The impact of leukocyte-associated RNA retention on platelet RNA-based cancer detection.
20 affiliations
- 1Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology of the University of Gdansk and the Medical University of Gdansk, Poland.
- 2Centre of Biostatistics and Bioinformatics, Medical University of Gdansk, Poland.
- 3Department of Software Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland.
- 4Department of Algorithms and Systems Modelling, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland.
- 5Laboratory of Single Cell Analyses, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland.
- 6Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland.
- 7Department of Quality in Healthcare, Medical University of Gdansk, Poland.
- 8Board of Directors, University Clinical Center, Medical University of Gdansk, Poland.
- 9Department of Oncologic, General and Transplant Surgery, University Clinical Centre, Medical University of Gdansk, Gdansk, Poland.
- 10Department of Obstetrics and Gynecology, Gynecological Oncology and Endocrinological Gynecology, University Clinical Center, Gdansk, Poland.
- 11Department of Oncology and Radiotherapy, Faculty of Medicine, Medical University of Gdansk, Gdansk, Poland.
- 12Molecular Medicine Program, University of Utah, Salt Lake City, UT, USA.
- 13George E. Wahlen Veterans Affairs Medical Center, Department of Internal Medicine and the Geriatric Research Education and Clinical Center (GRECC), Salt Lake City, UT, USA.
- 14Department of Pathology, University of Utah, Salt Lake City, UT, USA.
- 15Division of General Internal Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
- 16Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- 17Cancer Center Amsterdam and Brain Tumor Center Amsterdam, Amsterdam, the Netherlands.
- 18Cancer Center Amsterdam, Amsterdam UMC, The Netherlands.
- 19Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, University of Amsterdam, Amsterdam, The Netherlands.
- 20Translational AI in Laboratory Medicine, Department of Laboratory Medicine, Amsterdam UMC, Amsterdam, The Netherlands.
Abstract
Platelet RNA profiling is a promising liquid biopsy approach, offering minimally invasive insights into systemic diseases, such as cancer. Yet, the effect of leukocyte-associated RNA retention on platelet transcriptomic signatures remains unclear. This study provides the first comprehensive evaluation of how leukocyte-associated RNA retention influences platelet RNA sequencing and machine learning (ML)-based cancer detection. Using the largest publicly available platelet transcriptomic dataset (2351 human RNA samples) and single-cell/single-platelet RNA sequencing from 29 donors, we quantified leukocyte-associated RNA retention in matched sample groups (low vs. high retention) via a novel 132-gene leukocyte marker panel. Diagnostic implications were assessed using 10 ML algorithms for binary classification (cancer vs. healthy), examining the contribution of leukocyte-derived transcripts. Interestingly, models trained on high-retention samples often showed slightly improved performance, suggesting complementary diagnostic value from leukocyte signals. Importantly, post hoc correction using remove unwanted variation (RUV) and linear modeling effectively mitigated retention effects. However, adding the leukocyte-associated RNA retention score to the full-cohort classifier changed cancer-detection AUC only minimally (0.9247 to 0.9249). Thus, whether this signal should be corrected, recalibrated, or reported should depend on the study question rather than assuming routine removal.
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BibTeX
@article{sieczczynski2026impact,
title = {{The impact of leukocyte-associated RNA retention on platelet RNA-based cancer detection.}},
author = {Michał Sieczczyński and Krzysztof Pastuszak and Anna Samelak-Czajka and Zuzanna Cemka and Paulina Jackowiak and Tomasz Stefaniak and Sylwia Łapińska-Szumczyk and Jacek Jassem and Anna J Żaczek and Matthew T Rondina and Myron G Best and Sjors G J G In 't Veld and Anna Supernat},
journal = {Molecular oncology},
year = {2026},
month = oct,
doi = {10.1002/1878-0261.70353},
url = {https://europepmc.org/article/MED/42963897},
note = {CC BY},
}
RIS
TY - JOUR
TI - The impact of leukocyte-associated RNA retention on platelet RNA-based cancer detection.
AU - Michał Sieczczyński
AU - Krzysztof Pastuszak
AU - Anna Samelak-Czajka
AU - Zuzanna Cemka
AU - Paulina Jackowiak
AU - Tomasz Stefaniak
AU - Sylwia Łapińska-Szumczyk
AU - Jacek Jassem
AU - Anna J Żaczek
AU - Matthew T Rondina
AU - Myron G Best
AU - Sjors G J G In 't Veld
AU - Anna Supernat
PY - 2026
DA - 2026/10/30
JO - Molecular oncology
DO - 10.1002/1878-0261.70353
UR - https://europepmc.org/article/MED/42963897
AB - Platelet RNA profiling is a promising liquid biopsy approach, offering minimally invasive insights into systemic diseases, such as cancer. Yet, the effect of leukocyte-associated RNA retention on platelet transcriptomic signatures remains unclear. This study provides the first comprehensive evaluation of how leukocyte-associated RNA retention influences platelet RNA sequencing and machine learning (ML)-based cancer detection. Using the largest publicly available platelet transcriptomic dataset (2351 human RNA samples) and single-cell/single-platelet RNA sequencing from 29 donors, we quantified leukocyte-associated RNA retention in matched sample groups (low vs. high retention) via a novel 132-gene leukocyte marker panel. Diagnostic implications were assessed using 10 ML algorithms for binary classification (cancer vs. healthy), examining the contribution of leukocyte-derived transcripts. Interestingly, models trained on high-retention samples often showed slightly improved performance, suggesting complementary diagnostic value from leukocyte signals. Importantly, post hoc correction using remove unwanted variation (RUV) and linear modeling effectively mitigated retention effects. However, adding the leukocyte-associated RNA retention score to the full-cohort classifier changed cancer-detection AUC only minimally (0.9247 to 0.9249). Thus, whether this signal should be corrected, recalibrated, or reported should depend on the study question rather than assuming routine removal.
SN - 1574-7891
SN - 1878-0261
ER -