What question did this study set out to answer?

The aim is to establish a predictive biomarker for lung cancer risk using circulating exosomal small non-coding RNAs.

April 5, 2026Cancer Research0 citations

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

ZFZhuokun Feng MNMasaki Nasu LHLauren Higa

Puntos clave

The aim is to establish a predictive biomarker for lung cancer risk using circulating exosomal small non-coding RNAs.
Profiled circulating exosomal sncRNAs in two cohorts: one discovery cohort and one independent validation cohort.
Employed a 5×5 nested cross-validation method integrating differential expression and classifier comparisons.
Identified a 31-sncRNA signature using random forest as the optimal model for predicting lung cancer.
Achieved excellent discrimination in the discovery cohort with AUC=0.97, sensitivity=0.93, and specificity=1.00.
Moderate performance in the validation cohort, with AUC=0.73 and AUPRC=0.85 indicating effective discrimination between cancer patients and healthy individuals.
The risk score was significantly associated with incident lung cancer, independent of demographic factors, with multivariable OR=13.19.

Resumen

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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