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Background <p>The characterisation of circulating nucleic acid biomarkers in liquid biopsy based diagnostics holds great potential to transform the landscape of early cancer detection and screening. These tests are increasingly incorporating information beyond the primary sequence, to include epigenetic, fragmentomic, and other chemical properties to boost performance. Chemical modifications of RNA offer a rich, and currently underutilised source of biomarker signal. We aimed to develop a single-molecule method to profile 2′-O-methylation of a ribosomal RNA fragment previously linked to lung cancer, and to evaluate its diagnostic value in blood.</p> Methods <p>We have designed a targeted capture strategy that ligates structure-guided adapters to a ~22-nucleotide ribosomal RNA fragment to enable sequencing of native molecules on a nanopore platform. Raw ionic-current signals were used to train and validate machine-learning classifiers to detect methylation states of synthetic oligonucleotides and cell culture derived ribosomal RNA fragments. Finally, we applied these methods to liquid biopsy samples collected from a 43-patient cohort of individuals undergoing investigation for suspected lung cancer.</p> Results <p>Here we show that single molecules of the target fragment are sequenced, and their methylation states can be accurately (92%) and quantitatively (Pearson r = 0.997) measured. In clinical liquid biopsy samples, it reveals a differential pattern of methylation in lung cancer that yields a diagnostic classifier with an area under the receiver-operating characteristic curve of 0.84.</p> Conclusions <p>This approach enables direct, single-molecule methylation profiling of small RNAs in blood and identifies a lung cancer–associated methylation pattern with diagnostic potential. It is readily compatible with multi-modal liquid biopsy assays to enhance performance.</p>

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Nanopore based RNA methylation profiling of a circulating lung cancer biomarker

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Marta Sanchez-Delgado,
  • Maurice Frank,
  • Tomáš Šišmiš,
  • Mustafa Kahraman,
  • Alberto Daniel-Moreno,
  • Emmika Mummery,
  • Jessika Ceiler,
  • Jasmin Skottke,
  • Carla Bieg-Salazar,
  • Franziska Hinkfoth,
  • Christina Rudolf,
  • Ronja Weiblen,
  • Kaja Tikk,
  • Tobias Sikosek,
  • Bruno R. Steinkraus,
  • Rastislav Horos,
  • Michal Urda,
  • Timothy Rajakumar

摘要

Background

The characterisation of circulating nucleic acid biomarkers in liquid biopsy based diagnostics holds great potential to transform the landscape of early cancer detection and screening. These tests are increasingly incorporating information beyond the primary sequence, to include epigenetic, fragmentomic, and other chemical properties to boost performance. Chemical modifications of RNA offer a rich, and currently underutilised source of biomarker signal. We aimed to develop a single-molecule method to profile 2′-O-methylation of a ribosomal RNA fragment previously linked to lung cancer, and to evaluate its diagnostic value in blood.

Methods

We have designed a targeted capture strategy that ligates structure-guided adapters to a ~22-nucleotide ribosomal RNA fragment to enable sequencing of native molecules on a nanopore platform. Raw ionic-current signals were used to train and validate machine-learning classifiers to detect methylation states of synthetic oligonucleotides and cell culture derived ribosomal RNA fragments. Finally, we applied these methods to liquid biopsy samples collected from a 43-patient cohort of individuals undergoing investigation for suspected lung cancer.

Results

Here we show that single molecules of the target fragment are sequenced, and their methylation states can be accurately (92%) and quantitatively (Pearson r = 0.997) measured. In clinical liquid biopsy samples, it reveals a differential pattern of methylation in lung cancer that yields a diagnostic classifier with an area under the receiver-operating characteristic curve of 0.84.

Conclusions

This approach enables direct, single-molecule methylation profiling of small RNAs in blood and identifies a lung cancer–associated methylation pattern with diagnostic potential. It is readily compatible with multi-modal liquid biopsy assays to enhance performance.