Cancer continues to be one of the most significant public health issues worldwide. Thanks to early detection, the mortality rate of certain types of cancer has decreased significantly. However, the lack of accessible, low-cost, non-invasive, non-toxic, and easy-to-implement diagnostic methods results in late diagnoses, leading to 1 in every 6 people dying from cancer today. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and are found both inside cells and in bodily fluids such as blood and urine. Several studies have demonstrated their role in human diseases, especially cancer, where tumors release miRNAs into the bloodstream that can be used as biomarkers for early detection. The research and development team at Oncoliq SAS identified a panel of miRNAs associated with breast cancer, with a detection performance of 82% sensitivity and 83% specificity, validated in exploratory and preliminary confirmatory studies. These results led to the filing of a patent based on liquid biopsy technology. The objective of this protocol is to establish a panel of miRNAs for the early detection of breast cancer using samples from breast cancer patients and a cancer-free control group. Validation of the miRNAs will be carried out through RT-qPCR. This stage constitutes the analytical validation of the biomarkers, with the goal of developing an algorithm capable of classifying individuals with and without breast cancer. In future stages, a pilot clinical trial is planned to evaluate test implementation and clinical validation.
Study Type
OBSERVATIONAL
Enrollment
300
Collection of a blood sample for plasma isolation and miRNA analysis.
DIM Centros de Salud
Buenos Aires, Buenos Aires, Argentina
RECRUITINGEnrollment of 300 Participants
Successful recruitment and enrollment of a total of 300 eligible participants across both study groups within the planned recruitment period.
Time frame: October to December 2025
Development of a Machine Learning Classification Algorithm
Creation and validation of a machine learning model capable of distinguishing between individuals with breast cancer and cancer-free controls based on blood-derived biomarkers.
Time frame: December 2025 to March 2026
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