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W. Li

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    ORAL 39 - Potential Biomarkers for CT Screening (ID 149)

    • Event: WCLC 2015
    • Type: Oral Session
    • Track: Screening and Early Detection
    • Presentations: 1
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      ORAL39.05 - Identification of miRNAs as Biomarkers for Early Diagnosis of Lung Cancers (ID 808)

      16:45 - 18:15  |  Author(s): W. Li

      • Abstract

      Current clinical diagnostic methods lack the specificity in detecting lung cancer patients. The issue is critical for stage I & II patients as there are no available biomarkers to indicate which high-risk patients should undergo adjuvant therapy. There is considerable evidence that microRNA plays a very important role in lung carcinogenesis. We postulated that the expression pattern of multiple microRNAs (miRNAs) could aid clinicians in detecting cancer patients thus reducing the mortality of lung cancer.

      Differential expressed miRNAs were analyzed by miRNA microarrays in 15 paired non-small-cell lung cancer (NSCLC) tumors and distant normal tissues. The identified miRNAs were further validated by qRT-PCR using snap-frozen lung tissue samples collected from independent 22 patients with NSCLC. Classification analyses of miRNA expression data were performed by the Bayesian Compound Covariate predictor (BCCP). The expression levels of miR-141-5p, miR-301a-3p and miR-1244 were also analyzed by qRT-PCR in serum samples collected from 60 patients with lung cancer and 50 healthy controls.

      A total of 41 miRNAs was identified with significantly elevated levels in patients with lung cancer by profiling microRNA array, of which 12 miRNAs were further validated in the independent sample cohort. Multiplexing analysis with the panel of 12 miRNAs generated the highest discriminatory power in separating NSCLC from normal tissues with an AUC of 0.915 (95% CI = 0.894-1.000; P <0.001). Leave-one-out cross-validation revealed the 85% sensitivity and 95% specificity at a cutoff score of 0.5. In addition, serum miR-1244 was significantly upregulated in an independent trial and could distinguish NSCLC from controls with 77.6% sensitivity and 78.7% specificity.

      Our 12-miRNA classifier might have potential clinical utility in discriminating NSCLC from healthy population.