ArticleInternational journal of molecular sciences2022
Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 8 citations in OpenAlex.
- A comprehensive evaluation of long-read de novo transcriptome assembly.Genome biology · 2026Article
- Integrative transcriptomic analysis of Korean high-grade serous ovarian cancer.PLoS genetics · 2025Article
- Identification of Ovarian High-Grade Serous Carcinoma with Mitochondrial Gene Variation.International journal of molecular sciences · 2025Article
- Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation.Cancers · 2023Article
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Authors and funding
8 authors at 4 institutions in 1 country.
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Abstract
The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue.
methodsIn this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform.
resultsThe prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances.
conclusionsGenomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.
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