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Thesis
Transcriptome analysis on publicly available hepatocellular carcinoma datasets to identify hepatitis B virus-specific long non-coding RNAs
Master of Science (M.S.), Drexel University
May 2024
DOI:
https://doi.org/10.17918/00010601
Abstract
Liver cancer remains highly prevalent globally. With 90% of primary liver cancer cases attributed to Hepatocellular Carcinoma (HCC), this malignancy remains a significant focus of research efforts. As instances of HCC continue to rise, new research is needed to understand the underlying mechanisms and develop therapies to slow this progression. Chronic inflammation driven by long-term Hepatitis B (HBV) infection is attributed to approximately 54% of all HCC cases. While studies have been done to look at the effects of HBV infection in altering the coding genes driving HCC in hepatocytes, investigations into HBV's impact on long non-coding RNA (lncRNA) are lacking. LncRNA are non-protein coding transcripts that are greater than 200 nucleotides in length and play a variety of regulatory roles within the cell. Here, we sought to utilize publicly available RNA sequencing data using computational tools to identify dysregulated lncRNAs within HBV-infected HCC patients. Typically, transcriptome analyses compare tumor samples with adjacent tumor tissue from patients. However, our analysis suggests that such comparisons can be misleading. It's crucial to include data from healthy individuals for a comprehensive understanding of gene expression levels. In our studies, incorporating tumor, tumor-adjacent, and normal data, we identified the upregulation of lncRNA SNHG15 specifically in HBV-infected patient samples. Moreover, increased SNHG15 expression in hepatitis-infected HCC patients correlated with a poorer prognosis. We have validated these results and showed that SNHG15 is indeed upregulated in cell lines infected with HBV. Overall, our results emphasized the importance of using disease-free samples in transcriptome analyses and moving beyond a strictly tumor-adjacent versus tumor sample comparison model.
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Details
- Title
- Transcriptome analysis on publicly available hepatocellular carcinoma datasets to identify hepatitis B virus-specific long non-coding RNAs
- Creators
- Aaron Slifer
- Contributors
- Joshua Chang Mell (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- 36 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- Medicine (Graduate); College of Medicine; Hematology and Oncology; Drexel University
- Other Identifier
- 991021877012604721