Booth Id:
CBIO028
Category:
Computational Biology and Bioinformatics
Year:
2019
Finalist Names:
Misra, Suchir (School: Jericho High School)
Abstract:
Craniosynostosis, a congenital disease that affects 1 in 2000-2500 people, shares genetic mutations implicated in cancer progression, yet has not been studied as a potential predictive marker for cancers. In this study, a computational approach was designed to investigate craniosynostosis as a predictive marker of future cancer diagnosis. Craniosynostosis-mutated genes were identified using a literature review (N = 15) then molecular signatures in these genes were identified using the Molecular Signatures Database (MSigDB). Kaposi’s sarcoma-associated herpesvirus (Kshv) Infection Angiogenic Markers Up signatures (p < 1.96*10-9) and Pathways in Cancer signatures (p < 3.71*10-9) were highly prevalent in craniosynostosis-mutated genes, highlighting the predictive power of craniosynostosis for cancers. The Cancer Gene Atlas (TCGA) was then used to rank cancers (N = 33) by mutation percentage (1 = highest mutation percentage, 2 = second-highest mutation percentage, etc.) for each craniosynostosis-mutated gene, from which the average rank was calculated. Craniosynostosis was highly correlated with endometrial cancer with an average rank of 1.12 and forty-five craniosynostosis-mutated genes had higher mutation percentages than the median mutation percentage of all mutated genes in endometrial cancer (N = 22,162; p<2.94*10-5), indicating that craniosynostosis may serve as a predictive biomarker for endometrial cancer. Therefore, this study supports early intervention and screening for at-risk women. In the future, similar computational approaches may determine predictive markers for disorders that are difficult to prognose and diagnose, such as pancreatic cancer, and other high mortality diseases.
Awards Won: