Department of Gynecology, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Front Endocrinol (Lausanne). 2023 Jun 19;14:1196094. doi: 10.3389/fendo.2023.1196094. eCollection 2023.
Ovarian cancer is the most lethal gynaecological malignancy, and serous ovarian cancer (SOC) is one of the more important pathological subtypes. Previous studies have reported a significant association of epithelial tomesenchymal transition (EMT) with invasive metastasis and immune modulation of SOC, however, there is a lack of prognostic and immune infiltration biomarkers reported for SOC based on EMT.
Gene expression data for ovarian cancer and corresponding patient clinical data were collected from the TCGA database and the GEO database, and cell type annotation and spatial expression analysis were performed on single cell sequencing data from the GEO database. To understand the cell type distribution of EMT-related genes in SOC single-cell data and the enrichment relationships of biological pathways and tumour functions. In addition, GO functional annotation analysis and KEGG pathway enrichment analysis were performed on mRNAs predominantly expressed with EMT to predict the biological function of EMT in ovarian cancer. The major differential genes of EMT were screened to construct a prognostic risk prediction model for SOC patients. Data from 173 SOC patient samples obtained from the GSE53963 database were used to validate the prognostic risk prediction model for ovarian cancer. Here we also analysed the direct association between SOC immune infiltration and immune cell modulation and EMT risk score. and calculate drug sensitivity scores in the GDSC database.In addition, we assessed the specific relationship between GAS1 gene and SOC cell lines.
Single cell transcriptome analysis in the GEO database annotated the major cell types of SOC samples, including: T cell, Myeloid, Epithelial cell, Fibroblast, Endothelial cell, and Bcell. cellchat revealed several cell type interactions that were shown to be associated with EMT-mediated SOC invasion and metastasis. A prognostic stratification model for SOC was constructed based on EMT-related differential genes, and the Kapan-Meier test showed that this biomarker had significant prognostic stratification value for several independent SOC databases. The EMT risk score has good stratification and identification properties for drug sensitivity in the GDSC database.
This study constructed a prognostic stratification biomarker based on EMT-related risk genes for immune infiltration mechanisms and drug sensitivity analysis studies in SOC. This lays the foundation for in-depth clinical studies on the role of EMT in immune regulation and related pathway alterations in SOC. It is also hoped to provide effective potential solutions for early diagnosis and clinical treatment of ovarian cancer.
卵巢癌是最致命的妇科恶性肿瘤，浆液性卵巢癌（SOC）是其中一个重要的病理亚型。先前的研究表明上皮间质转化（EMT）与 SOC 的侵袭转移和免疫调节密切相关，然而，目前尚无基于 EMT 的 SOC 预后和免疫浸润生物标志物的报道。
从 TCGA 数据库和 GEO 数据库中收集卵巢癌的基因表达数据和相应的患者临床数据，并对 GEO 数据库中的单细胞测序数据进行细胞类型注释和空间表达分析。为了了解 EMT 相关基因在 SOC 单细胞数据中的细胞类型分布以及生物途径和肿瘤功能的富集关系。此外，对 EMT 主要表达的 mRNAs 进行 GO 功能注释分析和 KEGG 通路富集分析，以预测 EMT 在卵巢癌中的生物学功能。筛选 EMT 的主要差异基因，构建 SOC 患者的预后风险预测模型。使用从 GSE53963 数据库中获得的 173 个 SOC 患者样本数据对该模型进行验证。在这里，我们还分析了 SOC 免疫浸润与免疫细胞调节和 EMT 风险评分之间的直接关联，并在 GDSC 数据库中计算了药物敏感性评分。此外，我们评估了 GAS1 基因与 SOC 细胞系之间的特定关系。
GEO 数据库中的单细胞转录组分析注释了 SOC 样本的主要细胞类型，包括：T 细胞、髓样细胞、上皮细胞、成纤维细胞、内皮细胞和 B 细胞。cellchat 揭示了几种细胞类型的相互作用，这些相互作用与 EMT 介导的 SOC 侵袭和转移有关。基于 EMT 相关差异基因构建 SOC 的预后分层模型，Kapan-Meier 检验表明该标志物对几个独立的 SOC 数据库具有显著的预后分层价值。EMT 风险评分在 GDSC 数据库中对药物敏感性具有良好的分层和识别特性。
本研究构建了一个基于 EMT 相关风险基因的 SOC 免疫浸润机制和药物敏感性分析的预后分层生物标志物。这为 EMT 在 SOC 免疫调节和相关通路改变中的作用的深入临床研究奠定了基础。也希望为卵巢癌的早期诊断和临床治疗提供有效的潜在解决方案。
