Ada Wang, Jessica Wu, Raven Boss, Camelia Ursu, Altamish Bukhari, Mehul Gupta, Sunand Kannappan*
Calgary, AB
*This project was done as part of the Youreka Canada Program.
INTRODUCTION
The CALM3 gene belongs to a family of proteins that mediates the control of a large number of enzymes, ion channels, and other proteins through calcium binding, and its activity is important in the regulation of the cell cycle and cytokinesis. On the other hand, the ARAF gene has been discovered as being involved in the transduction of myogenic signals from the cell membrane to the nucleus and serves as a positive regulator of myogenic differentiation by inducing cell cycle arrest. The PRKCB gene belongs to the Protein kinase C family (PKC), which is a family of serine and threonine-specific protein kinases that can be activated by calcium. They are known to be involved in diverse cellular signaling pathways and sometimes serve as major receptors for classes of tumor promoters. The PRKCB gene is a protein coding gene; it potentially activates oxidative stress-induced apoptosis and endothelial cells proliferation. Moreover, the SHC1 gene is also a protein coding gene that couples with active growth factor receptors to signaling pathways and is indirectly related to the tumor suppressor p53. Lastly, the SHC2 gene acts as a signaling adapter that couples activated growth factor receptors to signaling pathway in neurons and is involved in the signal transduction pathways of neurotrophin- activated Trk receptors in cortical neurons (Database, 2019). Although mutations of these genes have been previously identified within LGG tumors, the impact on genomic or transcriptomic aberrations of these genes on patient survival is unknown. In specific, the role of these genes as positive prognostic factors (tumor suppressor genes) or negative prognostic factors (oncogenes) is not well described in the literature. As such, the objective of our cancer study is to determine whether the genes within the gene cascade can act as reasonable prognostic biomarkers to reveal a patient’s chances of survival depending on the level of expression of those certain genes and how the genes contribute to the progression of LGG.
METHODS
1. First, the gene pathway associated with LGG’s progression was identified through the online medical website KEGG (KEGG, 2019), which illustrates the genes associated with each tumour type. Each gene was then searched on PubMed to identify whether the gene already had prognostic research completed on it.
2. Data was then collected from cBioPortal (CBioPortal, n.d.). In specific, the mRNA expressions of the genes in LGG patients and their corresponding clinical data (namely survival in months and overall survival status). This data allows further survival analysis, stratified by gene expression profiles.
3. The top and bottom quartiles (low and high expression respectively) of the sorted patient data were used to produce a Kaplan Meier curve in the application Prism (GraphPad, 2008), which allows us to see the overall survival rate and its corresponding survival time in months. This allowed the effects of low and high expression of the gene of interest on patients’ survival to be visualized. The significance of the difference in survival between the quartiles was determined using the Log Rank Sum Test.
RESULTS
Figure 1. ARAF Kaplan Meier graph to find the survival rates and significance for the top and bottom quartiles.
Figure 2. CALM3 Kaplan Meier graph to find the survival rates and significance for the top and bottom quartiles.
Figure 3. SHC1 Kaplan Meier graph to find the survival rates and significance for the top and bottom quartiles.
Figure 4. PRKCB Kaplan Meier graph to find the survival rates and significance for the top and bottom quartiles.
Figure 5. SHC2 Kaplan Meier graph to find the survival rates and significance for the top and bottom quartiles
We found that the ARAF, SHC1, SHC2, and CALM3 genes of the LGG gene pathway all provided vital insight into the prognosis of LGG. CALM3, ARAF and SHC1 all showed significantly lower survival in the patients with high expression than in low expression. The SHC2 gene showed significantly shorter survival in patients with low gene expression versus those with higher gene expression. Although we did observe that patients with low expression of PRKCB did have better survival outcomes than those with high expression, this relationship was not significant.
DISCUSSION
In theory, with higher expression and a less favorable subsequent prognosis, a gene could be concluded to be overly expressed in low-grade glioma, which results in gene mutation, when it is likely to be more dormant in normal circumstances. Consequently, the high expression of the ARAF, SHC1, and CALM3 genes resulted in lower survival rates, which means that these genes could potentially be oncogenes. Conversely, due to higher expression of SHC2 leading to a higher survival possibility, it could be inferred that higher expression of this gene results in moderation of the tumor and could, thus, be concluded to be a tumor suppressor gene. It should be noted that the PRKCB gene did not significantly stratify patient survival, and as such, further evaluation of PRKCB contribution to LGG is necessary. Of course, our study is extremely limited in the technology and data gathered. There will be errors in our calculations of survival rates and cutoffs. Our next step would be to connect to larger and more influential research centers and see if we can work with them to obtain more up-to-date and reliable data. We would also research to see how the subsequent oncogenes can be regulated through certain drugs and medication.
CONCLUSION
In the end, it was discovered that a more favorable prognosis resulted when the ARAF, SHC1 and CALM3 of the gene cascade associated with the progression of LGG were expressed less, which signified these genes potentially being oncogenes. On the contrary, the SHC2 gene exhibited higher expression when more favorable prognosis occurred, which enables us to reasonably conclude that it is a tumor suppressor. However, PRKCB did not appear to be significantly predictive of patient survival. For these reasons, all postulated genes, except PRKCB, could serve as possible prognostic biomarkers for the survival of LGG patients.
AUTHOR CONTRIBUTIONS
Methods were provided by Sunand and Mehul, and the results were generated and described by Ada, Jessica, Raven and Altamish.
REFERENCES
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ABOUT THE FIRST AUTHOR - ADA WANG
I’m a 16 year old high school student from Calgary who loves to discover things I don’t know about. I also love science and math, and especially reading scientific journals from various magazines and websites. My friends and I really enjoy discussing and researching about science. It is this ambition that drove me to research about cancer. I have previously participated in Junior Achievement South Alberta business model program and won several awards in music and math. In my spare time, I like reading, writing, and watching Crash Course.