Ding Bo, Ye Zheng, Yin Han, Hong Xin-Yi, Feng Song-Wei, Xu Jing-Yun, Shen Yang
Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
Heliyon. 2024 Mar 19;10(6):e27873. doi: 10.1016/j.heliyon.2024.e27873. eCollection 2024 Mar 30.
Ovarian cancer, as a highly malignant tumor, features the critical involvement of tumor-associated fibroblasts in the ovarian cancer tissue microenvironment. However, due to the apparent heterogeneity within fibroblast subpopulations, the specific functions of these subpopulations in the ovarian cancer tissue microenvironment remain insufficiently elucidated.
In this study, we integrated single-cell sequencing data from 32 ovarian cancer samples derived from four distinct cohorts and 3226 bulk RNA-seq data from GEO and TCGA-OV cohorts. Utilizing computational frameworks such as Seurat, Monocle 2, Cellchat, and others, we analyzed the characteristics of the ovarian cancer tissue microenvironment, focusing particularly on fibroblast subpopulations and their differentiation trajectories. Employing the CIBERSORTX computational framework, we assessed various cellular components within the ovarian cancer tissue microenvironment and evaluated their associations with ovarian cancer prognosis. Additionally, we conducted Mendelian randomization analysis based on -eQTL to investigate causal relationships between gene expression and ovarian cancer.
Through integrative analysis, we identified 13 major cell types present in ovarian cancer tissues, including CD8 T cells, malignant cells, and fibroblasts. Analysis of the tumor microenvironment (TME) cell proportions revealed a significant increase in the proportion of CD8 T cells and CD4 T cells in tumor tissues compared to normal tissues, while fibroblasts predominated in normal tissues. Further subgroup analysis of fibroblasts identified seven subgroups, with the MMP11+Fib subgroup showing the highest activity in the TGFβ signaling pathway. Single-cell analysis suggested that oxidative phosphorylation could be a key pathway driving fibroblast differentiation, and the ATRNL1+KCN + Fib subgroup exhibited chromosomal copy number variations. Prognostic analysis using a large sample size indicated that high infiltration of MMP11+ fibroblasts was associated with poor prognosis in ovarian cancer. SMR analysis identified 132 fibroblast differentiation-related genes, which were linked to pathways such as platinum drug resistance.
In the context of ovarian cancer, fibroblasts expressing MMP11 emerge as the primary drivers of the TGF-beta signaling pathway. Their presence correlates with an increased risk of adverse ovarian prognoses. Additionally, the genetic regulation governing the differentiation of fibroblasts associated with ovarian cancer correlates with the emergence of drug resistance.
卵巢癌作为一种高度恶性的肿瘤，其肿瘤相关成纤维细胞在卵巢癌组织微环境中起着关键作用。然而，由于成纤维细胞亚群内存在明显的异质性，这些亚群在卵巢癌组织微环境中的具体功能仍未得到充分阐明。
在本研究中，我们整合了来自四个不同队列的32个卵巢癌样本的单细胞测序数据以及来自GEO和TCGA - OV队列的3226个批量RNA测序数据。利用诸如Seurat、Monocle 2、Cellchat等计算框架，我们分析了卵巢癌组织微环境的特征，特别关注成纤维细胞亚群及其分化轨迹。采用CIBERSORTX计算框架，我们评估了卵巢癌组织微环境中的各种细胞成分，并评估了它们与卵巢癌预后的关联。此外，我们基于-eQTL进行了孟德尔随机化分析，以研究基因表达与卵巢癌之间的因果关系。
通过综合分析，我们确定了卵巢癌组织中存在的13种主要细胞类型，包括CD8 T细胞、恶性细胞和成纤维细胞。肿瘤微环境（TME）细胞比例分析显示，与正常组织相比，肿瘤组织中CD8 T细胞和CD4 T细胞的比例显著增加，而成纤维细胞在正常组织中占主导地位。对成纤维细胞的进一步亚组分析确定了七个亚组，其中MMP11 + Fib亚组在TGFβ信号通路中表现出最高活性。单细胞分析表明，氧化磷酸化可能是驱动成纤维细胞分化的关键途径，而ATRNL1 + KCN + Fib亚组表现出染色体拷贝数变异。使用大样本量的预后分析表明，MMP11 +成纤维细胞的高浸润与卵巢癌的不良预后相关。SMR分析确定了132个与成纤维细胞分化相关的基因，这些基因与铂类药物耐药等途径有关。
在卵巢癌背景下，表达MMP11的成纤维细胞成为TGF-β信号通路的主要驱动因素。它们的存在与卵巢不良预后风险增加相关。此外，与卵巢癌相关的成纤维细胞分化的遗传调控与耐药性的出现相关。
