Integrate Gene and Long non-coding RNA expression profile to predict radiation sensitivity
Date Issued
2015
Date
2015
Author(s)
Wang, Wei-An
Abstract
Radiotherapy has been a standard procedure in cancer treatment. With the improvement of medical technology, the conventional radiation therapy is combined with image-guided method (processed by Computed Tomography) and treatment planning, provides high control rate in tumor region. However, most of the radiotherapy planning is based on the cancer type rather than the radiosensitivity of individuals. Thus, to identify the radiosensitivity of each patient to improve the treatment planning and to reduce the side effect becomes an important issue in the era of personal medicine. Recently, there are more and more studies that focus on the function of long non-coding RNA (LncRNA) which were once regarded as “dark matter”. Although there are several studied that discuss the relationship between gene expression and radiation, LncRNA is relatively new to the field of radiation. In this study, the process can be described into three parts: First, we identified radiation responded genes and LncRNAs which has differential expression in GSE26835 with total 1086 samples. Second, we integrate the NCI-60 cell line data, which has been regarded as standard cell lines and its radiation parameter to filter out the radiosensitivity-related pattern in the genes and LncRNAs that identified in the previous step. To select appropriate variables to build up prediction model, we used genetic algorithms to identify 20 probe sets that have the best performance in predicting the radiosensitivity of cell lines. Lastly, we applied two real glioblastoma multiforme (GBM) – TCGA and GSE16011 to validate the 20 probes sets can be used to predict the radiosensitivity of patients or not. We defined patients into radiosensitivity group and radioresistance group, and analyze their survival curve through Log rank test and cox regression test. To prove these 20 probe sets are radiotherapy-specific, we also examined the patients that did not receive radiotherapy. As expected, there is no significant difference in the survival of Non-RT patients. Compares to other research, our study first integrates the expression profile of genes and LncRNAs. Moreover, we can predict the radiosensitivity once we have the data of patients instead of collecting a series of patients, which makes our method more practically and can be used in clinical situation. To sum up, our study identifies 20 probe sets that can predict the radiosensitivity of patient with high throughput microarray. We are looking forward to our research can help doctors and medical physicians to improve the prognosis of cancer patients.
Subjects
Long non-coding RNA
radiosensitivity
microarray
biomarkers
bioinformatics
SDGs
Type
thesis
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