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.
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Data availability
Raw RNA-sequencing data are available under controlled access in the European Genome-phenome Archive (EGA) due to the presence of potentially identifiable genetic information and in accordance with participant informed consent and the European General Data Protection Regulation (GDPR). The pan-cancer and three-cancer cohort FASTQs can be found under study EGAS00001006755 (dataset EGAD00001009713). The prostate cancer cohort FASTQs can be found under study EGAS50000001265 (datasets EGAD50000001805). The non-malignant cohort FASTQs can be found under study and EGAS50000001264 (dataset EGAD50000001806). The lymphoma cohort FASTQs can be found under study EGAS00001007127 (dataset EGAD00001010259). The bladder cancer cohort FASTQs can be found under study EGAS00001003917 (dataset EGAD00001005439). Access to these controlled datasets is granted by the relevant Data Access Committee (DAC) following submission and approval of a data access request through the EGA portal. Researchers must provide a research proposal and agree to the terms of a Data Access Agreement restricting use to approved research purposes and prohibiting re-identification attempts. Requests are typically reviewed within 4–8 weeks. Differential abundance and gene set enrichment analysis results are available (open access) in a Zenodo repository (https://doi.org/10.5281/zenodo.7953707)40. An overview of tail genes per type and 50% consensus biomarker tail genes can be found in Supplementary Data 6. Source data for Figs. 1–7 can be found in Supplementary Data 7.
Code availability
Software versions used for data analysis were: bedtools v2.30.0; cutadapt v1.18; FASTQC v0.11.9; FusionCatcher v1.30; HTSeq v0.11.0; samtools v1.16.1; STAR v2.7.3; UMI-tools v1.0.0; and R v4.2.1. The following R packages were used for downstream analyses: biomarRt v2.62.0; caret v6.0.94; data.table v1.16.4; DESeq2 v1.36.0; EnhancedVolcano v1.24.0; eulerr v6.1.1; fgsea v1.22.0; GeneOverlap v1.32.0; ggpubr v0.6.0; gridExtra v2.3; here v1.0.1; pheatmap v1.0.12; pROC v1.18.0; rstatix v0.7.2; tidyverse v1.3.2. Custom R scripts used for differential abundance analysis, tail gene identification, and downstream statistical analyses are available at https://github.com/OncoRNALab/tailgenes and have been archived on Zenodo (v1.0.0) with https://doi.org/10.5281/zenodo.1866919941. The archived version corresponds to the code used for this study.
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Acknowledgements
This work was supported by Ghent University (BOF; BOF19/DOC/228), Ghent University Industrial Research Fund (IOF; F2023/IOF-ConcepTT/068), Ghent University Hospital, Fund for Scientific Research Flanders (FWO; 11C1623N predoctoral fellowship to A.M., 11H7523N predoctoral fellowship to P.D., G0B2820N), the Belgian Foundation Against Cancer (STK; 2025-085 postdoctoral fellowship to A.M.), and Kom Op Tegen Kanker (Stand up to Cancer, the Flemish cancer society). The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO, and the Flemish Government—department EWI. We would like to thank Tim Mercer for providing the Sequin spikes, Eva Hulstaert and Francisco Avila Cobos for their support during the first phase of the project, Anneleen Decock for her feedback on the initial draft, and Scott Ailliet, Sofie Roelandt, and Deyna Keppens for their assistance with blood collection. We would also like to thank the Biostatistics Unit of the Faculty of Medicine and Health Sciences (Ghent University) for their feedback on the tail gene performance assessment.
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Conceptualization: A.Mo., C.E., J.V., and P.M.; Data curation: A.mo.; Formal analysis: A.Mo. and O.T.; Funding acquisition: A.Mo., J.V., J.V.D., and P.M.; Investigation: E.V., J.N., K.V., and N.J.R.; Methodology: A.Mo., J.V., and P.M.; Project administration: A.Mo., J.V., and P.M.; Resources: A.Ma., A.V., D.H., E.H., F.H., F.O., G.B., H.V., J.L., J.V., J.V.D., K.P., K.S., L.V., N.L., P.D., P.M., S.C., and V.S.; Software: A.Mo. and J.A.; Supervision: J.V. and P.M.; Validation: A.Mo. and O.T.; Visualization: A.mo.; Writing—original draft: A.Mo., J.V., and P.M.; Writing—review & editing: all authors.
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A.M., J.V., and P.M. are inventors on the international PCT application “Method to screen, diagnose or monitor treatment using increased numbers of deviating analytes” (WO2024200811; published 03/10/2024) covering the tail gene concept. They are also inventors on the European priority application “Blood plasma RNA profiles enable accurate diagnosis of prostate cancer” (EP26151329.5; filed 12/01/2026) covering specific prostate cancer biomarker tail genes from the prostate cancer cohort in this manuscript. Both applications are assigned to Ghent University.
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Morlion, A., Decruyenaere, P., Schoofs, K. et al. Patient-specific alterations in blood plasma cfRNA profiles enable accurate classification of cancer patients and controls. Commun Med (2026). https://doi.org/10.1038/s43856-026-01507-8
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DOI: https://doi.org/10.1038/s43856-026-01507-8
