Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures
Abstract Background Lung cancer, particularly non-small cell lung cancer (NSCLC), accounts for about 85% of all lung cancer cases and remains a major global health challenge. Traditional diagnostic methods, such as chest X-rays and low-dose CT scans, have limitations, including high false-positive r...
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Nature Portfolio
2025-08-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-01068-2 |
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| author | Zhuokun Feng Masaki Nasu Gehan Devendra Ayman A. Abdul-Ghani Owen T. M. Chan Jeffrey A. Borgia Zitong Gao Hanqiu Zhang Yu Chen Ting Gong Gang Luo Hua Yang Lang Wu Yuanyuan Fu Youping Deng |
| author_facet | Zhuokun Feng Masaki Nasu Gehan Devendra Ayman A. Abdul-Ghani Owen T. M. Chan Jeffrey A. Borgia Zitong Gao Hanqiu Zhang Yu Chen Ting Gong Gang Luo Hua Yang Lang Wu Yuanyuan Fu Youping Deng |
| author_sort | Zhuokun Feng |
| collection | DOAJ |
| description | Abstract Background Lung cancer, particularly non-small cell lung cancer (NSCLC), accounts for about 85% of all lung cancer cases and remains a major global health challenge. Traditional diagnostic methods, such as chest X-rays and low-dose CT scans, have limitations, including high false-positive rates, radiation risks, and the invasiveness of tissue biopsies. This study aims to develop a non-invasive liquid biopsy approach for early NSCLC diagnosis. Methods We developed a machine-learning model to analyze small RNA sequencing data from 1446 tissue samples to identify a diagnostic tRNA signature. This signature was independently validated using the in-house data of 233 plasma exosome samples. Diagnostic performance was assessed using Area Under the Curve (AUC) metrics. Signature tRNAs were then evaluated across various clinical and demographic variables, with further survival analysis and functional studies to explore the molecular role of the signature tRNAs. Results We identify a robust six-tRNA signature with strong diagnostic performance, achieving AUC values of 0.97 in discovery, 0.96 in hold-out validation, and 0.84 in independent validation. The signature effectively distinguishes cancerous from benign samples (AUC = 0.85) and consistently performs across clinical and demographic variables, with AUC values exceeding 0.80, particularly for early-stage lung cancer diagnosis. Additionally, three signature tRNAs demonstrate prognostic value for independent survival prediction. Functional studies suggest potential regulatory roles of specific tRNAs and their associated fragments in tumor metabolism pathways. Conclusions This research underscores the diagnostic power of tRNA signature for NSCLC liquid biopsy and provides epigenetic insights that enhance our understanding of oncogenic molecular pathophysiology. |
| format | Article |
| id | doaj-art-535c521e79a84e938f5152d33beccd9a |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-535c521e79a84e938f5152d33beccd9a2025-08-24T11:47:43ZengNature PortfolioCommunications Medicine2730-664X2025-08-015111710.1038/s43856-025-01068-2Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signaturesZhuokun Feng0Masaki Nasu1Gehan Devendra2Ayman A. Abdul-Ghani3Owen T. M. Chan4Jeffrey A. Borgia5Zitong Gao6Hanqiu Zhang7Yu Chen8Ting Gong9Gang Luo10Hua Yang11Lang Wu12Yuanyuan Fu13Youping Deng14Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaThe Queen’s Medical CenterPali Momi Medical CenterPathology Core Shared Resource, University of Hawaii Cancer CenterRUSH University Medical CenterDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaIllinois Institute of TechnologyDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaPacific Center for Genome Research, University of Hawaii Cancer CenterDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at ManoaAbstract Background Lung cancer, particularly non-small cell lung cancer (NSCLC), accounts for about 85% of all lung cancer cases and remains a major global health challenge. Traditional diagnostic methods, such as chest X-rays and low-dose CT scans, have limitations, including high false-positive rates, radiation risks, and the invasiveness of tissue biopsies. This study aims to develop a non-invasive liquid biopsy approach for early NSCLC diagnosis. Methods We developed a machine-learning model to analyze small RNA sequencing data from 1446 tissue samples to identify a diagnostic tRNA signature. This signature was independently validated using the in-house data of 233 plasma exosome samples. Diagnostic performance was assessed using Area Under the Curve (AUC) metrics. Signature tRNAs were then evaluated across various clinical and demographic variables, with further survival analysis and functional studies to explore the molecular role of the signature tRNAs. Results We identify a robust six-tRNA signature with strong diagnostic performance, achieving AUC values of 0.97 in discovery, 0.96 in hold-out validation, and 0.84 in independent validation. The signature effectively distinguishes cancerous from benign samples (AUC = 0.85) and consistently performs across clinical and demographic variables, with AUC values exceeding 0.80, particularly for early-stage lung cancer diagnosis. Additionally, three signature tRNAs demonstrate prognostic value for independent survival prediction. Functional studies suggest potential regulatory roles of specific tRNAs and their associated fragments in tumor metabolism pathways. Conclusions This research underscores the diagnostic power of tRNA signature for NSCLC liquid biopsy and provides epigenetic insights that enhance our understanding of oncogenic molecular pathophysiology.https://doi.org/10.1038/s43856-025-01068-2 |
| spellingShingle | Zhuokun Feng Masaki Nasu Gehan Devendra Ayman A. Abdul-Ghani Owen T. M. Chan Jeffrey A. Borgia Zitong Gao Hanqiu Zhang Yu Chen Ting Gong Gang Luo Hua Yang Lang Wu Yuanyuan Fu Youping Deng Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures Communications Medicine |
| title | Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures |
| title_full | Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures |
| title_fullStr | Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures |
| title_full_unstemmed | Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures |
| title_short | Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures |
| title_sort | liquid biopsy diagnostics for non small cell lung cancer via elucidation of trna signatures |
| url | https://doi.org/10.1038/s43856-025-01068-2 |
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