Background: Lung cancer is the leading cause of cancer-related mortality globally, with late-stage diagnoses contributing to poor survival rates. While lung cancer screening with low-dose computed tomography (LDCT) has proven effective in reducing mortality among heavy smokers, its limitations, including high false-positive rates and resource intensiveness, restrict widespread use. Liquid biopsy, particularly using microRNA (miRNA) biomarkers, offers a promising adjunct to current screening strategies. This study aimed to evaluate the predictive power of a panel of serum miRNA biomarkers for lung cancer detection. Patients and Methods: A case-control study was conducted at two tertiary hospitals, enrolling 82 lung cancer cases and 123 controls. We performed an extensive literature review to shortlist 25 candidate miRNAs, of which 16 showed a significant two-fold increase in expression compared to the controls. Machine learning techniques, including Random Forest, K-Nearest Neighbors, Neural Networks, and Support Vector Machines, were employed to identify the top six miRNAs. We then evaluated predictive models, incorporating these biomarkers with lung nodule characteristics on LDCT. Results: A prediction model utilising six miRNA biomarkers (mir-196a, mir-1268, mir-130b, mir-1290, mir-106b and mir-1246) alone achieved area under the curve (AUC) values ranging from 0.78 to 0.86, with sensitivities of 70–78% and specificities of 73–85%. Incorporating lung nodule size significantly improved model performance, yielding AUC values between 0.96 and 0.99, with sensitivities of 92–98% and specificities of 93–98%. Conclusions: A prediction model combining serum miRNA biomarkers and nodule size showed high predictive power for lung cancer. Integration of the prediction model into current lung cancer screening protocols may improve patient outcomes.
背景：肺癌是全球癌症相关死亡的主要原因，晚期诊断导致生存率低下。虽然低剂量计算机断层扫描（LDCT）肺癌筛查已被证明能有效降低重度吸烟者的死亡率，但其局限性，包括高假阳性率和资源密集性，限制了广泛应用。液体活检，特别是使用微小RNA（miRNA）生物标志物，为现有筛查策略提供了一种有前景的辅助手段。本研究旨在评估一组血清miRNA生物标志物对肺癌检测的预测能力。 患者与方法：在两家三甲医院进行了一项病例对照研究，纳入82例肺癌病例和123例对照。我们进行了广泛的文献回顾，初步筛选出25个候选miRNA，其中16个与对照组相比表达量显著增加两倍。采用随机森林、K近邻、神经网络和支持向量机等机器学习技术，确定了前六个miRNA。随后，我们评估了预测模型，将这些生物标志物与LDCT上的肺结节特征相结合。 结果：仅使用六个miRNA生物标志物（mir-196a、mir-1268、mir-130b、mir-1290、mir-106b和mir-1246）的预测模型，其曲线下面积（AUC）值在0.78至0.86之间，敏感性为70–78%，特异性为73–85%。纳入肺结节大小后，模型性能显著提升，AUC值达到0.96至0.99，敏感性为92–98%，特异性为93–98%。 结论：结合血清miRNA生物标志物和结节大小的预测模型显示出对肺癌的高预测能力。将该预测模型整合到现有肺癌筛查方案中，可能改善患者预后。
Development of a miRNA-Based Model for Lung Cancer Detection
肿瘤(癌症)患者之家