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Relationship: 1516

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

Oxidative Stress leads to Liver 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
Cyp2E1 Activation Leading to Liver Cancer non-adjacent Moderate Not Specified Agnes Aggy (send email) Open for citation & comment EAGMST Under Review

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

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

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

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

There are a variety of ways in which oxidative stress can lead indirectly to cancer. The main routes involve: (a) reactive oxygen species (ROS) that cause cytotoxicity, followed by regenerative proliferation leading to cancer; (b) ROS-induced DNA damage leading to mutations in cancer-driver genes and subsequently cancer; and (c) oncogenic effects of the up-regulation of NRF2. The focus of this iKER is on (b) and (c), as the details of (a) are mapped out elsewhere.  

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
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

Moderate.

The types of genotoxic oxidative DNA damage that may occur following exposure to ROS have been extensively reviewed previously (Dizdaroglu 2012, Dizdaroglu 2015). Briefly, ROS can react with nitrogenous bases to produce various adducts that may be converted into a mutation following DNA replication. Further, ROS can damage the sugar phosphate backbone of DNA leading to abasic sites and strand breaks. If DNA damage leads to mutations that increases the expression of oncogenes or decreases the expression of tumour suppressor or DNA damage repair genes, they will transform normal cells into malignant cells. It is generally thought that liver cancer results from an accumulation of mutations in key cancer-driving genes such as TP53 and CTNNB1 (Fujimoto, et al. 2016, Shibata and Aburatani 2014a) (http://atlasgeneticsoncology.org/Tumors/HepatoCarcinID5039.html). 

In addition to DNA damage, at the molecular level, chronic activation of the Nrf2 oxidative stress response has been linked to promoting malignant transformation in pre-cancerous cells. Persistent Nrf2 activation results in the long-term up-regulation of antioxidant genes (which protect cancer cells that are known to have elevated ROS) and phase II metabolism genes (which facilitate the rapid metabolism of chemotherapeutics) (Kansanen, et al. 2013) providing a favourable environment for growth of pre-cancerous cells. The connection between chronically activated Nrf2 and cancer has been extensively studied and reviewed, most recently by Furfaro et al. (2016) and Karin and Dhar (2016). Further, Nrf2 control over cellular proliferation and differentiation has also been studied; reviewed most recently by Murakami and Motohashi  (2015).   

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

Not all agents that cause ROS in the liver cause liver cancer. Thus, there are additional modulating factors that must be considered when determining whether a ROS-producing chemical will cause liver cancer.

Overall, ROS-dependent DNA damage causing harmful mutations is known to occur. However, the specific mechanism and the quantitative relationships by which these mutations promote malignant transformation are incompletely understood.

Increase in NRF2 expression is associated with occurrence and recurrence of hepatocellular carcinoma; however, the mechanism is incompletely understood.

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
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
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
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

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

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

Beddowes, E.J., Faux, S.P., Chipman, J.K., 2003. Chloroform, carbon tetrachloride and glutathione depletion induce secondary genotoxicity in liver cells via oxidative stress. Toxicology 187, 101-115.

Ding, W., Petibone, D.M., Latendresse, J.R., Pearce, M.G., Muskhelishvili, L., White, G.A., Chang, C.-., Mittelstaedt, R.A., Shaddock, J.G., McDaniel, L.P., Doerge, D.R., Morris, S.M., Bishop, M.E., Manjanatha, M.G., Aidoo, A., Heflich, R.H., 2012. In vivo genotoxicity of furan in F344 rats at cancer bioassay doses. Toxicol. Appl. Pharmacol. 261, 164-171.

Dizdaroglu, M., 2015. Oxidatively induced DNA damage and its repair in cancer. Mutat. Res. Rev. Mutat. Res. 763, 212-245.

Dizdaroglu, M., 2012. Oxidatively induced DNA damage: mechanisms, repair and disease. Cancer Lett. 327, 26-47.

Fujimoto, A., Furuta, M., Totoki, Y., Tsunoda, T., Kato, M., Shiraishi, Y., Tanaka, H., Taniguchi, H., Kawakami, Y., Ueno, M., Gotoh, K., Ariizumi, S., Wardell, C.P., Hayami, S., Nakamura, T., Aikata, H., Arihiro, K., Boroevich, K.A., Abe, T., Nakano, K., Maejima, K., Sasaki-Oku, A., Ohsawa, A., Shibuya, T., Nakamura, H., Hama, N., Hosoda, F., Arai, Y., Ohashi, S., Urushidate, T., Nagae, G., Yamamoto, S., Ueda, H., Tatsuno, K., Ojima, H., Hiraoka, N., Okusaka, T., Kubo, M., Marubashi, S., Yamada, T., Hirano, S., Yamamoto, M., Ohdan, H., Shimada, K., Ishikawa, O., Yamaue, H., Chayama, K., Miyano, S., Aburatani, H., Shibata, T., Nakagawa, H., 2016. Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer. Nat. Genet. 48, 500-509.

Furfaro, A.L., Traverso, N., Domenicotti, C., Piras, S., Moretta, L., Marinari, U.M., Pronzato, M.A., Nitti, M., 2016. The Nrf2/HO-1 Axis in Cancer Cell Growth and Chemoresistance. Oxid Med. Cell. Longev 2016, 1958174.

Hickling, K.C., Hitchcock, J.M., Oreffo, V., Mally, A., Hammond, T.G., Evans, J.G., Chipman, J.K., 2010. Evidence of oxidative stress and associated DNA damage, increased proliferative drive, and altered gene expression in rat liver produced by the cholangiocarcinogenic agent Furan. Toxicol. Pathol. 38, 230-243.

Jackson, A.F., Williams, A., Recio, L., Waters, M.D., Lambert, I.B., Yauk, C.L., 2014. Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan. Toxicol. Appl. Pharmacol. 274, 63-77.

Jaramillo, M.C., Zhang, D.D., 2013. The emerging role of the Nrf2-Keap1 signaling pathway in cancer. Genes Dev. 27, 2179-2191.

Kansanen, E., Kuosmanen, S.M., Leinonen, H., Levonen, A.L., 2013. The Keap1-Nrf2 pathway: Mechanisms of activation and dysregulation in cancer. Redox Biol. 1, 45-49.

Karin, M., Dhar, D., 2016. Liver carcinogenesis: from naughty chemicals to soothing fat and the surprising role of NRF2. Carcinogenesis 37, 541-546.

Linhart, K., Bartsch, H., Seitz, H.K., 2014. The role of reactive oxygen species (ROS) and cytochrome P-450 2E1 in the generation of carcinogenic etheno-DNA adducts. Redox Biol. 3, 56-62.

Moser, G.J., Foley, J., Burnett, M., Goldsworthy, T.L., Maronpot, R., 2009. Furan-induced dose–response relationships for liver cytotoxicity, cell proliferation, and tumorigenicity (furan-induced liver tumorigenicity). Experimental and Toxicologic Pathology 61, 101-111.

Murakami, S., Motohashi, H., 2015. Roles of Nrf2 in cell proliferation and differentiation. Free Radic. Biol. Med. 88, 168-178.

Poungpairoj, P., Whongsiri, P., Suwannasin, S., Khlaiphuengsin, A., Tangkijvanich, P., Boonla, C., 2015. Increased Oxidative Stress and RUNX3 Hypermethylation in Patients with Hepatitis B Virus-Associated Hepatocellular Carcinoma (HCC) and Induction of RUNX3 Hypermethylation by Reactive Oxygen Species in HCC Cells. Asian Pac. J. Cancer. Prev. 16, 5343-5348.

Schulze, K., Imbeaud, S., Letouze, E., Alexandrov, L.B., Calderaro, J., Rebouissou, S., Couchy, G., Meiller, C., Shinde, J., Soysouvanh, F., Calatayud, A.L., Pinyol, R., Pelletier, L., Balabaud, C., Laurent, A., Blanc, J.F., Mazzaferro, V., Calvo, F., Villanueva, A., Nault, J.C., Bioulac-Sage, P., Stratton, M.R., Llovet, J.M., Zucman-Rossi, J., 2015. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat. Genet. 47, 505-511.

Takahashi, S., Hirose, M., Tamano, S., Ozaki, M., Orita, S., Ito, T., Takeuchi, M., Ochi, H., Fukada, S., Kasai, H., Shirai, T., 1998. Immunohistochemical detection of 8-hydroxy-2'-deoxyguanosine in paraffin-embedded sections of rat liver after carbon tetrachloride treatment. Toxicol. Pathol. 26, 247-252.

Totoki, Y., Tatsuno, K., Covington, K.R., Ueda, H., Creighton, C.J., Kato, M., Tsuji, S., Donehower, L.A., Slagle, B.L., Nakamura, H., Yamamoto, S., Shinbrot, E., Hama, N., Lehmkuhl, M., Hosoda, F., Arai, Y., Walker, K., Dahdouli, M., Gotoh, K., Nagae, G., Gingras, M.C., Muzny, D.M., Ojima, H., Shimada, K., Midorikawa, Y., Goss, J.A., Cotton, R., Hayashi, A., Shibahara, J., Ishikawa, S., Guiteau, J., Tanaka, M., Urushidate, T., Ohashi, S., Okada, N., Doddapaneni, H., Wang, M., Zhu, Y., Dinh, H., Okusaka, T., Kokudo, N., Kosuge, T., Takayama, T., Fukayama, M., Gibbs, R.A., Wheeler, D.A., Aburatani, H., Shibata, T., 2014. Trans-ancestry mutational landscape of hepatocellular carcinoma genomes. Nat. Genet. 46, 1267-1273.

Wacker, M., Wanek, P., Eder, E., 2001. Detection of 1,N2-propanodeoxyguanosine adducts of trans-4-hydroxy-2-nonenal after gavage of trans-4-hydroxy-2-nonenal or induction of lipid peroxidation with carbon tetrachloride in F344 rats. Chem. Biol. Interact. 137, 269-283.

Wang, E., Chen, F., Hu, X., Yuan, Y., 2014. Protective effects of apigenin against furan-induced toxicity in mice. Food Funct. 5, 1804-1812.

Wang, Y., Millonig, G., Nair, J., Patsenker, E., Stickel, F., Mueller, S., Bartsch, H., Seitz, H.K., 2009. Ethanol-induced cytochrome P4502E1 causes carcinogenic etheno-DNA lesions in alcoholic liver disease. Hepatology 50, 453-461.

Winczura, A., Zdzalik, D., Tudek, B., 2012. Damage of DNA and proteins by major lipid peroxidation products in genome stability. Free Radic. Res. 46, 442-459.

Xiang, M., Namani, A., Wu, S., Wang, X., 2014. Nrf2: Bane or blessing in cancer? J. Cancer Res. Clin. Oncol. 140, 1251-1259.

Zhang, T.T., Zhao, G., Li, X., Xie, F.W., Liu, H.M., Xie, J.P., 2015. Genotoxic and oxidative stress effects of 2-amino-9H-pyrido[2,3-b]indole in human hepatoma G2 (HepG2) and human lung alveolar epithelial (A549) cells. Toxicol. Mech. Methods 25, 212-222.