Single-cell RNA-seq highlights a specific carcinoembryonic cluster in ovarian cancer

Abstract

Expounding the heterogeneity for ovarian cancer (OC) with the cognition in developmental biology might be helpful to search for robust prognostic markers and effective treatments. In the present study, we employed single-cell RNA-seq with ovarian cancers, normal ovary, and embryo tissue to explore their heterogeneity. Then the differentiation process of clusters was explored; the pivotal cluster and markers were identified. Furthermore, the consensus clustering algorithm was used to explore the different clinical phenotypes in OC. At last, a prognostic model was construct and used to assess the prognosis for OCs. As a result, eight diverse clusters were identified, and the similarity existed in some clusters between embryo and tumours based on their gene expression. Meaningfully, a subtype of malignant epithelial cluster, PEG10+ EME, was associated with poor survival and was an intermediate stage of embryo to tumour. PEG10 was a CSC marker and might influence CSC self-renewal and promote cisplatin resistance via NOTCH pathway. Utilising specific gene profiles of PEG10+ EME based on public data sets, four phenotypes with different survival and clinical response to anti-PD-1/PD-L1 immunotherapy were identified. These insights allowed for the investigation of single-cell transcriptome of OCs and embryo, which advanced our current understanding of OC pathogenesis and resulted in promising therapeutic strategies.

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data

“…After principal component analysis (PCA), the 24 most powerful PCs were used for t-distributed stochastic neighbor embedding (t-SNE) analysis for dimension reduction. Subsequently, cells were divided into fourteen clusters with a resolution of 0.5 by KNN analysis and the “FindClusters” method, and cell types were subsequently annotated by specific cell markers as previously described 30 – 32 . Finally, cell–cell communications among the cell types were investigated by the “CellChat” package 33 .…”

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

“…They found that a pivotal malignant epithelial cluster, PEG10 + embryonic malignant epithelial (EME) cells, belonging to an intermediate stage of the embryo-to-tumor process, was associated with poor prognosis. Moreover, cell-specific marker genes of PEG10 + EME could well discriminate which patients would benefit from immunotherapy [ 40 ]. In the case of acute myeloid leukemia (AML) noted for ITH, researchers combined scRNA-seq and genotyping to characterize AML ecosystems and developed RF-based ML classifiers to predict AML cell types and distinguish malignant and normal cells in AML tumors.…”

Integration of Genomic Profiling and Organoid Development in Precision Oncology

“…JSI-124 (cucurbitacin I), which targets the STAT3 signaling pathway, increases the cancer cell death rate and extent of disease response in vivo [ 47 , 48 ], suggesting that the JAK/STAT pathway could be a promising target for HGSOC treatment. In another recent study, scRNA-seq was performed using ovarian cancer, normal ovarian, and embryonic tissues to investigate heterogeneity [ 49 ]. This study showed a comparison of gene expression profiles between ovarian cancer and embryonic tissues.…”

“…This study showed a comparison of gene expression profiles between ovarian cancer and embryonic tissues. Interestingly, PEG10+ clusters have been identified in both ovarian cancer and embryonic tissues, which modulate cancer stem cell activity and drug resistance in ovarian cancer cells [ 49 ]. This study suggested that the PEG10-mediated cancer embryo population could be a therapeutic target for ovarian cancer.…”