{"id":{"repo_id":"wustl","oai_identifier":"oai:openscholarship.wustl.edu:eng_etds-2215"},"canonical_url":"https://search.dev.ndltd.org/etd/wustl/oai:openscholarship.wustl.edu:eng_etds-2215","repository":{"repo_id":"wustl","name":"Washington University in St. Louis","base_url":"https://openscholarship.wustl.edu/do/oai/"},"display":{"title":"Pan-Cancer Analysis of RNA Dysregulation, Somatic Mutations, and Matrix Stiffness using Bioinformatics Approaches","abstract":"Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions in cancer, revealing key pathways associated with oncogenic viruses. Second, I established a pipeline to identify somatic mutations using RNA-seq, highlighting mutations missed by DNA sequencing and their clinical implications. Third, I designed a computational model to predict tumor matrix stiffness using RNA-seq data, uncovering how matrix stiffness influences tumor complexity, immune infiltration, and extracellular matrix alterations. These findings provide a deeper understanding of the molecular and biophysical characteristics of tumors, identify novel somatic mutations, and offer insights into potential therapeutic strategies. This work demonstrates the importance of combining multi-omics data with computational approaches to unravel cancer’s complexity and advance precision oncology.","abstract_html":"Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions in cancer, revealing key pathways associated with oncogenic viruses. Second, I established a pipeline to identify somatic mutations using RNA-seq, highlighting mutations missed by DNA sequencing and their clinical implications. Third, I designed a computational model to predict tumor matrix stiffness using RNA-seq data, uncovering how matrix stiffness influences tumor complexity, immune infiltration, and extracellular matrix alterations. These findings provide a deeper understanding of the molecular and biophysical characteristics of tumors, identify novel somatic mutations, and offer insights into potential therapeutic strategies. This work demonstrates the importance of combining multi-omics data with computational approaches to unravel cancer’s complexity and advance precision oncology.","abstract_has_math":false,"creators":["Tang, Gongyu"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering & Materials Science","degree_department":null,"school":null,"contributors":["Jessica Wagenseil","Xiaowei Wang; Jessica Wagenseil; Jin Zhang; Matthew Bersi; Ramesh Agarwal"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-20T08:00:00Z","date_published":"2024-12-20T08:00:00Z","updated_at":"2026-07-24T06:13:55Z","subjects":["Bioinformatics;Cancer;Genomics;Machine learning;Mutation;Tumor matrix stiffness","Engineering","Mechanical Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/1142"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/1142","href":"https://openscholarship.wustl.edu/eng_etds/1142","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/hm8r-px05","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jessica Wagenseil","Xiaowei Wang; Jessica Wagenseil; Jin Zhang; Matthew Bersi; Ramesh Agarwal"]},{"key":"dc:creator","label":"Author","values":["Tang, Gongyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-13T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering & Materials Science","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bioinformatics;Cancer;Genomics;Machine learning;Mutation;Tumor matrix stiffness","Engineering","Mechanical Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/hm8r-px05","https://openscholarship.wustl.edu/eng_etds/1142"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions in cancer, revealing key pathways associated with oncogenic viruses. Second, I established a pipeline to identify somatic mutations using RNA-seq, highlighting mutations missed by DNA sequencing and their clinical implications. Third, I designed a computational model to predict tumor matrix stiffness using RNA-seq data, uncovering how matrix stiffness influences tumor complexity, immune infiltration, and extracellular matrix alterations. These findings provide a deeper understanding of the molecular and biophysical characteristics of tumors, identify novel somatic mutations, and offer insights into potential therapeutic strategies. This work demonstrates the importance of combining multi-omics data with computational approaches to unravel cancer’s complexity and advance precision oncology."]},{"key":"dc:title","label":"Title","values":["Pan-Cancer Analysis of RNA Dysregulation, Somatic Mutations, and Matrix Stiffness using Bioinformatics Approaches"]}]}],"canonical_facts":{"dc:contributor":["Jessica Wagenseil","Xiaowei Wang; Jessica Wagenseil; Jin Zhang; Matthew Bersi; Ramesh Agarwal"],"dc:creator":["Tang, Gongyu"],"dc:date.available":["2025-01-13T08:00:00Z"],"dc:description.abstract":["Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions in cancer, revealing key pathways associated with oncogenic viruses. Second, I established a pipeline to identify somatic mutations using RNA-seq, highlighting mutations missed by DNA sequencing and their clinical implications. Third, I designed a computational model to predict tumor matrix stiffness using RNA-seq data, uncovering how matrix stiffness influences tumor complexity, immune infiltration, and extracellular matrix alterations. These findings provide a deeper understanding of the molecular and biophysical characteristics of tumors, identify novel somatic mutations, and offer insights into potential therapeutic strategies. This work demonstrates the importance of combining multi-omics data with computational approaches to unravel cancer’s complexity and advance precision oncology."],"dc:identifier":["https://doi.org/10.7936/hm8r-px05","https://openscholarship.wustl.edu/eng_etds/1142"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."],"dc:subject":["Bioinformatics;Cancer;Genomics;Machine learning;Mutation;Tumor matrix stiffness","Engineering","Mechanical Engineering"],"dc:title":["Pan-Cancer Analysis of RNA Dysregulation, Somatic Mutations, and Matrix Stiffness using Bioinformatics Approaches"],"thesis:degree_discipline":["Mechanical Engineering & Materials Science","McKelvey School of Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T06:13:55Z"}