通过人工智能衍生模型提高卵巢癌预后：多组学整合及其治疗意义

IF 5 2区医学 Q2 Medicine

Translational Oncology Pub Date : 2025-06-27 DOI:10.1016/j.tranon.2025.102439

You Wu , Kunyu Wang , Yan Song , Bin Li

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引用次数: 0

摘要

妇科恶性肿瘤，特别是卵巢癌，对妇女的福祉构成巨大挑战，全球发病率和死亡率证明了这一点，强调迫切需要先进的诊断和治疗方式。卵巢癌的异质性对传统的治疗方法提出了挑战，需要探索新的精准医学技术。方法本研究利用多数据集分析构建并验证卵巢癌人工智能衍生预后指数（AIDPI）。转录组数据来自TCGA、ICGC和GEO数据库，包括大量和单细胞RNA测序。AIDPI模型是使用单变量Cox回归分析和机器学习算法的集合来开发和完善的。研究人员对MFAP4基因的功能分析、免疫分析和作用进行了研究，以阐明该模型的生物学机制。结果与现有模型相比，AIDPI模型在预测卵巢癌预后方面具有更高的准确性。它与临床治疗结果相关，包括化疗反应性，并被整合到改善预后分层的nomogram中。功能分析揭示了AIDPI基因对肿瘤免疫浸润和细胞周期调控的影响。单细胞分析揭示了细胞类型特异性表达模式，MFAP4基因因其与患者预后和细胞行为调节相关而被确定为潜在的治疗靶点。在卵巢癌患者的临床样本中，MFAP4在转移灶中高表达，与预后不良相关。体外和体内实验表明，敲低MFAP4可减少卵巢癌细胞的转移。结论AIDPI模型结合多组学数据和人工智能，为卵巢癌的预后和治疗决策提供了高度准确的工具。该模型的性能和生物学见解为推进卵巢癌的精准医学提供了基础。MFAP4的功能和DNA甲基化的影响为前瞻性研究和潜在的治疗干预提供了机会。

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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications

Background

Gynecological malignancies, particularly ovarian cancer, pose a formidable challenge to women's wellbeing, as evidenced by the global incidence and mortality rates, emphasizing the pressing need for advanced diagnostic and treatment modalities. The heterogeneity of ovarian cancer poses challenges for traditional therapeutic approaches, necessitating the exploration of novel, precision medicine techniques.

Methods

This study leveraged multi-dataset analysis to construct and validate an Artificial Intelligence-Derived Prognostic Index (AIDPI) for ovarian cancer. Transcriptome data from the TCGA, ICGC, and GEO databases were utilized, encompassing bulk and single-cell RNA sequencing. The AIDPI model was developed and refined using univariate Cox regression analysis and an ensemble of machine learning algorithms. Functional analysis, immunoprofiling, and the role of the MFAP4 gene were investigated to elucidate the biological mechanisms underlying the model.

Results

The AIDPI model demonstrated superior accuracy in predicting ovarian cancer prognosis compared to existing models. It correlated with clinical treatment outcomes, including chemotherapy responsiveness, and was integrated into a nomogram for improved prognostic stratification. Functional analysis revealed the influence of AIDPI genes on tumor immune infiltration and cell cycle regulation. Single-cell analysis exposed cell type-specific expression patterns, and the MFAP4 gene was identified as a potential therapeutic target due to its association with patient prognosis and modulation of cellular behavior. In clinical samples of ovarian cancer patients, MFAP4 is highly expressed in metastatic lesions and is associated with poor prognosis. In vitro and in vivo experiments, knockdown of MFAP4 reduces the metastasis of ovarian cancer cells.

Conclusion

The AIDPI model offers a highly accurate tool for ovarian cancer prognosis and treatment decision-making, underscored by the integration of multi-omics data and artificial intelligence. The model's performance and biological insights provide a foundation for advancing precision medicine in ovarian cancer. MFAP4′s functionality and the influence of DNA methylation present opportunities for prospective research endeavors and potential therapeutic interventions.

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来源期刊

Translational Oncology ONCOLOGY-

CiteScore

8.40

自引率

2.00%

发文量

314

审稿时长

54 days

期刊介绍： Translational Oncology publishes the results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of oncology patients. Translational Oncology will publish laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer. Peer reviewed manuscript types include Original Reports, Reviews and Editorials.