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Metabolomics and Machine Learning for Early-Stage Cancer Diagnosis
Metabolomics and Machine Learning for Early-Stage Cancer Diagnosis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102902
- ISBN
- 9798263326968
- DDC
- 616.99465
- 저자명
- Sah, Samyukta.
- 서명/저자
- Metabolomics and Machine Learning for Early-Stage Cancer Diagnosis
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 316 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Fernandez, Facundo M.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약Amongst all omics sciences, metabolomics is the most recent and is rapidly advancing as one of the predominant methodologies for early disease diagnosis and precision medicine. Metabolomics involves the high-throughput analysis of low molecular weight metabolites and their interactions with biological networks. Metabolomics studies involve measuring the abundance of hundreds to thousands of metabolites in biological fluids, tissues, and cells, and provide instantaneous snapshot of the status of the biological system. For cancer research, metabolomics is a powerful platform as it enables the identification of metabolic alterations useful in diagnosis, prognosis, and therapeutics. However, because of the complexity of the metabolome, no single analytical platform can capture the complete metabolic profile of a biological system. Thus, the use of complementary techniques allows for a more comprehensive analysis. Mass spectrometry (MS) is one of the commonly used techniques in metabolomics. Due to its high sensitivity and high-resolution, MS based metabolomics studies can provide a wide breath of knowledge of cancer metabolism. MS is typically coupled with separation techniques such as liquid chromatography (LC) or capillary electrophoresis to reduce the spectral complexity. MS based metabolomics studies generate a large amount of data and thus the use of machine learning (ML) methods are becoming increasingly popular to interpret and visualize metabolomics data and uncover new biological insights into disease biology.This thesis work focuses on MS based metabolomics analysis for improved understanding of cancer metabolism. As diagnosis of ovarian cancer (OC) remains an unmet clinical challenge, the main focus of this thesis is on uncovering the metabolic profile of OC and identifying potential biomarkers for its early diagnosis. In addition, ML methods were applied to identify the urinary metabolic profile of renal cell carcinoma (RCC). Kidney cancer is one the most lethal urinary cancers out of which 90% are renal cell carcinomas (RCC). Diagnosis of RCC is typically performed with expensive imagining tests and biopsies which not only are invasive but also are prone to sampling errors. Due to the proximity of the tumor to the urine, urine metabolomic profiling provides an excellent opportunity to study the metabolic rewiring of RCC.Chapter 1introduces OC and the application of metabolomics to identify the metabolic reprogramming associated with OC pathogenesis. An overview of the analytical platforms and techniques in metabolomics is given, and the general workflow, including the use of ML in metabolomics data handling, is outlined. The commonly used statistical approaches and ML assisted metabolomics analysis for cancer research are provided. Furthermore, an overview of various MS based metabolomics studies of OC is given, describing the metabolic phenotype of OC. The diagnostic potential of the metabolite panels, as identified by the described studies, is also given. Finally, the biological implications of the potentially important metabolic alterations in OC are described.Chapter 2presents a longitudinal serum metabolomics profiling of a triple-mutant (TKO) mouse model of high-grade serous carcinoma (HGSC), a subtype of OC. Two complementary ultrahigh performance liquid chromatography (UHPLC) - MS techniques were used to profile both the serum lipidome and the polar metabolome of TKO and TKO control mice. Sequentially collected serum samples from TKO mice starting from 8 weeks of age until death were analyzed, and a comprehensive metabolic map associated HGSC onset, development, and progression was revealed. These UHPLC-MS experiments were complemented with spatial lipidomic profiling of the entire reproductive system of the TKO mice.
- 일반주제명
- Ovarian cancer
- 일반주제명
- Lipids
- 일반주제명
- Metabolites
- 일반주제명
- Support vector machines
- 일반주제명
- Computer science
- 일반주제명
- Oncology
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aSah, Samyukta.
■24510▼aMetabolomics and Machine Learning for Early-Stage Cancer Diagnosis
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a316 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Fernandez, Facundo M.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aAmongst all omics sciences, metabolomics is the most recent and is rapidly advancing as one of the predominant methodologies for early disease diagnosis and precision medicine. Metabolomics involves the high-throughput analysis of low molecular weight metabolites and their interactions with biological networks. Metabolomics studies involve measuring the abundance of hundreds to thousands of metabolites in biological fluids, tissues, and cells, and provide instantaneous snapshot of the status of the biological system. For cancer research, metabolomics is a powerful platform as it enables the identification of metabolic alterations useful in diagnosis, prognosis, and therapeutics. However, because of the complexity of the metabolome, no single analytical platform can capture the complete metabolic profile of a biological system. Thus, the use of complementary techniques allows for a more comprehensive analysis. Mass spectrometry (MS) is one of the commonly used techniques in metabolomics. Due to its high sensitivity and high-resolution, MS based metabolomics studies can provide a wide breath of knowledge of cancer metabolism. MS is typically coupled with separation techniques such as liquid chromatography (LC) or capillary electrophoresis to reduce the spectral complexity. MS based metabolomics studies generate a large amount of data and thus the use of machine learning (ML) methods are becoming increasingly popular to interpret and visualize metabolomics data and uncover new biological insights into disease biology.This thesis work focuses on MS based metabolomics analysis for improved understanding of cancer metabolism. As diagnosis of ovarian cancer (OC) remains an unmet clinical challenge, the main focus of this thesis is on uncovering the metabolic profile of OC and identifying potential biomarkers for its early diagnosis. In addition, ML methods were applied to identify the urinary metabolic profile of renal cell carcinoma (RCC). Kidney cancer is one the most lethal urinary cancers out of which 90% are renal cell carcinomas (RCC). Diagnosis of RCC is typically performed with expensive imagining tests and biopsies which not only are invasive but also are prone to sampling errors. Due to the proximity of the tumor to the urine, urine metabolomic profiling provides an excellent opportunity to study the metabolic rewiring of RCC.Chapter 1introduces OC and the application of metabolomics to identify the metabolic reprogramming associated with OC pathogenesis. An overview of the analytical platforms and techniques in metabolomics is given, and the general workflow, including the use of ML in metabolomics data handling, is outlined. The commonly used statistical approaches and ML assisted metabolomics analysis for cancer research are provided. Furthermore, an overview of various MS based metabolomics studies of OC is given, describing the metabolic phenotype of OC. The diagnostic potential of the metabolite panels, as identified by the described studies, is also given. Finally, the biological implications of the potentially important metabolic alterations in OC are described.Chapter 2presents a longitudinal serum metabolomics profiling of a triple-mutant (TKO) mouse model of high-grade serous carcinoma (HGSC), a subtype of OC. Two complementary ultrahigh performance liquid chromatography (UHPLC) - MS techniques were used to profile both the serum lipidome and the polar metabolome of TKO and TKO control mice. Sequentially collected serum samples from TKO mice starting from 8 weeks of age until death were analyzed, and a comprehensive metabolic map associated HGSC onset, development, and progression was revealed. These UHPLC-MS experiments were complemented with spatial lipidomic profiling of the entire reproductive system of the TKO mice.
■590 ▼aSchool code: 0078.
■650 4▼aOvarian cancer
■650 4▼aLipids
■650 4▼aMetabolites
■650 4▼aSupport vector machines
■650 4▼aComputer science
■650 4▼aOncology
■690 ▼a0800
■690 ▼a0984
■690 ▼a0992
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365953▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
