Modeling and Validation of Gene Networks in Breast and Pancreatic Cancer

dc.contributor.advisorLin, Chin-Yo
dc.contributor.committeeMemberDas, Joydip
dc.contributor.committeeMemberUmetani, Michihisa
dc.contributor.committeeMemberChung, Sang-Hyuk
dc.contributor.committeeMemberFeng, Qin
dc.creatorHo, Charles
dc.creator.orcid0000-0003-0863-6292
dc.date.accessioned2023-01-14T23:25:28Z
dc.date.createdMay 2022
dc.date.issued2022-04-30
dc.date.updated2023-01-14T23:25:29Z
dc.description.abstractOmics data have been growing at an extraordinary pace and are on track to hit exabytes of data within the decade. With the ability to quickly sequence whole genomes within a day, there are many opportunities to use this data in the study of disease pathophysiology and in therapy development. With 1.9 million new cases of cancer and an estimated 610,000 deaths each year in the United States, it would be beneficial to have an integrated bioinformatic pipeline to quickly and efficiently integrate this growing amount of publicly available data in cancer related omics research. In our studies, we utilized a multi-omics approach to develop an integrated bioinformatic pipeline which combines machine learning methods, the latest bioinformatic tools, and various large omics data sets for uncovering disease mechanisms. We then took advantage of large patient cohort databases to establish a methodology for testing and validating the clinical relevance of these novel mechanisms. Using this approach, we conducted three studies in breast and pancreatic cancer. First, we focused on the oncogenic mechanisms of alcohol in the development and progression of breast cancer. We conducted a secondary analysis of our previous published transcriptome data and used our integrated pipeline to discover alcohol-regulated metabolic genes associated with oncogenic calcium signaling. The role calcium signaling in cancer promoting actions of alcohol was validated experimentally and the clinical relevance of these genes was established by their expression profiles in publicly available patient data. Our second study used a similar pipeline to find gene networks which play an important role in mediating the cytotoxic effects of liver x receptor (LXR) β activation in pancreatic cancers. This study revealed that LXRβ activity up-regulated the genes involved in pro-apoptotic fatty acid production and endoplasmic reticulum stress genes and down-regulated cell cycle genes. Many of these genes showed clinically relevant expression profiles in patient samples. Finally, our third study integrated machine learning with our established pipeline to predict targetable mechanisms in novel predicted pancreatic cancer molecular subtypes. We uncovered three novel subtypes with targetable immune associated mechanisms and subtype-specific gene expression profiles with therapeutic and prognostic implications.
dc.description.departmentBiology and Biochemistry, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Ho C, Lin CY. Genes Associated with Calcium Signaling are Involved in Alcohol-Induced Breast Cancer Growth. Alcohol Clin Exp Res. 2021 Jan;45(1):79-91.
dc.identifier.urihttps://hdl.handle.net/10657/13316
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectBioinformatics
dc.subjectMulti-omics
dc.subjectCancer
dc.titleModeling and Validation of Gene Networks in Breast and Pancreatic Cancer
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2024-05-01
local.embargo.terms2024-05-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentBiology and Biochemistry, Department of
thesis.degree.disciplineBiology
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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