Single-Cell Sequencing of Malignant Ascites Reveals Transcriptomic Remodeling of the Tumor Microenvironment during the Progression of Epithelial Ovarian Cancer.
Yiqun LiWenjie WangDanyun WangLiuchao ZhangXizhi WangJia HeLei CaoKang LiHongyu XiePublished in: Genes (2022)
Epithelial ovarian cancer (EOC) is the main cause of mortality among gynecological malignancies worldwide. Although patients with EOC undergo aggregate treatment, the prognosis is often poor. Peritoneal malignant ascites is a distinguishable clinical feature in EOC patients and plays a pivotal role in tumor progression and recurrence. The mechanisms of the tumor microenvironment (TME) in ascites in the regulation of tumor progression need to be explored. We comprehensively analyzed the transcriptomes of 4680 single cells from five EOC patients (three diagnostic samples and two recurrent samples) derived from Gene Expression Omnibus (GEO) databases. Batch effects between different samples were removed using an unsupervised deep embedding single-cell cluster algorithm. Subcluster analysis identified the different phenotypes of cells. The transition of a malignant cell state was confirmed using pseudotime analysis. The landscape of TME in malignant ascites was profiled during EOC progression. The transformation of epithelial cancer cells into mesenchymal cells was observed to lead to the emergence of related anti-chemotherapy and immune escape phenotypes. We found the activation of multiple biological pathways with the transition of tumor-associated macrophages and fibroblasts, and we identified the infiltration of CD4 + CD25 + T regulatory cells in recurrent samples. The cell adhesion molecules mediated by integrin might be associated with the formation of the tumorsphere. Our study provides novel insights into the remodeling of the TME heterogeneity in malignant ascites during EOC progression, which provides evidence for identifying novel therapeutic targets and promotes the development of ovarian cancer treatment.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- end stage renal disease
- gene expression
- cell cycle arrest
- cell free
- machine learning
- ejection fraction
- high throughput
- newly diagnosed
- cell adhesion
- poor prognosis
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- dna methylation
- cardiovascular disease
- transcription factor
- type diabetes
- stem cells
- cell death
- bone marrow
- long non coding rna
- neural network
- coronary artery disease
- cardiovascular events
- mesenchymal stem cells
- cell proliferation
- drug induced