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Relationship: 2614


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

EMT leads to Metastasis, Breast Cancer

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 High High Agnes Aggy (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 Do not cite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
human Homo sapiens 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
Adult, reproductively mature Moderate

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, EMT

Downstream event: Metastasis

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

The “epithelial–mesenchymal transition” (EMT), a key developmental regulatory program, has been reported to play critical and intricate roles in promoting tumor invasion and metastasis in epithelium-derived carcinomas in recent years. The EMT program allows stationary and polarized epithelial cells, which are connected laterally via several types of junctions and normally interact with the basement membrane via their basal surfaces to maintain apical–basal polarity, to undergo multiple biochemical changes that enable them to disrupt cell–cell adherence, lose apical–basal polarity, dramatically remodel the cytoskeleton, and acquire mesenchymal characteristics such as enhanced migratory capacity, invasiveness, elevated resistance to apoptosis and greatly increased production of ECM components. (Boyer et al., 1993).Some of the cells undergoing EMT have the characteristics of cancer stem cells (CSCs), which are linked to cancer malignancy (Shibue & Weinberg, 2017; Shihori Tanabe, 2015a, 2015b; Tanabe, Aoyagi, Yokozaki, & Sasaki, 2015).Cancer metastasis and cancer therapeutic resistance are linked to the EMT phenomenon (Smith & Bhowmick, 2016; Tanabe, 2013). EMT causes the cell to escape from the basement membrane and metastasize by increasing the production of enzymes that breakdown extracellular matrix components and decreasing adherence to the basement membrane (Smith & Bhowmick, 2016). Therapy resistance is linked to morphological alterations seen during EMT (Smith & Bhowmick, 2016).

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

Biological Plausibility

EMT is marked by a decrease in E-cadherin and β- -catenin expression and an increase in vimentin, fibronectin, and N-cadherin expression (Irani et al., 2018). EMT is a master mechanism in cancer cells that allows them to lose their epithelial characteristics and gain mesenchymal-like qualities. EMT is the most crucial step in initiating metastasis, including metastasis to lymph nodes, because tumour cell movement is a pre-requisite for the metastatic process (Da et al., 2017). Multiple signalling pathways cause cancer cells to lose their cell-to-cell connections and cellular polarity during EMT, increasing their motility and invasive ness (Huang et al., 2017). MMPs cause E-cadherin to be cleaved, which increases tumour cell motility and invasion (Pradella et al., 2017).

Invasiveness and medication resistance are linked to the morphological and physiological changes associated with EMT (Shibue & Weinberg, 2017). In initial tumours, EMT-activated carcinoma cells penetrate the surrounding stroma (Shibue & Weinberg, 2017). EMT-activated carcinoma cells interact with the extracellular matrix protein to activate focal adhesion kinase and extracellular signal-related kinase, followed by TGFbeta and canonical and/or noncanonical Wnt pathways to develop cancer stem cell (CSC) traits, which contribute to drug resistance (Shibue & Weinberg, 2017).

Drug efflux and cell proliferation are slowed by EMT-associated downregulation of several apoptotic signalling pathways, resulting in general resistance of carcinoma cells to anti-cancer drugs (Shibue & Weinberg, 2017).Snail, an EMT-related transcription factor, promotes the production of the AXL receptor tyrosine kinase, which allows cancer cells to survive by activating AXL signalling when its ligand, growth arrest-specific protein 6 (GAS6), binds to it (Shibue & Weinberg, 2017).

EMT-activated cells are resistant to the deadly effects of cytotoxic T cells, which include increased expression of programmed cell death 1 ligand (PD-L1), which binds to the inhibitory immune-checkpoint receptor programmed cell death protein 1 (PD-1) on the cell surface of cytotoxic T cells(Shibue & Weinberg, 2017).

The reversing process of EMT, which names as a mesenchymal-epithelial transition (MET), maybe one of the candidates for the anti-cancer therapy, where the plasticity of the cell phenotype is of importance and under investigation (Shibue & Weinberg, 2017).

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

Whenever cell phenotype plasticity is crucial and under investigation, the reverse of EMT, known as the mesenchymal-epithelial transition (MET), may be one of the prospects for anti-cancer therapy (Shibue & Weinberg, 2017).

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

TGFbeta and Twist induce EMT by upregulating the expression of EMT markers such Snail, Vimentin, N-cadherin, and ABC transporters like ABCA3, ABCC1, ABCC3, and ABCC10 (Saxena et al., 2011).In the treatment with about 0.3, 3, 30 mM of doxorubicin, human mammary epithelial cells (HMLE) stably expressing Twist, FOXC2 or Snail demonstrate increased cell viability compared to control HMLE, dose-dependently (Saxena et al., 2011).

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

When Twist/FOXC2/Snail overexpressed HMLE is treated with doxorubicin for 48 hours, cell viability increases compared to control HMLE (Saxena et al., 2011).When Twist or Zeb1 were inhibited with small interference RNA (siRNA), cell viability was reduced relative to control MDAMB231 cells treated with doxorubicin for 48 hours (Saxena et al., 2011).

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

In EMT-activated cells, ABC transporters linked to drug resistance are overexpressed (Saxena et al., 2011). In EMT-activated cells, the expression of PD-L1, which binds to PD-1 on cytotoxic T cells, is upregulated, inhibiting cancer immunity and increasing resistance to cancer therapy (Shibue & Weinberg, 2017).

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
  • Understanding the association between EMT and cancer malignancy necessitates further research into the EMT-cancer stem cells (CSC) relationship. Non-CSCs in cancer can spontaneously undergo EMT and dedifferentiate into new CSCs, resulting in tumorigenic potential renewal (Marjanovic, Weinberg, & Chaffer, 2013; Shibue & Weinberg, 2017).The plastic CSC theory demonstrates bidirectional conversions between non-CSCs and CSCs, which could help EMT-activated cells acquire cancer malignancy (Marjanovic et al., 2013).
  • Long non-coding RNAs (lncRNAs) play crucial roles in many biological and pathological processes, including tumor metastasis. Kong et al reported a novel lncRNA, LINC01133 that was downregulated by TGF- β, which could inhibit epithelial–mesenchymal transition (EMT) and metastasis in colorectal cancer (CRC) cells (Kong et al.,2016). SRSF6, an alternative splicing factor that interacts directly with LINC01133, was found to enhance EMT and metastasis in CRC cells even when LINC01133 was not present. The study also found that the EMT process in CRC cells was regulated by LINC01133 in the presence of SRSF6. In vivo, the ability of LINC01133 to prevent metastasis was confirmed. Furthermore, clinical data revealed that LINC01133 expression was favourably correlated with E-cadherin and negatively correlated with Vimentin, and that low LIINC01133 expression in tumours was associated with poor CRC survival. These findings show that LINC01133, by directly binding to SRSF6 as a target mimic and inhibiting EMT and metastasis, could be used as a predictive biomarker and an effective target for anti-metastasis therapy in CRC.
  • MiR-148a inhibited Met expression directly by binding to its 30-UTR, according to Zhang et al's findings. Furthermore, reintroducing miR-148a reduced the nuclear accumulation of Snail, a transcription factor that promotes EMT, by inhibiting Met's downstream signalling, such as activating phosphorylation of AKT-Ser473 and inhibitory phosphorylation of GSK-3b-Ser9 (Zhang et al.,2015). MiR-148a, when combined, may suppress hepatoma cell EMT and metastasis by adversely regulating Met/Snail signalling.

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

EMT induces cancer invasion, metastasis (Homo sapiens)(P. Zhang et al., 2015).

EMT is related to cancer drug resistance in MCF-7 human breast cancer cells (Homo sapiens)(B. Du & Shim, 2016).


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
  • BOYER, B., & THIERY, J. P. (1993). Epithelium‐mesenchyme interconversion as example of epithelial plasticity. Apmis101(1‐6), 257-268.

Chen, S. P., Liu, B. X., Xu, J., Pei, X. F., Liao, Y. J., Yuan, F., & Zheng, F. (2015). MiR-449a suppresses the epithelial-mesenchymal transition and metastasis of hepatocellular carcinoma by multiple targets. BMC cancer15(1), 1-13.

  • Cui, B., Zhang, S., Chen, L., Yu, J., Widhopf, G. F., Fecteau, J. F., ... & Kipps, T. J. (2013). Targeting ROR1 inhibits epithelial–mesenchymal transition and metastasis. Cancer research73(12), 3649-3660.

Chen, Y., Wang, D. D., Wu, Y. P., Su, D., Zhou, T. Y., Gai, R. H., ... & Yang, B. (2017). MDM2 promotes epithelial–mesenchymal transition and metastasis of ovarian cancer SKOV3 cells. British journal of cancer117(8), 1192-1201..

  • Casas, E., Kim, J., Bendesky, A., Ohno-Machado, L., Wolfe, C. J., & Yang, J. (2011). Snail2 is an essential mediator of Twist1-induced epithelial mesenchymal transition and metastasis. Cancer research71(1), 245-254.
  • Chen, L., Mai, W., Chen, M., Hu, J., Zhuo, Z., Lei, X., ... & Zhang, D. (2017). Arenobufagin inhibits prostate cancer epithelial-mesenchymal transition and metastasis by down-regulating β-catenin. Pharmacological research123, 130-142.

Chen, L., Gibbons, D. L., Goswami, S., Cortez, M. A., Ahn, Y. H., Byers, L. A., ... & Qin, F. X. F. (2014). Metastasis is regulated via microRNA-200/ZEB1 axis control of tumour cell PD-L1 expression and intratumoral immunosuppression. Nature communications5(1), 1-12.

Da, C., Wu, K., Yue, C., Bai, P., Wang, R., Wang, G., ... & Hou, P. (2017). N-cadherin promotes thyroid tumorigenesis through modulating major signaling pathways. Oncotarget8(5), 8131.

Du, B., & Shim, J. S. (2016). Targeting epithelial–mesenchymal transition (EMT) to overcome drug resistance in cancer. Molecules21(7), 965.

  • Gao, J., Yang, Y., Qiu, R., Zhang, K., Teng, X., Liu, R., & Wang, Y. (2018). Proteomic analysis of the OGT interactome: novel links to epithelial–mesenchymal transition and metastasis of cervical cancer. Carcinogenesis39(10), 1222-1234.
  • Gumireddy, K., Li, A., Gimotty, P. A., Klein-Szanto, A. J., Showe, L. C., Katsaros, D., ... & Huang, Q. (2009). KLF17 is a negative regulator of epithelial–mesenchymal transition and metastasis in breast cancer. Nature cell biology11(11), 1297-1304.
  • Gujral, T. S., Chan, M., Peshkin, L., Sorger, P. K., Kirschner, M. W., & MacBeath, G. (2014). A noncanonical Frizzled2 pathway regulates epithelial-mesenchymal transition and metastasis. Cell159(4), 844-856.

Huang, Y., Zhao, M., Xu, H., Wang, K., Fu, Z., Jiang, Y., & Yao, Z. (2014). RASAL2 down-regulation in ovarian cancer promotes epithelial-mesenchymal transition and metastasis. Oncotarget5(16), 6734.

  • Huang, R., & Zong, X. (2017). Aberrant cancer metabolism in epithelial–mesenchymal transition and cancer metastasis: Mechanisms in cancer progression. Critical reviews in oncology/hematology115, 13-22.
  • Inukai, T., Inoue, A., Kurosawa, H., Goi, K., Shinjyo, T., Ozawa, K., ... & Look, A. T. (1999). SLUG, a ces-1-related zinc finger transcription factor gene with antiapoptotic activity, is a downstream target of the E2A-HLF oncoprotein. Molecular cell4(3), 343-352.
  • Irani, S., & Dehghan, A. (2018). The expression and functional significance of vascular endothelial-cadherin, CD44, and vimentin in oral squamous cell carcinoma. Journal of International Society of Preventive & Community Dentistry8(2), 110.
  • Jackstadt, R., Röh, S., Neumann, J., Jung, P., Hoffmann, R., Horst, D., ... & Hermeking, H. (2013). AP4 is a mediator of epithelial–mesenchymal transition and metastasis in colorectal cancer. Journal of Experimental Medicine210(7), 1331-1350.
  • Kong, J., Sun, W., Li, C., Wan, L., Wang, S., Wu, Y., ... & Lai, M. (2016). Long non-coding RNA LINC01133 inhibits epithelial–mesenchymal transition and metastasis in colorectal cancer by interacting with SRSF6. Cancer letters380(2), 476-484.
  • Kudo-Saito, C., Shirako, H., Takeuchi, T., & Kawakami, Y. (2009). Cancer metastasis is accelerated through immunosuppression during Snail-induced EMT of cancer cells. Cancer cell15(3), 195-206.
  • Liang, Y. J., Wang, Q. Y., Zhou, C. X., Yin, Q. Q., He, M., Yu, X. T., ... & Zhao, Q. (2013). MiR-124 targets Slug to regulate epithelial–mesenchymal transition and metastasis of breast cancer. Carcinogenesis34(3), 713-722.
  • Liu, Y., Wang, G., Yang, Y., Mei, Z., Liang, Z., Cui, A., ... & Cui, L. (2016). Increased TEAD4 expression and nuclear localization in colorectal cancer promote epithelial–mesenchymal transition and metastasis in a YAP-independent manner. Oncogene35(21), 2789-2800.

Liu, M., Xiao, Y., Tang, W., Li, J., Hong, L., Dai, W., ... & Xiang, L. (2020). HOXD9 promote epithelial‐mesenchymal transition and metastasis in colorectal carcinoma. Cancer medicine9(11), 3932-3943.

Marjanovic, N. D., Weinberg, R. A., & Chaffer, C. L. (2013). Cell plasticity and heterogeneity in cancer. Clinical chemistry59(1), 168-179.

  • Pirozzi, G., Tirino, V., Camerlingo, R., Franco, R., La Rocca, A., Liguori, E., ... & Rocco, G. (2011). Epithelial to mesenchymal transition by TGFβ-1 induction increases stemness characteristics in primary non small cell lung cancer cell line. PloS one6(6), e21548.
  • Pradella, D., Naro, C., Sette, C., & Ghigna, C. (2017). EMT and stemness: flexible processes tuned by alternative splicing in development and cancer progression. Molecular cancer16(1), 1-19.
  • Sarkar, T. R., Battula, V. L., Werden, S. J., Vijay, G. V., Ramirez-Peña, E. Q., Taube, J. H., ... & Mani, S. A. (2015). GD3 synthase regulates epithelial–mesenchymal transition and metastasis in breast cancer. Oncogene34(23), 2958-2967.
  • Saxena, M., Stephens, M. A., Pathak, H., & Rangarajan, A. (2011). Transcription factors that mediate epithelial–mesenchymal transition lead to multidrug resistance by upregulating ABC transporters. Cell death & disease2(7), e179-e179.
  • Shibue, T., & Weinberg, R. A. (2017). EMT, CSCs, and drug resistance: the mechanistic link and clinical implications. Nature reviews Clinical oncology14(10), 611-629.
  • Shiota, M., Zardan, A., Takeuchi, A., Kumano, M., Beraldi, E., Naito, S., ... & Gleave, M. E. (2012). Clusterin mediates TGF-β–induced epithelial–mesenchymal transition and metastasis via Twist1 in prostate cancer cells. Cancer research72(20), 5261-5272.
  • Smith, B. N., & Bhowmick, N. A. (2016). Role of EMT in metastasis and therapy resistance. Journal of clinical medicine5(2), 17.
  • Tanabe, S. (2013). Perspectives of gene combinations in phenotype presentation. World journal of stem cells5(3), 61.
  • Tanabe, S. (2015). Origin of cells and network information. World journal of stem cells7(3), 535.
  • Tanabe, S. (2015). Signaling involved in stem cell reprogramming and differentiation. World journal of stem cells7(7), 992.
  • Tanabe, S., Aoyagi, K., Yokozaki, H., & Sasaki, H. (2015). Regulated genes in mesenchymal stem cells and gastric cancer. World journal of stem cells7(1), 208.

Wang, L., Tong, X., Zhou, Z., Wang, S., Lei, Z., Zhang, T., ... & Zhang, H. T. (2018). Circular RNA hsa_circ_0008305 (circPTK2) inhibits TGF-β-induced epithelial-mesenchymal transition and metastasis by controlling TIF1γ in non-small cell lung cancer. Molecular cancer17(1), 1-18.

Wu, W. S., Heinrichs, S., Xu, D., Garrison, S. P., Zambetti, G. P., Adams, J. M., & Look, A. T. (2005). Slug antagonizes p53-mediated apoptosis of hematopoietic progenitors by repressing puma. Cell123(4), 641-653.

  • Yu, C. P., Yu, S., Shi, L., Wang, S., Li, Z. X., Wang, Y. H., ... & Liang, J. (2017). FoxM1 promotes epithelial-mesenchymal transition of hepatocellular carcinoma by targeting Snai1. Molecular medicine reports16(4), 5181-5188.
  • Yu, J., Lei, R., Zhuang, X., Li, X., Li, G., Lev, S., ... & Hu, G. (2016). MicroRNA-182 targets SMAD7 to potentiate TGFβ-induced epithelial-mesenchymal transition and metastasis of cancer cells. Nature communications7(1), 1-12.

Yue, B., Song, C., Yang, L., Cui, R., Cheng, X., Zhang, Z., & Zhao, G. (2019). METTL3-mediated N6-methyladenosine modification is critical for epithelial-mesenchymal transition and metastasis of gastric cancer. Molecular cancer18(1), 1-15.

  • Zhang, P., Sun, Y., & Ma, L. (2015). ZEB1: at the crossroads of epithelial-mesenchymal transition, metastasis and therapy resistance. Cell cycle14(4), 481-487.

Zhang, J. P., Zeng, C., Xu, L., Gong, J., Fang, J. H., & Zhuang, S. M. (2014). MicroRNA-148a suppresses the epithelial–mesenchymal transition and metastasis of hepatoma cells by targeting Met/Snail signaling. Oncogene33(31), 4069-4076.

Zhang, W., Shi, X., Peng, Y., Wu, M., Zhang, P., Xie, R., ... & Wang, J. (2015). HIF-1α promotes epithelial-mesenchymal transition and metastasis through direct regulation of ZEB1 in colorectal cancer. PloS one10(6), e0129603.