通过机器学习综合多组学鉴定骨质疏松症中的新型诊断生物标志物和免疫代谢治疗靶点
Integrated Multi-Omics Identification of Novel Diagnostic Biomarkers and Immunometabolic Therapeutic Targets in Osteoporosis via Machine Learning.
摘要(中文译文)
骨质疏松症（OP）是一种多因素骨骼疾病，以骨量减少和微结构退化为特征。我们通过整合生物信息学和机器学习分析，研究了潜在的诊断生物标志物和免疫代谢靶点。对经批次校正的GEO转录组数据采用WGCNA分析，以识别与OP相关的模块。使用12种机器学习算法（113种组合）建立了最优诊断模型。通过CIBERSORT和GSEA评估免疫浸润和通路活性，并在血浆样本中通过qRT-PCR验证核心基因。最佳的基于随机森林的模型在训练数据集和验证数据集中均具有良好的预测性能（AUC > 0.80）。最终筛选出十个核心基因作为候选诊断生物标志物：POLA1、ARAP1、MAP7D1、TSPYL2、MRPL4、PPP1R14D、INTS1、SMARCB1、DLK1和FPGT。功能分析显示这些基因富集于免疫相关和代谢通路，包括PI3K-Akt信号传导和磷酸戊糖代谢。这些基因与免疫细胞改变相关，如CD8⁷ T细胞、Th17细胞和巨噬细胞。qRT-PCR验证证实了它们在OP样本中的显著差异表达。本研究强调了免疫代谢失调在OP病理生物学中的核心作用。尽管外部验证队列存在异质性且样本量较小，但我们的整合分析流程提高了潜在生物标志物的精确性和临床相关性。研究结论认为，本工作识别了十个可能的诊断生物标志物，并为理解免疫代谢与OP之间的相互作用提供了机制，这将有助于指导OP精准诊断和治疗的进一步研究。
Abstract
Osteoporosis (OP) is a multifactorial skeletal disorder marked by reduced bone mass and microstructural deterioration. We investigated potential diagnostic biomarkers and immunometabolic targets through integrated bioinformatics and machine learning analysis. Batch-corrected GEO transcriptomic data were analyzed by WGCNA to identify OPassociated modules. An optimal diagnostic model was established using 12 machine learning algorithms (113 combinations). Immune infiltration and pathway activity were evaluated by CIBERSORT and GSEA, and hub genes were validated by qRT-PCR in plasma samples. The best random forest-based model had good predictive performance for both the training dataset and validation dataset (AUC > 0.80). Ten hub genes were finally screened out as candidate diagnostic biomarkers: POLA1, ARAP1, MAP7D1, TSPYL2, MRPL4, PPP1R14D, INTS1, SMARCB1, DLK1, and FPGT. Functional analyses showed enrichment in immunerelated and metabolic pathways, including PI3K-Akt signaling and pentose phosphate metabolism. These genes correlated with immune cell alterations, such as CD8⁷ T cells, Th17 cells, and macrophages. qRT-PCR validation confirmed their significant differential expression in OP samples. This study underscores immune-metabolic dysregulation at the core of OP pathobiology. Although heterogeneity existed in external validation cohorts, and sample sizes were small, our integrative analysis pipeline enhances the precision and clinical relevance of potential biomarkers. The study concluded that this work identified ten possible diagnostic biomarkers, and provided a mechanism for understanding the interaction between immune metabolism and OP, which will help guide further research on precision diagnosis and treatment in OP.
如何引用
AMA
Kai-Kai Ding, Yu Zhang, Ning Yang, Jing-Yi Hou, Nai-Qiang Zhu. Integrated Multi-Omics Identification of Novel Diagnostic Biomarkers and Immunometabolic Therapeutic Targets in Osteoporosis via Machine Learning.. Combinatorial chemistry & high throughput screening. 2026; doi:10.2174/0113862073442187260330101528.
APA
Kai-Kai Ding, Yu Zhang, Ning Yang, Jing-Yi Hou, Nai-Qiang Zhu (2026). Integrated Multi-Omics Identification of Novel Diagnostic Biomarkers and Immunometabolic Therapeutic Targets in Osteoporosis via Machine Learning.. Combinatorial chemistry & high throughput screening. https://doi.org/10.2174/0113862073442187260330101528
GB/T 7714
Kai-Kai Ding, Yu Zhang, Ning Yang, Jing-Yi Hou, Nai-Qiang Zhu. Integrated Multi-Omics Identification of Novel Diagnostic Biomarkers and Immunometabolic Therapeutic Targets in Osteoporosis via Machine Learning.[J]. Combinatorial chemistry & high throughput screening, 2026 doi:10.2174/0113862073442187260330101528.
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