Non-small cell lung cancer is the predominant form of lung cancer and is associated with a poor prognosis. MiRNAs implicated in cancer initiation and progression can be easily detected in liquid biopsy samples and have the potential to serve as non-invasive biomarkers. In this study, we employed next-generation sequencing to globally profile miRNAs in serum samples from 71 early-stage NSCLC patients and 47 non-cancerous pulmonary condition patients. Preliminary analysis of differentially expressed miRNAs revealed 28 upregulated miRNAs in NSCLC compared to the control group. Functional enrichment analyses unveiled their involvement in NSCLC signaling pathways. Subsequently, we developed a gradient-boosting decision tree classifier based on 2588 miRNAs, which demonstrated high accuracy (0.837), sensitivity (0.806), and specificity (0.859) in effectively distinguishing NSCLC from non-cancerous individuals. Shapley Additive exPlanations analysis improved the model metrics by identifying the top 15 miRNAs with the strongest discriminatory value, yielding an AUC of 0.96 ± 0.04, accuracy of 0.896, sensitivity of 0.884, and specificity of 0.903. Our study establishes the potential utility of a non-invasive serum miRNA signature as a supportive tool for early detection of NSCLC while also shedding light on dysregulated miRNAs in NSCLC biology. For enhanced credibility and understanding, further validation in an independent cohort of patients is warranted.
非小细胞肺癌是肺癌的主要类型，预后较差。参与癌症发生与发展的微小RNA在液体活检样本中易于检测，具备作为无创生物标志物的潜力。本研究采用新一代测序技术，对71例早期非小细胞肺癌患者和47例非癌性肺部疾病患者的血清样本进行全谱微小RNA分析。差异表达微小RNA的初步分析显示，与对照组相比，非小细胞肺癌患者中有28种微小RNA表达上调。功能富集分析揭示了这些微小RNA参与非小细胞肺癌相关信号通路。随后，我们基于2588种微小RNA构建了梯度提升决策树分类器，该模型在有效区分非小细胞肺癌患者与非癌性个体方面表现出较高的准确度（0.837）、灵敏度（0.806）和特异度（0.859）。通过Shapley加性解释分析筛选出区分效能最强的15种微小RNA，进一步优化了模型指标，获得曲线下面积0.96±0.04、准确度0.896、灵敏度0.884和特异度0.903。本研究证实了无创血清微小RNA特征谱作为非小细胞肺癌早期检测辅助工具的潜在价值，同时揭示了非小细胞肺癌生物学中失调的微小RNA。为增强结果的可信度与可理解性，需要在独立患者队列中进一步验证。
肿瘤(癌症)患者之家