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基于单细胞测序技术分析上皮细胞相关基因与卵巢癌患者预后的关系

赵丽珠 董莹 邓玥 杨丽华

赵丽珠, 董莹, 邓玥, 杨丽华. 基于单细胞测序技术分析上皮细胞相关基因与卵巢癌患者预后的关系[J]. 昆明医科大学学报, 2024, 45(4): 9-16. doi: 10.12259/j.issn.2095-610X.S20240402
引用本文: 赵丽珠, 董莹, 邓玥, 杨丽华. 基于单细胞测序技术分析上皮细胞相关基因与卵巢癌患者预后的关系[J]. 昆明医科大学学报, 2024, 45(4): 9-16. doi: 10.12259/j.issn.2095-610X.S20240402
Lizhu ZHAO, Ying DONG, Yue DENG, Lihua YANG. Correlation between Epithelial Cell Related Genes and Prognosis of Patients with Ovarian Cancer based on Single Cell Sequencing[J]. Journal of Kunming Medical University, 2024, 45(4): 9-16. doi: 10.12259/j.issn.2095-610X.S20240402
Citation: Lizhu ZHAO, Ying DONG, Yue DENG, Lihua YANG. Correlation between Epithelial Cell Related Genes and Prognosis of Patients with Ovarian Cancer based on Single Cell Sequencing[J]. Journal of Kunming Medical University, 2024, 45(4): 9-16. doi: 10.12259/j.issn.2095-610X.S20240402

基于单细胞测序技术分析上皮细胞相关基因与卵巢癌患者预后的关系

doi: 10.12259/j.issn.2095-610X.S20240402
基金项目: 国家自然科学基金资助项目(82360579);云南省万人计划名医专项基金资助项目(YNWR-MY-2019-037);昆明医科大学创新团队基金资助项目(CXTD202008);昆明医科大学第二附属医院对外合作研究基金资助项目(2022dwhz06);云南省科技厅-昆明医科大学应用基础研究联合专项基金资助项目(202401AY070001-053)
详细信息
    作者简介:

    赵丽珠(1997~),女,白族,云南剑川人,医学硕士,住院医师,主要从事妇科肿瘤研究工作

    通讯作者:

    杨丽华,E-mail:lihuazhang33@sina.com

  • 中图分类号: R711.75;R737.31

Correlation between Epithelial Cell Related Genes and Prognosis of Patients with Ovarian Cancer based on Single Cell Sequencing

  • 摘要:   目的  基于上皮细胞标志物的表达构建1个多基因风险评分来评估卵巢癌患者的预后。  方法  对卵巢癌单细胞测序数据进行降维、聚类,识别上皮细胞标记物、恶性和非恶性标记物。使用回归分析筛选与预后相关的上皮细胞标记基因以构建风险评分模型,基于风险评分将患者分为高、低风险(H.Risk、L.Risk)组,用于预测卵巢癌患者的预后。  结果  构建了1个4个基因(EPCAM、CLDN4、CXCR4和TIMP3)的风险评分模型。生存分析表明在试验队列和验证队列中H.Risk组患者的OS均比L.Risk组患者差(P < 0.05)。途径富集分析显示,高、低风险组之间的差异基因与免疫抑制和恶性进展相关,包括细胞粘附、细胞外基质、神经活性配体-受体相互作用、钙信号通路、转化生长因子-β等。  结论  通过bulkRNA-seq和scRNA-seq数据的综合分析提出了1种基于上皮细胞亚群标记基因的风险评分模型,并可能为卵巢癌患者提供潜在的治疗靶点。
  • 图  1  卵巢癌的单细胞RNA测序分析

    A:线粒体基因与测序深度的关系(线粒体基因含量为0)和测序深度与基因数目呈正相关关系;B:线粒体基因含量;C:前1500个高变基因;D:前15个主成分。

    Figure  1.  Single cell RNA sequencing analysis of ovarian cancer

    图  2  单细胞数据的降维和聚类

    A:细胞的T-SNE图,显示细胞群;B:细胞注释的T-SNE图;C:细胞群标记物的相对表达热图(仅显示前10名)。

    Figure  2.  Dimensionality reduction and clustering of single cell data

    图  3  单细胞CNV分析及预后基因筛选

    A:拷贝组变异分析热图;B:MECRGs在良恶性患者中的表达热图;C:基因表达和OS之间单变量Cox回归分析结果的森林图。

    Figure  3.  Single cell CNV analysis and prognosis gene screening

    图  4  TCGA-OV中的多基因风险评分构建与验证

    A~B:Lasso-cox回归分析;C:TCGA队列中OS状态、OS和风险评分的分布、模型基因的表达热图;D:TCGA-OV队列高、低风险组患者OS的K-M曲线(P < 0.001);E:TCGA-OV队列ROC曲线;F:GEO队列中OS状态、OS和风险评分的分布、模型基因的表达热图;G:GEO队列高低风险组患者OS的K-M曲线(P < 0.001);H:GEO队列ROC曲线。

    Figure  4.  Construction and verification of polygene risk score in 4TCGA-OV

    图  5  路径分析的主要结果

    A~B:在高低风险组中差异基因的GO和KEGG富集分析。

    Figure  5.  Main results of path analysis

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  • 收稿日期:  2024-01-04
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