Fragmentomics features of ovarian cancer.
Xiaopei ChaoZhentian KaiHuanwen WuJing WangXiaojing ChenHaiqi SuXiao ShangRuijue LinLisha HuangHongsheng HeJinghe LangLei LiPublished in: International journal of cancer (2024)
Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.
Keyphrases
- circulating tumor
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- papillary thyroid
- prognostic factors
- peritoneal dialysis
- circulating tumor cells
- metabolic syndrome
- big data
- type diabetes
- squamous cell carcinoma
- young adults
- deep learning
- single molecule
- risk factors
- insulin resistance
- squamous cell
- cardiovascular events
- depressive symptoms
- adipose tissue