Dynamic and static circulating cancer microRNA biomarkers - a validation study

• Published in: RNA biology Vol. 20; no. 1; pp. 1 - 9
• Main Authors: Abu-Halima, Masood, Keller, Andreas, Becker, Lea Simone, Fischer, Ulrike, Engel, Annika, Ludwig, Nicole, Kern, Fabian, Rounge, Trine B., Langseth, Hilde, Meese, Eckart, Keller, Verena
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 2023; Landes Bioscience; Taylor & Francis Group
• Subjects: biomarker; Biomarkers; Biomarkers, Tumor - genetics; breast cancer; cancer; Circulating MicroRNA; colon cancer; Early Detection of Cancer; Gene Expression Profiling; Humans; microRNA; MicroRNAs - genetics; miR-155; miR-99a; Neoplasms - diagnosis; Neoplasms - genetics; Research Paper; colon cancer; miR-155; miR-99a; breast cancer; microRNA; cancer; biomarker
• ISSN: 1547-6286; 1555-8584; 1555-8584
• DOI: 10.1080/15476286.2022.2154470

For cancers and other pathologies, early diagnosis remains the most promising path to survival. Profiling of longitudinal cohorts facilitates insights into trajectories of biomarkers. We measured microRNA expression in 240 serum samples from patients with colon, lung, and breast cancer and from cancer-free controls. Each patient provided at least two serum samples, one prior to diagnosis and one following diagnosis. The median time interval between the samples was 11.6 years. Using computational models, we evaluated the circulating profiles of 21 microRNAs. The analysis yielded two sets of biomarkers, static ones that show an absolute difference between certain cancer types and controls and dynamic ones where the level over time provided higher diagnostic information content. In the first group, miR-99a-5p stands out for all three cancer types. In the second group, miR-155-5p allows to predict lung cancers and colon cancers. Classification in samples from cancer and non-cancer patients using gradient boosted trees reached an average accuracy of 79.9%. The results suggest that individual change over time or an absolute value at one time point may predict a disease with high specificity and sensitivity.