Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis

Abstract

Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients. The integration of multi-omics data has increased our knowledge of the disease and identified valuable OC biomarkers. In this review, we summarize the recent advances and perspectives in the use of multi-omics technologies in OC research and highlight potential applications of multi-omics for identifying novel biomarkers and improving clinical assessments.

Keywords: Biomarker; Multi-omics; Ovarian cancer; Translational medicine.

Figure 1

Opportunities for reducing ovarian cancer mortality through early detection. Many research studies and clinical trials have been conducted to develop sensitive screening tests that could allow for earlier detection of OC in women who are asymptomatic. Recommendations for OC screening need to be dependent on the risk level of the population. Women with genetic mutations known to increase susceptibility to OC or a strong family history of the disease may be at increased risk of developing OC. For women at average risk, there are no recommended screening tests for them to date. For women who have a high risk of developing OC, screening tests may be offered to help this population get timely prevention and treatment. NR = not recommended. TBD = to be determined.

Figure 2

Schematic representation of multi-omics approaches towards biomarker discovery for early diagnosis in ovarian cancer. Tissue and body fluids, such as blood, ascites, uterine lavage, cervical smear, and urine, can be analyzed by multi-platform omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, etc. These multidimensional data could be integrated based on machine learning techniques. The multi-omics approach promotes a comprehensive understanding of OC and biomarker discovery in early diagnosis.