Patient-specific alterations in blood plasma cfRNA profiles enable accurate classification of cancer patients and controls

Annelien Morlion; Philippe Decruyenaere; Kathleen Schoofs; Jasper Anckaert; Nickolas Johns Ramirez; Justine Nuytens; Eveline Vanden Eynde; Kimberly Verniers; Celine Everaert; Guy Brusselle; Steven Callens; Filomeen Haerynck; Dimitri Hemelsoet; Eric Hoste; Jo Lambert; Nicolaas Lumen; Fritz Offner; Koen Paemeleire; Vanessa Smith; Lies Van den Eynde; Jo Van Dorpe; Amber Vanhaecke; Hans Van Vlierberghe; An Mariman; Olivier Thas; Jo Vandesompele; Pieter Mestdagh

doi:10.1038/s43856-026-01507-8

Patient-specific alterations in blood plasma cfRNA profiles enable accurate classification of cancer patients and controls

Commun Med (Lond). 2026 Mar 7;6(1):230. doi: 10.1038/s43856-026-01507-8.

Authors

Annelien Morlion^{1

2}, Philippe Decruyenaere^{1

2

3}, Kathleen Schoofs^{1

2

4}, Jasper Anckaert^{1

2}, Nickolas Johns Ramirez^{1

2}, Justine Nuytens^{1

2}, Eveline Vanden Eynde^{1

2}, Kimberly Verniers^{1

2}, Celine Everaert⁴, Guy Brusselle⁵, Steven Callens⁶, Filomeen Haerynck^{7

8}, Dimitri Hemelsoet⁹, Eric Hoste¹⁰, Jo Lambert¹¹, Nicolaas Lumen¹², Fritz Offner³, Koen Paemeleire⁹, Vanessa Smith^{13

14}, Lies Van den Eynde¹², Jo Van Dorpe¹⁵, Amber Vanhaecke¹⁵, Hans Van Vlierberghe^{7

16

17}, An Mariman^{18

19}, Olivier Thas^{20

21

22}, Jo Vandesompele^#^{1

2}, Pieter Mestdagh^#^{23

24}

Affiliations

¹ Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
² OncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
³ Department of Hematology, Ghent University Hospital, Ghent, Belgium.
⁴ TOBI Lab, Center for Medical Biotechnology, VIB-UGent, Zwijnaarde, Belgium.
⁵ Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
⁶ Department of General Internal Medicine, Ghent University Hospital, Ghent, Belgium.
⁷ Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.
⁸ Department of Pediatric Respiratory and Infectious Medicine, Ghent University Hospital, Ghent, Belgium.
⁹ Department of Neurology, Ghent University Hospital, Ghent, Belgium.
¹⁰ Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium.
¹¹ Department of Dermatology, Ghent University Hospital, Ghent, Belgium.
¹² Department of Urology, Ghent University Hospital, Ghent, Belgium.
¹³ Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.
¹⁴ Unit for Molecular Immunology and Inflammation, Inflammation Research Center (IRC), VIB-UGent, Zwijnaarde, Belgium.
¹⁵ Department of Pathology, Ghent University Hospital, Ghent, Belgium.
¹⁶ Hepatology Research Unit, Liver Research Center, Ghent, Belgium.
¹⁷ Department of Gastroenterology and Hepatology, Ghent University Hospital, Ghent, Belgium.
¹⁸ Department of Physical Medicine and Rehabilitation, Center for Integrative Medicine, Ghent University Hospital, Ghent, Belgium.
¹⁹ Department of Head and Skin, Ghent University, Ghent, Belgium.
²⁰ Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
²¹ Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
²² National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Wollongong, NSW, Australia.
²³ Department of Biomolecular Medicine, Ghent University, Ghent, Belgium. pieter.mestdagh@ugent.be.
²⁴ OncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium. pieter.mestdagh@ugent.be.

^# Contributed equally.

Abstract

Background: Circulating nucleic acids in blood plasma form an attractive, minimally invasive resource to study human health and disease. In this study, we aimed to identify cell-free RNA alterations that can distinguish cancer patients from cancer-free individuals.

Methods: We first performed mRNA capture sequencing on 266 blood plasma samples from cancer patients and controls, including a discovery set of 208 donors across 25 cancer types and a replication set of 58 donors across three cancer types. We first conducted group-level comparisons and then compared individual patient profiles to a reference control population in a one-versus-many approach. This approach was further evaluated in independent cohorts: a prostate cancer plasma cohort (n = 180), a non-malignant disease plasma cohort (n = 125), a lymphoma plasma cohort (n = 65), and a bladder cancer urine cohort (n = 24), each including both patients and controls.

Results: Here we show that cancer patients exhibit both cancer type-specific and general cell-free RNA alterations. However, differentially abundant RNAs vary widely among patients and across cohorts, hampering robust biomarker identification. By comparing individual patient profiles to control populations, we identify so-called biomarker tail genes, which strongly deviate from controls. The number of these genes per sample distinguishes cancer patients from control samples. Independent cohorts also confirm the potential of this approach.

Conclusions: Our findings demonstrate substantial heterogeneity in cell-free RNA alterations among cancer patients and propose that patient-specific changes can be exploited for classification.

Plain language summary

RNAs that circulate in biofluids can provide information about a person’s health. In this study, we examined such extracellular RNAs in blood plasma from people with and without cancer to see if specific patterns could help identify cancer. We analyzed over 600 samples from multiple cancer types and compared each patient’s RNA patterns to those of healthy individuals. We found that each patient shows unique changes, and that the number of strongly altered RNAs can distinguish cancer patients from healthy people. These findings suggest that studying individual RNA patterns could improve cancer detection and support personalized care.