (2011). The ATM kinase induces microRNA biogenesis in the DNA damage response. Molecular cell41(4), 371-383.