MARM：一种基于支气管肺泡灌洗液mNGS数据中宿主来源的CNV和微生物混合物进行恶性肿瘤风险预测的框架

摘要(中文译文)

恶性肿瘤的早期识别与风险评估对于改善临床决策和患者预后至关重要。支气管肺泡灌洗液（BALF）宏基因组二代测序（mNGS）数据同时包含微生物来源和宿主来源的信号，而将此类数据拓展至肿瘤相关应用的一个关键挑战在于，如何从这一复杂的混合背景中稳健地提取对恶性肿瘤具有判别价值的宿主特征。为解决这一问题，我们开发了MARM，一种以宿主来源拷贝数变异（CNV）为核心的恶性肿瘤风险预测方法。MARM利用BALF mNGS数据中的宿主来源reads，进行全基因组窗口覆盖度定量、标准化与偏倚校正、参考基线构建以及基于主成分的去噪，从而衍生出窗口水平的CNV特征用于恶性肿瘤风险建模。此外，研究引入了基于伪标签的扩展策略，通过高置信度筛选纳入弱标注样本，并利用CNV特征、微生物特征及联合特征系统评估了XGBoost、随机森林和广义线性模型（GLM）的性能。基于宿主来源CNV特征构建的模型持续优于基于微生物特征的模型，并达到与联合特征模型相当的性能，而联合建模并未提供稳定的额外获益。这些发现表明，在当前数据设置和特征构建策略下，CNV是比微生物特征更稳定、信息量更丰富的判别信号。在所评估的分类器中，XGBoost与窗口水平CNV特征的兼容性最佳，总体优于随机森林和GLM。在独立验证集上，经伪标签增强的MARM取得了最佳总体性能，灵敏度为0.686，特异度为0.975，准确率为0.847，约登指数为0.671。相比之下，微生物特征未表现出稳定的独立判别能力，联合建模也未带来明确或持续的性能提升。综上，这些结果表明，在微生物混合的BALF mNGS数据中，宿主来源CNV比所评估的微生物特征更适合作为恶性肿瘤风险预测的核心建模信号。MARM为复杂临床样本中的恶性肿瘤预测提供了新的方法学框架，并为更深入挖掘mNGS数据中的宿主来源信号及相关辅助诊断应用提供了参考。

Abstract

Early identification and risk assessment of malignancy are essential for improving clinical decision-making and patient outcomes. Bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) data contain both microbial and host-derived signals, and a key challenge in extending such data to tumor-associated applications is the robust extraction of host features with discriminative value for malignancy from this complex, admixed background. To address this problem, we developed MARM, a malignancy risk prediction method centered on host-derived copy number variation (CNV). Using host-derived reads from BALF mNGS data, MARM performs genome-wide window-based coverage quantification, normalization and bias correction, reference baseline construction, and principal component-based denoising to derive window-level CNV features for malignancy risk modeling. In addition, a pseudo-label-based extension strategy was introduced to incorporate weakly labeled samples through high-confidence screening, and the performance of XGBoost, Random Forest, and generalized linear models (GLM) was systematically evaluated using CNV features, microbial features, and combined features. Models built on host-derived CNV features consistently outperformed those based on microbial features and achieved performance comparable to combined-feature models, while joint modeling did not provide a stable additional benefit. These findings indicate that, under the current data setting and feature construction strategy, CNV represents a more stable and informative discriminative signal than microbial features. Among the evaluated classifiers, XGBoost showed the best compatibility with window-level CNV features and outperformed Random Forest and GLM overall. On the independent validation set, the pseudo-label-enhanced MARM achieved the best overall performance, with a sensitivity of 0.686, specificity of 0.975, accuracy of 0.847, and Youden index of 0.671. By contrast, microbial features did not show stable independent discriminative ability, and combined modeling did not yield clear or sustained performance gains. Together, these results indicate that, in microbially admixed BALF mNGS data, host-derived CNV is more suitable than the evaluated microbial features as the core modeling signal for malignancy risk prediction. MARM provides a new methodological framework for malignancy prediction in complex clinical samples and offers a reference for deeper exploitation of host-derived signals in mNGS data and related auxiliary diagnostic applications.

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