RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
乳腺癌诊断的机器学习驱动线粒体基因特征开发与验证
英文原题：Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.
查看英文原题
Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.
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研究概要
本研究识别了与 BC 相关的线粒体基因特征，并提出了一个区分肿瘤与正常组织的计算模型。这些发现为未来的生物标志物开发提供了潜在线索，但需要进一步的临床和功能验证。
研究思路结论见上方概要
背景
乳腺癌（BC）是全球女性中最常见的恶性肿瘤之一，而线粒体功能障碍构成了其致病机制之一。
目的
探讨线粒体功能相关基因与BC进展之间的关系。
方法
我们通过GEO数据库识别BC差异表达基因，构建加权共表达网络以确定BC发病机制相关的关键模块。利用113种机器学习算法和MitoCarta线粒体遗传学数据，我们开发了基于线粒体基因的诊断模型。GO/KEGG富集分析描绘了线粒体基因与BC相关的生物学过程，为理解BC机制提供了线索。高通量组织芯片和免疫组织化学（IHC）验证了关键基因在组织中的局部表达。CiberSort免疫浸润分析突出了NK和T细胞在BC中的作用；单细胞分析识别了肿瘤微环境细胞类型中的基因表达模式。计算药物预测和分子对接探索了靶向治疗候选药物。此外，我们进行了分子动力学模拟。
结果
glmBoost+LDA模型在验证队列中具有最高的C-index（0.947），包括18个潜在的BC生物标志物（例如，ACADS，AUC = 0.810；AIFM2，AUC = 0.806）。实验验证结果显示，ACADS在癌组织中的表达评分显著低于邻近非癌组织。免疫浸润和单细胞分析强调了NK细胞和T细胞在BC中的关键作用。Disulfiram和eugenol被预测为潜在治疗药物，并通过对接验证。分子动力学模拟验证了Eugenol与靶蛋白AIFM2和ACADS表现出强结合相互作用。
展开英文摘要原文
BACKGROUND
Breast cancer (BC) ranks among the most prevalent malignant tumors in women globally, with mitochondrial dysfunction constituting one of its pathogenic mechanisms.
OBJECTIVES
To investigate the relationship between mitochondrial function-related genes and BC progression.
METHODS
We identified BC differentially expressed genes via the GEO database, constructed a weighted co-expression network to determine BC pathogenesis-related key modules. Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. GO/KEGG enrichment analyses delineated BC-related biological processes of mitochondrial genes, offering clues for understanding BC mechanism. High-throughput tissue chip and Immunohistochemistry (IHC) validated key genes' local expression in tissues. CiberSort immune infiltration analysis highlighted NK and T cells' role in BC; single-cell analysis identified gene expression patterns across tumor microenvironment cell types. Computational drug prediction and molecular docking explored targeted therapeutic candidates. Additionally, we conducted molecular dynamics simulations.
RESULTS
The glmBoost+LDA model had the highest C-index (0.947) in the validated cohort, including 18 potential BC biomarkers (e.g., ACADS, AUC = 0.810; AIFM2, AUC = 0.806). The results of experimental validation showed that the expression score of ACADS in cancerous tissues was significantly lower than that in adjacent non-cancerous tissues. Immune infiltration and single-cell analyses emphasized the crucial roles of NK cells and T cells in BC. Disulfiram and eugenol were predicted as potential therapeutics and validated by docking. Molecular dynamics simulations validated that Eugenol exhibits strong binding interactions with the target proteins AIFM2 and ACADS.
CONCLUSIONS
This study identifies mitochondrial gene signatures associated with BC and proposes a computational model distinguishing tumor from normal tissue. These findings offer potential leads for future biomarker development but require additional clinical and functional validation.
论文信息
- 作者
- Tong S、Teng F、Kong W、Tian X、Guo D、Liu M、Ren J
- 单位
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.China
- 文献类型
- 验证性研究
- 期刊
- Frontiers in immunology2025
- 原文标识
- PubMed 41415282 · DOI 10.3389/fimmu.2025.1712089
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