Abstract
Rare ovarian cancers are ovarian cancers with an annual incidence of less than 6 cases per 100,000 women. They generally have a poor prognosis due to being delayed diagnosis and treatment. Exploration of molecular mechanisms in these cancers has been challenging due to their rarity and research efforts being fragmented across the world. Omics approaches can provide detailed molecular snapshots of the underlying mechanisms of these cancers. Omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, can identify potential candidate biomarkers for diagnosis, prognosis, and screening of rare gynecological cancers and can aid in identifying therapeutic targets. The integration of multiple omics techniques using approaches such as proteogenomics can provide a detailed understanding of the molecular mechanisms of carcinogenesis and cancer progression. Further, omics approaches can provide clues towards developing immunotherapies, cancer recurrence, and drug resistance in tumors; and form a platform for personalized medicine. The current review focuses on the application of omics approaches and integrative biology to gain a better understanding of rare ovarian cancers.
Highlights
Ovarian cancer is one of the most lethal gynecological malignancies [1]
Most patients with low-grade serous carcinoma present at an early stage, they do occasionally present at an advanced stage, and in this case, the initial treatment may lead to a complete response, relapse is common with poor response to chemotherapy [13]
While several omics approaches currently exist for delineating molecular mechanisms of biological processes, the current review focuses mainly on genomics, transcriptomics, proteomics, and metabolomics approaches employed to investigate rare ovarian cancers
Summary
Ovarian cancer is one of the most lethal gynecological malignancies [1]. The contributing factors to its poor prognosis include the fact that its initial symptoms are not always obvious, making it difficult for early detection. Rare ovarian cancers overall have the same problem of late diagnosis and are more difficult to treat They can be misdiagnosed due to their rarity and consequent clinical inexperience [3,5]. Multi-omics data analysis, based on machine learning techniques, provides a greater understanding of predictive and prognostic phenotypes, improves the clustering of samples into specific biologically meaningful groups, analyzes the cellular responses to therapy, and contributes to translational research using integrative models [9,10]. This review article describes fundamental principles, challenges, advances, and clinical applications of different “omics” technologies, including genomics, transcriptomics, proteomics, and metabolomics, highlighting the significance of integrating multi-omics data in rare ovarian cancer research and evaluating clinically relevant outcomes
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