Researchers at the Carlos Simon Foundation have identified a molecular signature in blood that can differentiate uterine fibroids from leiomyosarcomas, a rare malignant tumor that remains difficult to identify before surgery.
The study, led by Dr. Aymara Mas and published in the American Journal of Obstetrics & Gynecology, analyzed preoperative blood samples from 102 patients recruited across 16 hospitals in Spain.
Using cell-free RNA circulating in plasma, the team developed MYOSARC, a 100-gene signature combined with machine learning that showed the ability to distinguish between the two types of uterine tumors.
Differentiating a benign fibroid from a leiomyosarcoma before surgery remains a clinical challenge. Having more information about the risk of malignancy could help clinicians select the most appropriate surgical approach for each patient.
These preliminary findings require prospective validation before this approach can be translated into a clinical test.
Read the study: Liquid biopsy cell-free RNA-based machine learning enables preoperative risk-stratification of uterine leiomyosarcoma.