Abstract 6517: A decade-ahead signal of lung cancer from circulating exosomal sncRNAs

Abstract Lung cancer remains the leading cause of cancer-related mortality worldwide, yet current screening criteria focusing on heavy smoking overlook a significant portion of at-risk individuals. To develop a robust predictive biomarker, we profiled circulating exosomal small non-coding RNAs (sncRNAs, including miRNAs, piRNAs, tiRNAs, and tRFs) in a prospective discovery cohort (UHCC; n = 202 smokers, with up to 16-year follow-up) and an independent validation cohort (CHTN; n = 186) including healthy individuals or patients with either malignant or benign tumor of the lung. A rigorous 5×5 nested cross-validation pipeline integrating differential expression, correlation pruning, and eight classifier comparisons identified a 31-sncRNA panel. Random forest was selected as the optimal model and yielded excellent discrimination in UHCC (AUC=0.97), with sensitivity 0.93 and specificity 1.00 at the Youden-optimized threshold. In the independent validation cohort, the signature demonstrated moderate performance, particularly in discriminating diagnosed cancer patients from healthy controls (AUC=0.73; AUPRC=0.85). Furthermore, the resulting risk score was strongly associated with incident lung cancer in the UHCC cohort, independent of demographic and smoking factors (multivariable OR=13.19, 95% CI 6.83-25.45), and predicted a shorter time-to-diagnosis in a Fine-Gray competing risks model (sHR=4.73, 95% CI 3.61-6.21) with a significant non-linear dose-response. Landmark and time-dependent analyses confirmed robust discrimination up to a decade pre-diagnosis, although precision was attenuated at longer intervals. At last, pathway analysis of the signature's targets implicated key oncogenic pathways, including the PI3K-Akt, p53, MAPK, ErbB, and mTOR signaling pathways. This work establishes a panel of novel exosomal sncRNA signatures as a powerful risk prediction biomarker for lung cancer, enabling early risk stratification and creating a critical window for timely clinical intervention. Citation Format: Zhuokun Feng, Masaki Nasu, Lauren Higa, Isam M. Ibrahim, Loïc L. Marchand, Youping Deng. A decade-ahead signal of lung cancer from circulating exosomal sncRNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6517.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-6517
Primary Topic
Extracellular vesicles in disease
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article
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article

Abstract 6517: A decade-ahead signal of lung cancer from circulating exosomal sncRNAs

Zhuokun Feng, Isam Ibrahim, L. Le Marchand, Masaki Nasu et al.
Cancer Research
Extracellular vesicles in disease
article

Abstract 6517: A decade-ahead signal of lung cancer from circulating exosomal sncRNAs

Zhuokun Feng, Isam Ibrahim, L. Le Marchand, Masaki Nasu, Youping Deng, Lauren Higa
article en

Abstract

Abstract Lung cancer remains the leading cause of cancer-related mortality worldwide, yet current screening criteria focusing on heavy smoking overlook a significant portion of at-risk individuals. To develop a robust predictive biomarker, we profiled circulating exosomal small non-coding RNAs (sncRNAs, including miRNAs, piRNAs, tiRNAs, and tRFs) in a prospective discovery cohort (UHCC; n = 202 smokers, with up to 16-year follow-up) and an independent validation cohort (CHTN; n = 186) including healthy individuals or patients with either malignant or benign tumor of the lung. A rigorous 5×5 nested cross-validation pipeline integrating differential expression, correlation pruning, and eight classifier comparisons identified a 31-sncRNA panel. Random forest was selected as the optimal model and yielded excellent discrimination in UHCC (AUC=0.97), with sensitivity 0.93 and specificity 1.00 at the Youden-optimized threshold. In the independent validation cohort, the signature demonstrated moderate performance, particularly in discriminating diagnosed cancer patients from healthy controls (AUC=0.73; AUPRC=0.85). Furthermore, the resulting risk score was strongly associated with incident lung cancer in the UHCC cohort, independent of demographic and smoking factors (multivariable OR=13.19, 95% CI 6.83-25.45), and predicted a shorter time-to-diagnosis in a Fine-Gray competing risks model (sHR=4.73, 95% CI 3.61-6.21) with a significant non-linear dose-response. Landmark and time-dependent analyses confirmed robust discrimination up to a decade pre-diagnosis, although precision was attenuated at longer intervals. At last, pathway analysis of the signature's targets implicated key oncogenic pathways, including the PI3K-Akt, p53, MAPK, ErbB, and mTOR signaling pathways. This work establishes a panel of novel exosomal sncRNA signatures as a powerful risk prediction biomarker for lung cancer, enabling early risk stratification and creating a critical window for timely clinical intervention. Citation Format: Zhuokun Feng, Masaki Nasu, Lauren Higa, Isam M. Ibrahim, Loïc L. Marchand, Youping Deng. A decade-ahead signal of lung cancer from circulating exosomal sncRNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6517.

Cancer ResearchVol. 86(7_Supplement)
University of Hawaiʻi at Mānoa (US)
Reduced inequalities
Openalex Percentile: Top 7%
Extracellular vesicles in disease
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