Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples.

ABSTRACT:

Background and objectives

Breast cancer is the most common cancer in women, with one in eight women suffering from this disease in her lifetime. The implementation of centrally organized mammography screening for women between 50 and 69 years of age was a major step in the direction of early detection. However, the participation rate reaches approximately 50% of the eligible women, one reason being the painful compression of the breast, cited as a major issue for not participating in this very important program. Therefore, focusing current research on less painful and less invasive techniques for the detection of breast cancer is highly clinically relevant. Liquid biopsies offer this option by detection of distinct molecules such as microRNAs (miRNAs) or circulating tumor DNA (ctDN

SUBMITTER: Maurer J 

PROVIDER: S-EPMC11645719 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature