UROP Proceedings 2020-21

School of Science Division of Life Science 35 Computational Study of Long Noncoding RNAs in Cancer Supervisor: WANG Jiguang / LIFS Student: LEE Jooran / BIEN Course: UROP1100, Fall The identification of the novel lncRNA in glioma was the primary focus of the research, and several mistakes were identified at the beginning of this semester while revising the previous work. An incomplete run of the FLORA, and several coding mistakes produced the wrong file with some inconsistent parts. To produce a correct file, I firstly used FLORA to generate the count matrix, followed by the FPKM matrix by normalizing the count data using the IDH samples (wildtype + mutant). There were total 462 IDH samples, and 301 were IDH-wildtype patient samples, and the rest 152 samples were IDH-mutant ones among them. Then, I focused on the IDH wildtype (wt) samples for the downstream analysis of the TMZ-treated patients, and several differentially expressed lncRNAs between recurrent and initial glioma were identified. Computational Study of Long Noncoding RNAs in Cancer Supervisor: WANG Jiguang / LIFS Student: WU Yurong / CHEM Course: UROP1100, Fall Cancer is one of the deadliest diseases in the world. Glioblastoma is a type of brain cancer that recurs easily even if the patient is treated in best effort. In this project, the focus is the analysis of long non-coding RNAs (lncRNAs) of glioblastoma with computational methods such as Python and R, and statistics using data of expression levels of lncRNAs in initial and recurrent tumors from a group of 305 patients (denoted as from G001 to G305). Using statistics, we can find out the lncRNAs that are the most upregulated and downregulated in recurrent tumors. These are the important genes that is worth further analysis including the investigation of their secondary and tertiary structures of the transcripts. Computational Study of Long Noncoding RNAs in Cancer Supervisor: WANG Jiguang / LIFS Student: WU Yurong / CHEM Course: UROP2100, Spring Cancer is a deadly incurable disease. Many scientists are working on it, hoping to discover new methods of treatment. Despite the large number of researches conducted, no effective treatments can be developed. In the light of this, we are using long non-coding RNAs (lncRNAs) as a new direction to analyze cancers. In this project, we use glioblastoma as our first example. Our objective is to find out the mechanisms of various characteristics of cancer such as proliferation. By using statistics and bioinformatics, a large number of genes were analyzed in various aspects including expression levels in initial and recurrent tumors, and conservation scores. These results can later provide us some clues to the real mechanisms of cancers.

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