机器学习驱动的肺癌分期基因表达谱分析。

IF 1.9
Yinbo Wang, Kai Fu
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引用次数: 0

摘要

肺癌仍然是癌症相关死亡的主要原因,准确的分期对指导治疗至关重要。新一代测序(NGS)和机器学习(ML)的进步使分类更加精确,改进了传统的基于成像的方法。目的:本回顾性研究应用XGBoost交叉验证(CV)对来自癌症基因组图谱(TCGA)队列的993例患者的RNA-Seq数据进行早期和晚期肺癌分类。方法对训练数据采用Wilcoxon秩和检验进行基因选择,并通过交叉验证对XGBoost模型进行优化。使用曲线下面积(AUC)评估模型性能,并进行跨分类阈值的敏感性-特异性分析。结果XGBoost模型的测试AUC为0.6534,确定了40个关键基因,优化了预测精度,同时最大限度地减少了过拟合。0.3和0.4为最佳阈值,以平衡临床应用的敏感性和特异性。结论将RNA-Seq数据与机器学习相结合可提高肺癌分期准确性。未来的研究应集中在数据集扩展、模型标杆化和多组学整合等方面,以提高临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-driven gene expression profiling for lung cancer stage determination.

BackgroundLung cancer remains a leading cause of cancer-related mortality, with accurate staging essential for guiding treatment. Advances in next-generation sequencing (NGS) and machine learning (ML) enable more precise classification, improving on traditional imaging-based methods.ObjectiveThis retrospective study applies XGBoost with cross-validation (CV) to classify early vs. late-stage lung cancer using RNA-Seq data from 993 patients in The Cancer Genome Atlas (TCGA) cohort.MethodsGene selection was conducted using the Wilcoxon rank-sum test on training data, and the XGBoost model was optimized via cross-validation. Model performance was assessed using the Area Under the Curve (AUC), with sensitivity-specificity analysis across classification thresholds.ResultsThe XGBoost model achieved a test AUC of 0.6534, identifying 40 key genes that optimize predictive accuracy while minimizing overfitting. Thresholds of 0.3 and 0.4 were optimal, balancing sensitivity and specificity for clinical application.ConclusionsIntegrating RNA-Seq data with machine learning improves lung cancer staging accuracy. Future research should focus on dataset expansion, model benchmarking, and multi-omics integration to enhance clinical applicability.

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