Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
Comput Methods Programs Biomed. 2024 Dec;257:108481. doi: 10.1016/j.cmpb.2024.108481. Epub 2024 Oct 26.
Immunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions METHODS: To address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance RESULTS: We demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities CONCLUSION: The proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.
免疫疗法有望提高乳腺癌的病理完全缓解率，但仅限于特定的患者群体。因此，确定预测治疗反应的因素至关重要。基因表达和调控，内在地在复杂的网络中运作，是细胞过程的基本分子机制，并且经常作为强大的生物标志物。然而，当代的特征选择方法面临两个关键挑战：建模的不透明性和基因-基因相互作用的稀缺性。
为了解决这些局限性，我们设计了一种新的基于合作博弈理论的特征选择方法，与复杂的机器学习模型和谐地集成。这种方法识别相互关联的基因调控网络生物标志物模块，具有先验的遗传连锁结构。具体来说，我们利用网络上的 Shapley 值来量化特征的重要性，同时根据网络扩展原则和节点邻接性策略地约束它们的集成，从而在特征选择中提高可解释性。我们将我们的方法应用于公开的乳腺癌免疫治疗反应的单细胞 RNA 测序数据集，使用识别的特征基因集作为生物标志物。使用独立验证的功能富集分析进一步说明了它们在预测性能方面的有效性。
我们在具有网络结构的数据中展示了所提出方法的复杂性和卓越性。它揭示了一个包含 27 个基因的免疫治疗反应的凝聚生物标志物模块。值得注意的是，该模块在乳腺癌患者的抗 PD1 治疗结果的精确预测方面表现出色，分类准确率为 0.905，AUC 值为 0.971，突出了其独特的阐明基因功能的能力。
所提出的方法可有效识别网络模块生物标志物，所检测的抗 PD1 反应生物标志物可丰富我们对免疫治疗潜在生理机制的理解，为实现精准医学提供了有前途的应用。
