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Background <p>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.</p> Methods <p>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 (<i>n</i> = 180), a non-malignant disease plasma cohort (<i>n</i> = 125), a lymphoma plasma cohort (<i>n</i> = 65), and a bladder cancer urine cohort (<i>n</i> = 24), each including both patients and controls.</p> Results <p>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.</p> Conclusions <p>Our findings demonstrate substantial heterogeneity in cell-free RNA alterations among cancer patients and propose that patient-specific changes can be exploited for classification.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • 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

摘要

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.