Background: An exhaled microRNA-based lung cancer case-control discriminant biomarker strategy is reported.Methods: A microRNA-seq discovery effort compared paired tumor to non-tumor tissue, was reconciled with analogous TCGA and published literature-based tissue-discriminant microRNA data, yielding a candidate panel of 24 microRNAs that are upregulated in either adenocarcinomas and/or squamous cell carcinomas. The technical feasibility of microRNA-PCR assays in exhaled breath condensate (EBC)

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... as tested. The airway origin of exhaled microRNAs was then topographically "fingerprinted", using paired EBC and bronchoscopic samples. For initial EBC testing, a clinic-based case-control set of 351 individuals (166 NSCLC cases, 185 non-cancer controls) was interrogated with the 24-candidate microRNA panel by qualitative RT-PCR, and curated by melt curve analysis. Data were analyzed by both logistic regression (LR), and by random-forest (RF) models, validated by iterative resampling.Results: Both feasibility of exhaled microRNA detection, and its origins in part from lower airway sources, were confirmed. LR models adjusted for age, sex, smoking status, pack years, quit-years, and underlying lung disease identified exhaled miR-21, 33b, 212 (p.adj,=0.019, 0.018, 0.033, resp.) as case-control discriminant. For the RF analysis, the combined clinical + microRNA models showed modest added discrimination capacity (1.1–2.5%) beyond the clinical models alone: by subgroup, all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03). Sensitivity, specificity, positive- and negative-predictive values of the clinical + microRNA models for the entire cohort were 71%-76%.Conclusion: This work suggests that exhaled microRNAs are measurable qualitatively; reflect in part lower airway signatures; and if improved/refined, can potentially help distinguish lung cancer cases from controls.