Abstract 5396: Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian cancer

Abstract

Abstract Introduction: Ovarian cancer (OC) is the second most common gynecologic cancer in the U.S, and is the deadliest cancer of the female reproductive system. Because of the complex nature of OC, diagnoses are often by display of symptoms at late stages of disease. Identifying OC at early stages can increase the 5-year survival rate from 40% in stage IV to 94% in stage I. Therefore, effective screening and detection of OC at early stages is imperative. The main objective of this study is to develop a diagnostic panel of markers that correlate with early-stage OC, and ultimately, improve the survival rate of patients. Background: Approaches in the past several years to detect malignant OC have focused on processing relevant images or screening genomic data. These studies use liquid-based biopsies, sonographic images, cyst images, CT images, MR imaging and genomics data. Some studies have focused on distinguishing malignant tumors versus benign tumor cells. In addition to statistical methods to examine available genomic datasets, Machine Learning models including K-Nearest Neighbor, K-means, Convolutional Neural Networks, Deep Learning, Support Vector Machine, Random Forest, and Fuzzy algorithms have been used. Methods: In this study we proposed a method to determine a possible correlation between some actively expressed genes and OC, using publicly available mRNA data sets. The samples were retrieved from the GSE106817 dataset via the NCBI GEO site, with 333 cancerous and 2759 Non-cancerous samples. The objective is to define a set of genes which is able to illustrate prediction of correlation through the use of PCA, NMF and SVD. We further verified our findings by testing on one additional different mRNA datasets. For this, we compared several machine learning algorithms to potentially correlate active genes and OC stages, from the available data. Results and Discussions: Through our study we have developed Machine Learning models valuable to identify patterns of disease at different stages, thus, potentially be useful in developing predictive models. Our initial experimental results have been demonstrated with a set of mRNA signatures, which may be used to create a panel of biomarkers valuable for detecting early-stage OC. Our results confirm the correlation between OC and PAX8, PEG3, and BIRC5, and MYB and RAC2. Continuation of this study highlights methods towards the early detection of OC and to improve disease identification outcome. Citation Format: Zahra Saghaie, Christine Richardson, M. Taghi Mostafavi. Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5396.