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Relationship: 1980

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, Cell Proliferation leads to Increase, lung 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
Deposition of energy leading to lung cancer adjacent High Low Vinita Chauhan (send email) Under development: Not open for comment. Do not cite EAGMST Under Review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
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus 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
Unspecific 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
All life stages High

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

Cell proliferation is a process that occurs in normal healthy cells, allowing for tissue growth and repair. It is controlled by the cell cycle, which contains specific and highly controlled checkpoints that must be passed before the cell can undergo DNA synthesis and mitosis (Pucci et al., 2000; Bertram, 2001; Eymin & Gazzeri, 2009). In cases where there are cells that contain severely damaged DNA or that are unneeded, regulatory mechanisms may arrest pro-proliferative signals and instead direct the cell cycle towards apoptosis (programmed cell death) (Portt et al., 2011). Proliferation may also be halted if the protective telomeres capping the ends of chromosomes become too short to support DNA replication; this causes cells to either enter into a state of replicative senescence (Bertram, 2001; Panov, 2005; Hanahan & Weinberg, 2011) or to undergo apoptosis (Hanahan & Weinberg, 2011). The cell cycle thus plays an important role in balancing cell proliferation with cell death to maintain homeostasis (Pucci et al., 2000; Bertram, 2001; Panov, 2005; Portt et al., 2011).

Dysregulation of the cell cycle may lead to abnormally high rates of cellular proliferation. This may occur through upregulation of pro-proliferative signalling, downregulation of anti-proliferative signaling (including alterations to proteins controlling cell cycle checkpoints), increasing resistance to pro-apoptotic signalling, and evasion of replicative senescence (Bertram, 2001; Panov, 2005; Hanahan & Weinberg, 2011). As these pro-proliferative events accumulate and cellular proliferation rates increase, cells may become increasingly tumourigenic. High rates of cellular proliferation may thus lead to the development of cancer; if these processes occur in the lung specifically, the end result may be lung cancer (Panov, 2005; Eymin & Gazzeri, 2009; Sanders & Albitar, 2010; Larsen & Minna, 2011).

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

There is a strong biological plausibility for the relationship between cell proliferation and lung cancer. This is heavily supported by the multitude of research examining the general mechanistic control of cell proliferation, and the ways in which dysregulation of cell proliferation promotes the transformation of normal cells to carcinogenic ones (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011). In this section, an overview cell proliferation processes will be provided, followed by a discussion of how these control mechanisms are modified to increase cell proliferation rates in carcinogenesis.

Cell proliferation rates are controlled by the cell cycle. The cell cycle consists of five phases: G0, G1, S, G2, and M. G0 is described as the quiescent stage, where cells are inactive in terms of cellular proliferation. The cell exits G0 and enters G1, when growth signals are initiated. G1 is known as a gap phase, where the cell begins to prepare for DNA synthesis. In the S-phase, DNA is replicated and identical sister chromatids are formed in preparation for cell division. Another gap phase, known as G2, follows DNA synthesis; during G2, cell organelles are duplicated as the cell prepares to divide. Mitosis occurs during the M-phase, which culminates in cytokinesis and the production of two genetically-identical daughter cells (Pucci et al. 2000; Eymin and Gazzeri 2009). 

Progression through the cell cycle is highly regulated and very tightly controlled, as there is a very specific and time-sensitive order of events that must occur to ensure proper cell division (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). As such, there are several key check-points that must be passed before the cell can proceed into the next phase of the cell cycle. One of the most important checkpoints is between G1 and S, known as the restriction point; it is the ‘point of no return’ in terms of DNA synthesis. This check point is controlled by RB (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009), a protein that decides whether the cell cycle progresses by integrating intra- and extra-cellular signals (Hanahan and Weinberg 2011). In its unphosphorylated state, RB binds tightly to the transcription factor E2F and thus prevents transcription of genes required for DNA synthesis. When growth signals are received by the cell, this activates the transcription of cyclin-D and cyclin-dependent kinase (CDK) 4 and CDK6. Binding of cyclin-D with CDK4 or CDK6 allows activation of the kinase function, which results in the phosphorylation of RB. Phosphorylated RB releases E2F, allowing for the transcription of genes required not only for DNA synthesis, but also for maintaining the phosphorylated state of RB throughout the DNA synthesis process (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009).

The protein product of TP53, p53, also plays an important role in controlling the cell cycle. This tumour suppressor protein is responsible for DNA quality control and for monitoring stresses within the cell. If DNA damage is detected (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011) or if cellular supplies (such as nucleotides, oxygen or glucose) are inadequate (Bertram 2001; Hanahan and Weinberg 2011), p53 is upregulated. Even in the presence of growth signals, p53 inhibits RB phosphorylation and prevents activation of E2F (Bertram 2001), thereby halting the cell cycle. This cell cycle arrest provides the DNA repair machinery time to repair the damaged DNA before the process of cell division is resumed. If the damage is too severe, p53 can trigger cell death through the process of apoptosis (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011).

Apoptosis is a non-inflammatory process of programmed cell death that is used to remove heavily damaged, defective, or unneeded cells. This process is homeostatically balanced with cell proliferation, thus allowing the organism to adapt to and change with its environment as required (Pucci et al. 2000; Bertram 2001; Panov 2005; Portt et al. 2011). A higher proportion of pro-apoptotic compared to anti-apoptotic factors will trigger a cell to undergo apoptosis (Hanahan and Weinberg 2011; Portt et al. 2011). This programmed cell death can be initiated by an intrinsic pathway mediated by cytochrome C release from the mitochondria, or by an extrinsic pathway mediated by death receptors on the plasma membrane. After initiation of apoptosis, a sequential cascade of caspase activations eventually leads to the characteristic hallmarks of apoptosis, including DNA and nuclear fragmentation, and break-down of cellular components (Panov 2005; Hanahan and Weinberg 2011; Portt et al. 2011). Key regulators of apoptosis include p53 and Bcl-2, while the main executors are the caspases (Panov 2005; Hanahan and Weinberg 2011).

In addition to cell cycle checkpoints and apoptosis, cell proliferation is also limited by telomere length. Telomeres are six-nucleotide repeats found on the ends of chromosomes that protect coding DNA from damage (Bertram 2001; Ferguson and Alt 2001; Panov 2005; Vodicka et al. 2018). After each round of replication, however, telomeres become progressively shorter due to the unidirectionality (5’-3’) of the replication machinery (Bertram 2001; Panov 2005). Eventually, the telomeres become too short to support cellular proliferation (Bertram 2001; Ferguson and Alt 2001; Hanahan and Weinberg 2011; Vodicka et al. 2018). In this case, DNA repair machinery may fuse the short telomeres (mistaken for damaged DNA) to form dicentric chromosomes (Ferguson and Alt 2001; Vodicka et al. 2018). The short telomeres may also trigger the cell to enter into a state of replicative senescence in which cell division is no longer supported (Bertram 2001; Hanahan and Weinberg 2011), or to undergo apoptosis (Hanahan and Weinberg 2011). In contrast, germ cells and stem cells are able to infinitely divide; this is due to their expression of the enzyme telomerase, which maintains telomere length (Bertram 2001). Most somatic cells, however, do not express telomerase and are thus limited in their replicative potential (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011). 

All of these processes play a role in controlling the rate of cellular proliferation in cells. In general, cellular proliferation is balanced with cell death to maintain homeostasis within an organism. If any of the above processes become aberrantly regulated such that cells begin to proliferate at excessively high rates, this may result in cancer. High rates of proliferation are considered one of the most dominant characteristics of cancer cells (Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). In fact, several of the identified hallmarks of cancer are processes that relate to increases in proliferation. These hallmarks, as stated by Hanahan 2011, include: sustained proliferative signalling, evading growth suppressors, resisting cell death, and enabling replicative immortality (Hanahan and Weinberg 2011). 

Sustained proliferative signalling allows cancer cells to carry out pro-proliferative activities even in the absence of external growth signals (Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). This may be achieved by abnormally activated proto-oncogenes which stimulate cell proliferation and thus are able to increase the level of pro-proliferative signalling within the cell (Bertram 2001; Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011; Larsen and Minna 2011). The mechanisms by which proto-oncogenes enhance proliferative signaling include: increased expression of growth factor receptors on the cell surface, increased production of ligands for growth factor receptors, constitutive activation of downstream pro-proliferative signalling molecules (Bertram 2001; Hanahan and Weinberg 2011), or structurally modified growth factor receptors that activate downstream pathways even in the absence of ligand binding (Hanahan and Weinberg 2011). In lung cancer specifically, several commonly activated proto-oncogenes include EGFR, ERBB2, MYC, KRAS, MET, CCND1, CDK4 and BCL2 (Larsen and Minna 2011).

As cells transition from normal to tumourigenic, cellular proliferation can be further enhanced by evading growth suppressors and resisting cell death (Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). This is often achieved by genetic alterations that inactivate tumour suppressor genes (TSGs). TSGs encode proteins, often involved in cell cycle checkpoints, which limit cell proliferation and promote apoptosis (Harris 1996; Bertram 2001; Vogelstein and Kinzler 2004). Two of the most common TSGs inactivated in cancer include RB1 (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011) and TP53 (Harris 1996; Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011). Inactivation of RB1 (and therefore decreased levels of RB) allows for uncontrolled proliferation by removing the restriction checkpoint in the cell cycle, thus allowing cells to easily pass from G1 to S (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011). In a similar fashion, inactivation of TP53 (and therefore decreased p53) removes DNA quality control, meaning that cells with damaged DNA are able to continue with cell proliferation unhindered (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). Loss of the pro-apoptotic p53 as well as downregulation of other pro-apoptotic factors, coupled with the upregulation of anti-apoptotic factors such as Bcl-2, further promotes cell proliferation by increasing the cell’s resistance to apoptotic pathways (Hanahan and Weinberg 2011; Portt et al. 2011). In terms of lung cancer, TSGs that are commonly inactivated include not only TP53 and RB1, but also STK11, CDKN2A, FHIT, RASSF1A, and PTEN (Larsen and Minna 2011).

Lastly, cancer cells often accumulate genetic abnormalities that allow them to overcome replicative senescence. These immortalized cancer cells are thus capable of dividing an infinite number of times. Immortalization is most often achieved in tumour cells through activation of telomerase. Expression of telomerase allows telomeres to be regenerated upon DNA replication, which prevents cells from undergoing replicative senescence or apoptosis from critically shortened telomeres (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). In lung cancer specifically, telomerase has been found to be activated in nearly all small cell lung cancer (SCLC) cases, and in over three-quarters of non-small cell lung cancer (NSCLC) cases (Panov 2005; Larsen and Minna 2011).

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

Uncertainties in this KER are as follows:

  1. Inconsistencies in results were observed in studies using radiation as a stressor.The dose threshold for the onset of proliferation and lung cancer induction varies with radiation quality, individual cell sensitivity, and confounding factors (Taylor 2013).  The latter two are also be true for chemical carcinogens (Malhotra et al., 2016).
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

Not identified.

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

Studies that directly assessed the time scale between increased cellular proliferation and lung carcinogenesis are lacking. There are some studies, however, that provide details regarding the timing between these two events. In vitro experiments using lung cancer cell lines demonstrated that expression levels of key proteins involved in the regulation of the cell cycle and/or proliferation were modified by chemical inhibitors within the first 48 hours of treatment. Delphinidin caused changes in the expression levels of EGFR, pEGFR, VEGFR2 and pVEGFR2 within the first 3 hours (Pal et al. 2013), and pargyline decreased LSD1 levels within 6 hours of treatment (Lv et al. 2012). Delphinidin-induced changes to the expression of PI3K/p110, PI3K/p85, pAKT, pERK1/2, pJNK1/2, pp38, PCNA and cyclin-D1 were documented within 48 hours of treatment (Pal et al. 2013). Similarly, CAT application led to significant declines in cell cycle checkpoint proteins cyclin-D1, CDK4 and CDK6 by 36 hours post-treatment (Wanitchakool et al. 2012). Additionally, changes to the cell cycle were evident within 24 - 48 hours of CAT treatment (Wanitchakool et al. 2012), and within 48 hours of ZIC5 knockdown with silencing RNA (Sun et al. 2016). ZIC5 knockdown also caused declines in cell proliferation by 96 hours post-transfection, and declines in clone formation after 2 weeks (Sun et al. 2016). Overall, these in vitro studies demonstrate that modifications to both cell cycle regulation and cell proliferation rates in cancer cells can be affected within hours to days of a perturbance. 

In vivo studies also provide information regarding the timescale between cell proliferation and tumourigenesis. Tumours in xenograft nude mice were detected within two weeks of NSCLC-cell inoculation (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018), with one study showing tumour detection as early as 1 week post-inoculation (Warin et al. 2014).Tumours continued to grow over the experimental period until time of harvest (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). Differences in tumour growth rates between treated and untreated mice were evident within 13 -16 days of delphinidin treatment (Pal et al. 2013), 3 weeks of ZIC5 knock-down (Sun et al. 2016), and by 27 days of either taurine, PUMA or taurine and PUMA treatment (Tu et al. 2018). At the time of xenograft nude mouse tumour harvest (which varied between 22 days and 27 weeks), there were significant differences in markers of cell proliferation and tumour size or number in mice exposed to anti-cancer compounds and their respective controls (Kassie et al. 2008; Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). In non-xenograft mice exposed to a high levels of tobacco smoke, increased markers of cell proliferation and the incidence of airway squamous metaplasia was evident upon sacrifice after 14 weeks of constant tobacco smoke exposure (Zhong et al. 2005).

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

Ingestible materials, such as wine and vitamin E, may be capable of modulating cell proliferation and thus tumourigenesis. Treatment of NSCLC cells with wine at low doses was found to inhibit proliferation of the cells, suggesting that wine may have an anti-tumourigenic effect (Barron et al. 2014). Vitamin E exposure has also been associated with anti-tumourigenesis by inducing apoptosis in proliferating endothelial cells and thus decreasing angiogenesis. This is significant, as angiogenesis is required to support tumour development (Dong et al. 2007).

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

Usually, non-cancerous cells are stimulated by growth factors originating from other cell types. For cancer cell lines, cell proliferation rates can be increased by autocrine signalling. Some cancer cells acquire the ability to produce both the growth factors and the required receptors, thus allowing the cell to respond to its own growth signals, and further stimulate more cell proliferation (Hanahan and Weinberg 2011).  

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

The domain of applicability for this KER is mammals.

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

Barron, C.C. et al. (2014), "Inhibition of human lung cancer cell proliferation and survival by wine.", Cancer Cell Int. 14(1):1–13. doi:10.1186/1475-2867-14-6.

Bertram, J.S. (2001), "The molecular biology of cancer.", Mol. Aspects. Med. 21:166–223. doi:10.1016/S0098-2997(00)00007-8.

Dong, L.F. et al. (2007), "Vitamin E Analogues Inhibit Angiogenesis by Selective Induction of Apoptosis in Proliferating Endothelial Cells: The Role of Oxidative Stress.", Cancer Res. 67(24):11906–11914. doi:10.1158/0008-5472.CAN-07-3034.

Eymin, B. & S. Gazzeri (2010), "Role of cell cycle regulators in lung carcinogenesis.", Cell Adh Migr. 4(1):114–123.

Ferguson, D.O. & F.W. Alt (2001), "DNA double strand break repair and chromosomal translocation: Lessons from animal models.", Oncogene 20(40):5572–5579.

Hanahan, D. & R.A. Weinberg (2011), "Review Hallmarks of Cancer: The Next Generation.", Cell. 144(5):646–674. doi:10.1016/j.cell.2011.02.013.

Harris, C.C. (1996), "p53 Tuimor suppressor gene : from the basic research laboratory to the clinic —an abridged historical perspective.", Carcinogenesis.1996 Jun;17(6):1187-98. doi: 10.1093/carcin/17.6.1187.

Kassie, F. et al. (2008), NIH Public Access. 1(7):1–16. doi:10.1158/1940-6207.CAPR-08-0064.

Larsen, J.E. & J. Minna (2011), "Molecular Biology of Lung Cancer: Clinical Implications.", Clin. Chest Med., 32(4):703–740. doi:10.1016/j.ccm.2011.08.003.

Lv, T. et al. (2012), "Over-Expression of LSD1 Promotes Proliferation, Migration and Invasion in Non-Small Cell Lung Cancer.", PLoS One, 7(4):1–8. doi:10.1371/journal.pone.0035065.

Pal, H.C. et al. (2013), "Delphinidin Reduces Cell Proliferation and Induces Apoptosis of Non-Small-Cell Lung Cancer Cells by Targeting EGFR / VEGFR2 Signaling Pathways.", PLoS One, 8(10):1–13. doi:10.1371/journal.pone.0077270.

Panov, S.Z. (2005), "Molecular biology of the lung cancer.", Radiology and Oncology 39(3):197–210.

Portt, L. et al. (2011), "Biochimica et Biophysica Acta Anti-apoptosis and cell survival: A review.", BioChim Biophys Acta, 1813(1):238–259. doi:10.1016/j.bbamcr.2010.10.010.

Pucci, B., M. Kasten & A. Giordano (2000), "Cell Cycle and Apoptosis 1", Neoplasia, 2(4):291–299.doi: 10.1038/sj.neo.7900101

Sanders, H.R. & M. Albitar (2010), "Somatic mutations of signaling genes in non-small-cell lung cancer.", Cancer Genet Cytogenet. 203(1):7–15. doi:10.1016/j.cancergencyto.2010.07.134.

Sun, Q. et al. (2016), "Overexpression of ZIC5 promotes proliferation in non-small cell lung cancer.", BioChem. & Biophys Res. Comm. 479:502–509. doi:10.1016/j.bbrc.2016.09.098.

Taylor, A. (2013), "Human Radiosensitivity Report of the independent Advisory Group on Ionising Radiation.", London: Health Protection Agency 2013.

Tu, S. et al. (2018), "Effect of taurine on cell proliferation and apoptosis human lung cancer A549 cells.", Oncol. Lett. 15(4):5473–5480. doi:10.3892/ol.2018.8036.

Vodicka, P. et al. (2018), "Genetic variation of acquired structural chromosomal aberrations.", Mutat Res Gen Tox En, 836(May):13–21. doi:10.1016/j.mrgentox.2018.05.014.

Vogelstein, B. & K.W. Kinzler (2004), "Cancer genes and the pathways they control.", Nat. Med, 10(8):789–799. doi:10.1038/nm1087.

Wanitchakool, P. et al. (2012), "Cleistanthoside A tetraacetate-induced DNA damage leading to cell cycle arrest and apoptosis with the involvement of p53 in lung cancer cells.", Eur J Pharmacol. 696(1–3):35–42. doi:10.1016/j.ejphar.2012.09.029.

Warin, R.F. et al. (2014), "Induction of Lung Cancer Cell Apoptosis through a p53 Pathway by [6]-Shogaol and Its Cysteine-Conjugated Metabolite M2.", Journal of Agricultural and Food Chemistry.62(6). doi:10.1021/jf405573e.

Zhong, C. et al. (2005), "MAPK / AP-1 signal pathway in tobacco smoke-induced cell proliferation and squamous metaplasia in the lungs of rats.", Carcinogenesis 26(12):2187–2195. doi:10.1093/carcin/bgi189.