Development and validation of a small extracellular vesicle-derived RNA signature for early diagnosis of lung adenocarcinoma and prognosis in advanced stages

This study developed and validated a diagnostic signature comprising five small extracellular vesicle (sEV)-derived long RNAs (ATF4, GRAP2, MCL1, PAK2, PIK3CB) for distinguishing early-stage lung adenocarcinoma (LUAD) from benign pulmonary nodules and assessing prognosis in advanced LUAD. Utilizing a multi-center cohort of 698 participants, researchers employed RNA sequencing and quantitative PCR to analyze plasma sEV RNA profiles. Differentially expressed mRNAs and long intergenic non-coding RNAs (lincRNAs) were identified using LASSO regression to construct a diagnostic model. The signature demonstrated high diagnostic accuracy with an area under the curve (AUC) of 0.971 in the validation cohort and 0.950 in the prospective cohort. It also surpassed low-dose CT sensitivity (95.24% vs. 71.43%), specificity (100% vs. 93.96%), positive predictive value (100% vs. 45.45%) and negative predictive value (99.67% vs. 97.90%) in the prospective cohort. In advanced LUAD patients undergoing chemoradiotherapy or PD-L1 inhibitor therapy, lower expression of these RNAs correlated with improved progression-free survival (PFS; HR = 0.38-0.39). The signature integrates non-invasively detected sEV RNAs to complement LDCT, addressing its high false-positive rate, and offers prognostic insights for personalized treatment strategies. These findings highlight the clinical potential of sEV-derived long RNAs in early LUAD detection and precision oncology.

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Publication Details

Journal
Molecular Cancer
Published
2025-12-03
DOI
https://doi.org/10.1186/s12943-025-02524-2
Citations
3
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
2.42
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article

Development and validation of a small extracellular vesicle-derived RNA signature for early diagnosis of lung adenocarcinoma and prognosis in advanced stages

Zheng Peng, Weihong Sun, Congying Xie, Liangcheng Zheng et al.
3 citations
Molecular Cancer
Extracellular vesicles in disease
2.42
article

Development and validation of a small extracellular vesicle-derived RNA signature for early diagnosis of lung adenocarcinoma and prognosis in advanced stages

Zheng Peng, Weihong Sun, Congying Xie, Liangcheng Zheng, Yu Liu, Xiaoming Lin, Lanxiao Shen, Dezhi Cheng, Rui Meng, Yida Li, Yanjun Gao, Hua Chen, Huanle Pan
article en
3 citations

Abstract

This study developed and validated a diagnostic signature comprising five small extracellular vesicle (sEV)-derived long RNAs (ATF4, GRAP2, MCL1, PAK2, PIK3CB) for distinguishing early-stage lung adenocarcinoma (LUAD) from benign pulmonary nodules and assessing prognosis in advanced LUAD. Utilizing a multi-center cohort of 698 participants, researchers employed RNA sequencing and quantitative PCR to analyze plasma sEV RNA profiles. Differentially expressed mRNAs and long intergenic non-coding RNAs (lincRNAs) were identified using LASSO regression to construct a diagnostic model. The signature demonstrated high diagnostic accuracy with an area under the curve (AUC) of 0.971 in the validation cohort and 0.950 in the prospective cohort. It also surpassed low-dose CT sensitivity (95.24% vs. 71.43%), specificity (100% vs. 93.96%), positive predictive value (100% vs. 45.45%) and negative predictive value (99.67% vs. 97.90%) in the prospective cohort. In advanced LUAD patients undergoing chemoradiotherapy or PD-L1 inhibitor therapy, lower expression of these RNAs correlated with improved progression-free survival (PFS; HR = 0.38-0.39). The signature integrates non-invasively detected sEV RNAs to complement LDCT, addressing its high false-positive rate, and offers prognostic insights for personalized treatment strategies. These findings highlight the clinical potential of sEV-derived long RNAs in early LUAD detection and precision oncology.

Molecular CancerVol. 25(1)
Wenzhou Medical University (CN), Discovery Institute (US), First Affiliated Hospital of Wenzhou Medical University (CN)
Openalex Percentile: Top 10%
Extracellular vesicles in disease
2.42
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