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Identifying opportunities for improving epithelial ovarian cancer survival using novel approaches for exploring the role of ovulation and hormone-related conditions

Fu, Zhuxuan (2022) Identifying opportunities for improving epithelial ovarian cancer survival using novel approaches for exploring the role of ovulation and hormone-related conditions. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Ovarian cancer is the most lethal gynecologic cancer. Epithelial ovarian cancer (EOC) accounts for more than 90% of ovarian cancers. Although the etiology of EOC remains unknown, the established protective effects of parity and oral contraceptive use suggest that ovulation plays a role. However, ovulation alone cannot explain the magnitude of the protective effects from these exposures. Hormones, including androgen, estrogen, and progesterone, may play a role in ovarian carcinogenesis and affect survival outcomes via hormone receptors. The overall objective of this dissertation is to assess the role of lifetime ovulatory years (LOYs) in EOC development and identify risk factors related to the survival of patients with ovarian tumors defined by hormone receptor status.
First, we evaluated the association of LOYs, calculated by 15 different algorithms, with EOC risk. We further evaluated the individual components in LOYs with EOC risk overall and by histotype. Our findings show the heterogeneity of the histotype-specific associations with LOYs and with the individual components of LOYs, suggesting that carcinogenesis mechanisms may differ by the individual components in LOYs and by histotype. Second, we demonstrated that EOC patients with tumor types defined by hormone receptor status and stratified by histotype have varying risk and prognostic profiles. These data suggest potential biological mechanisms underlying the association of hormonally-linked risk factors and EOC risk. Furthermore, outcomes need to be studied by histotype and by tumor hormone receptor status. Third, we built a prediction model for EOC survival using machine learning techniques and conducted feature identification using nine immunohistochemistry biomarkers and clinical variables. Our prediction model indicates that CD8+ tumor-infiltrating lymphocytes, androgen receptor, progesterone receptor and p16 play critical roles in predicting EOC survival.
The implication of these findings allows us to better understand the role of LOYs and hormone receptors in ovarian cancer carcinogenesis and survival. Furthermore, the results provide a foundation for targeting risk factors related to survival and hormone receptors by histotype in treatment and provide potential opportunities to extend survival in EOC patients.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Fu, Zhuxuanzhf20@pitt.eduzhf200000-0002-9190-195X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairModugno,
Committee MemberBrooks, Maria Morimbrooks@pitt.edumbrooks
Committee MemberTang, Lulutang@pitt.edulutang
Committee MemberTaylor, Sarah
Committee MemberSonger, Thomas Jtjs@pitt.edutjs
Date: 4 January 2022
Date Type: Publication
Defense Date: 30 September 2021
Approval Date: 4 January 2022
Submission Date: 30 November 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 237
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Epidemiology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: ovarian cancer, risk factors, survival, machine learning, ovulation, hormone
Date Deposited: 04 Jan 2022 15:28
Last Modified: 04 Jan 2022 15:28


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