Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.
肺癌的早期检测对患者生存和治疗至关重要。新一代测序分析技术的进步使得基于游离DNA的液体活检能够检测癌症中的染色体易位、体细胞突变和拷贝数变异等变化。利用癌症标志物进行机器学习分析是识别癌症模式和异常极具前景的工具，因此开发基于机器学习的分析方法至关重要。本研究收集了92例肺癌患者和80例健康个体的血液样本进行分析比较。血液中肺癌标志物细胞角蛋白19片段和癌胚抗原的检测结果显示患者组与对照组存在显著差异。我们运用自适应增强算法、多层感知器和逻辑回归模型，结合癌症标志物、游离DNA浓度及拷贝数变异筛查数据进行机器学习分析并获得受试者工作特征曲线下面积值。进一步研究发现，整合多组学数据进行机器学习分析获得的曲线下面积值高于单一指标分析结果，表明该方法有望实现高精度癌症诊断。总体而言，基于血液多组学数据的机器学习分析模型展现出卓越的肺癌与健康人群区分能力，这为构建肺癌诊断模型提供了重要潜力。
肿瘤(癌症)患者之家