Fragmentomics features of ovarian cancer.
Int J Cancer
; 155(7): 1316-1326, 2024 Oct 01.
Article
in En
| MEDLINE
| ID: mdl-38769763
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Ovarian Neoplasms
/
Biomarkers, Tumor
/
Machine Learning
/
Circulating Tumor DNA
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Middle aged
Language:
En
Journal:
Int J Cancer
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
United States