UROP Proceedings 2020-21

School of Science Division of Life Science 36 Computational Study of Long Noncoding RNAs in Cancer Supervisor: WANG Jiguang / LIFS Student: WU Yurong / CHEM Course: UROP3100, Summer Cancer is a deadly, incurable disease. Although many researchers are working on the problem, there is no breakthrough in the development of treatment of the disease. We are using long noncoding RNAs (lncRNAs) as a new approach to tackle the problem. In the past, scientists once thought that lncRNAs had little or no biological function, but later it was discovered that they are involved in many cellular activities, so this will be a novel direction for studying the mechanism of cancers. We used glioblastoma as an example and study the lncRNAs expressed. Then, we will try to study other kinds of tumors using lncRNAs. We hope that mechanisms of cancers can be found so that we can develop new treatments. The Application of Big Data Technologies in Precision Cancer Medicine Supervisor: WANG Jiguang / LIFS Student: CHOI Dawon / BIOT Course: UROP1100, Spring UROP2100, Summer Conducting cellular deconvolution on tumor tissues is crucial for understanding the cause and mechanism of cancer, and for identifying appropriate treatments for the patients. CIBERSORTx is one analytical tool that takes scRNA-seq data and conducts a deconvolution analysis by providing the proportion of different cell types in a mixture. In this project, CIBERSORTx was used to analyze tissue samples from GBM patients. To benchmark the various parameters provided by CIBERSORTx and evaluate the deconvolution analysis of GBM tissue samples using this tool, the procedure was repeated on two different datasets. Additional datasets will be included in future analysis to validate the robustness of the method, and the pipeline will then be applied in analyzing immune cell types in glioma evolution. The Application of Big Data Technologies in Precision Cancer Medicine Supervisor: WANG Jiguang / LIFS Student: SHAO Zhihao / BCB Course: UROP1100, Spring Chromosome instability (CIN) is a type of genomic instability in which the rate that chromosomes or large portions of chromosomes change increase. As one of the most common causes of aneuploidy and chromosome rearrangement, CIN has been reported to result in a high burden of genomic aberrations such as loss of heterogeneity (LOH) and even drug resistance. With the emergence of copy number variation (CNV) profiles based next-generation sequencing (NGS) results, CIN characteristics of one tumor sample could be analyzed and estimated in the absence of single cell data. In this report, several quantitative measurements of CIN based on CNV profiles are proposed and performed on glioblastoma multiform (GBM) samples to validate their feasibility.