Ovarian cancer is one of the most lethal gynecological tumors worldwide.
However, the systematic investigation of molecular and cellular mechanism across all clinical stages at the single-cell level remain poorly characterized.
Here, we collected and re-processed the 505,102 single-cell transcriptomic data of 84 ovarian tumors patients across all the clinical stages from 16 publicly available studies.
Integrative analysis reveals that heterogeneous cellular compartments in ovarian cancer.
Epithelial compartments showed the clinical stage-related epithelial subtype features with functional differences.
Immune compartments showed the distinct T cell subtype lineage trajectory and Tregs and CD8+ exhausted T cells were identified to be significantly increased after stage IC2.
We also identified antigen-presenting cancer-associated fibroblasts (CAFs) and found that the extracellular matrix (ECM)
function was enriched in myofibroblastic CAFs (myCAFs) related to tumor progression at stage IC2.
Furthermore, our study reveals receptor-ligand interactions within and across cellular compartments as potential
mechanisms mediating immune immunosuppression, exemplified by T cells cross talk with epithelial, fibroblast,
endothelial and other cell types via NECTIN2-TIGIT. We have also identified a panel of genes that can be utilized for
early detection and diagnosis of ovarian cancer. The findings highlight the cellular compartments and functional
features of ovarian cancer, shedding light on the molecular mechanisms involved in stage IC2 and potentially
informing the potential therapeutic strategies for ovarian cancer.
Please cite the human ovarian cancer Single Cell Database as:
Cell information / gene expression violin plot / box plot
In this tab, users can visualise the gene expression or continuous cell information
(e.g. Number of UMIs / module score) across groups of cells (e.g. libary / clusters).
Proportion / cell numbers across different cell information
In this tab, users can visualise the composition of single cells based on one discrete
cell information across another discrete cell information.
Usage examples include the library or cellcycle composition across clusters.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by categorical cell information (e.g. library / cluster).
The normalised expression are averaged, log-transformed and then plotted.