Plasma exosomal tsRNA may be used as a marker for differential diagnosis of benign and malignant pulmonary nodules - Na Zhou; Kecheng Li; Li Xie (Journal of Translational Medicine)
Abstract Background tRNA-derived small RNAs (tsRNAs) have garnered significant attention in the field of cancer research, however, exosomal tsRNAs remain relatively understudied as potential biomarkers in the pulmonary nodules. This study aims to identify exosomal tsRNAs that are differentially expressed between benign and malignant pulmonary nodules, integrate these findings with other clinical parameters, and develop a novel predictive model to estimate the likelihood of malignancy in pulmonary nodules. Methods Exosomes were extracted from plasma of patients with benign pulmonary nodules and malignant pulmonary nodules (early-stage lung cancer), then characterized using transmission electron microscopy (TEM), qNano, and western blot. Differentially expressed tsRNAs were identified through small RNA microarray screening and validated by Quantitative Real-Time PCR (qRT-PCR). Receiver operating characteristic (ROC) analysis evaluated their diagnostic efficiency, while logistic regression integrated blood and imaging data to build a predictive model. Diagnostic performance was further assessed using random forest and nomogram analyses. Results A total of 43 differentially expressed tsRNAs were identified through small RNA array analysis. Among these, the expression levels of 3’tiRNA-43-GlyGCC-4 and tRF3-17-GlyTCC were significantly higher in patients with benign pulmonary nodules compared to those with early-stage lung cancer. Conversely, the expression of 5’Leader-ValAAC-1-2 was significantly lower in benign cases than in early-stage lung cancer patients. Using logistic regression, a predictive model was constructed by combining these tsRNA biomarkers with blood-based and imaging parameters. The model demonstrated excellent performance in distinguishing early-stage lung cancer from benign pulmonary nodules, achieving an area under the curve (AUC) of 0.9559, with a sensitivity of 91.06% and a specificity of 91.53%. Conclusion Our model highlights its potential as a robust tool for predicting the malignancy probability of pulmonary nodules.