Liu Xiangxiang, Ping Guoqiang, Ji Dongze, Wen Zhifa, Chen Yajun
Department of Clinical Laboratory, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing, Jiangsu, China.
Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Biomed Eng Comput Biol. 2024 Aug 30;15:11795972241274024. doi: 10.1177/11795972241274024. eCollection 2024.
Cancer-associated fibroblasts (CAFs) play critical roles in the metastasis and therapeutic response of high-grade serous ovarian cancer (HGSC). Our study intended to select HGSC patients with unfavorable prognoses and therapeutic responses based on CAF-enriched prognostic genes. The bulk RNA and single-cell RNA sequencing (scRNA-seq) data of tumor tissues were collected from the TCGA and GEO databases. The infiltrated levels of immune and stromal cells were estimated by multiple immune deconvolution algorithms and verified through immunohistochemical analysis. The univariate Cox regression analyses were used to identify prognostic genes. Gene Set Enrichment Analysis (GSEA) was conducted to annotate enriched gene sets. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore potential alternative drugs. We found the infiltered levels of CAFs were remarkedly elevated in advanced and metastatic HGSC tissues and identified hundreds of genes specifically enriched in CAFs. Then we selected 6 CAF-enriched prognostic genes based on which HGSC patients were reclassified into 2 subclusters with discrepancy prognoses. Further analysis revealed that the HGSC patients in cluster-2 tended to undergo poor responses to traditional chemotherapy and immunotherapy. Subsequently, we selected 24 novel potential therapeutic drugs for cluster-2 HGSC patients. Moreover, we discovered a positive correlation of infiltrated levels between CAFs and monocytes/macrophages in HGSC tissues. Collectively, our study successfully reclassified HGSC patients into 2 different subgroups that have discrepancy prognoses and responses to current therapeutic methods.
癌症相关成纤维细胞（CAFs）在高级别浆液性卵巢癌（HGSC）的转移和治疗反应中起关键作用。我们的研究旨在基于富含CAF的预后基因选择预后不良和治疗反应不佳的HGSC患者。从TCGA和GEO数据库收集肿瘤组织的批量RNA和单细胞RNA测序（scRNA-seq）数据。通过多种免疫反卷积算法估计免疫细胞和基质细胞的浸润水平，并通过免疫组织化学分析进行验证。使用单变量Cox回归分析来鉴定预后基因。进行基因集富集分析（GSEA）以注释富集的基因集。利用癌症药物敏感性基因组学（GDSC）数据库探索潜在的替代药物。我们发现CAFs的浸润水平在晚期和转移性HGSC组织中显著升高，并鉴定出数百个在CAFs中特异性富集的基因。然后我们选择了6个富含CAF的预后基因，据此将HGSC患者重新分类为2个预后不同的亚组。进一步分析表明，第2组中的HGSC患者对传统化疗和免疫治疗的反应往往较差。随后，我们为第2组HGSC患者选择了24种新的潜在治疗药物。此外，我们发现HGSC组织中CAFs与单核细胞/巨噬细胞的浸润水平呈正相关。总体而言，我们的研究成功地将HGSC患者重新分类为2个不同的亚组，它们对当前治疗方法的预后和反应存在差异。
