Biomarkers for ovarian cancer in multiple settings; screening, early diagnosis, triaging and relapse detection. Data-driven multi-omics (DNA, RNA, proteomics, microbiome) approaches are combined with ML/AI to find predictive markers for clinical use in home/self-collected samples (DBS, dried cervico-vaginal fluid). Technical studies on possibilities and limitations with e.g. DBS compared to wet samples. Research is translational with close collaborations with national network of clinicians.
Stefan Enroth
Uppsala University
Key Publications
Toward ovarian cancer screening with protein biomarkers using dried, self-sampled cervico-vaginal fluid
iScience, 2024
Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets
Nature Immunology, 2023
Forensic prediction of sex, age, height, body mass index, hip-to-waist ratio, smoking status and lipid lowering drugs using epigenetic markers and plasma proteins
Forensic Science International: Genetics, 2023
Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
Communications Medicine, 2022
Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention.
Nature genetics, 2022