Extracellular vesicles derived from Wharton's Jelly mesenchymal stem cells inhibit the tumor environment via the miR-125b/HIF1α signaling pathway.

Triple negative breast cancer (TNBC) is associated with worse outcomes and results in high mortality; therefore, great efforts are required to find effective treatment. In the present study, we suggested a novel strategy to treat TNBC using mesenchymal stem cell (MSC)-derived extracellular vesicles (EV) to transform the behaviors and cellular communication of TNBC cells (BCC) with other non-cancer cells related to tumorigenesis and metastasis. Our data showed that, BCC after being internalized with EV derived from Wharton's Jelly MSC (WJ-EV) showed the impaired proliferation, stemness properties, tumorigenesis and metastasis under hypoxic conditions. Moreover, these inhibitory effects may be involved in the transfer of miRNA-125b from WJ-EV to BCC, which downregulated the expression of HIF1α and target genes related to proliferation, epithelial-mesenchymal transition, and angiogenesis. Of note, WJ-EV-internalized BCC (wBCC) showed transformed behaviors that attenuated the in vivo development and metastatic ability of TNBC, the angiogenic abilities of endothelial cells and endothelial progenitor cells and the generation of cancer-associated fibroblasts from MSC. Furthermore, wBCC generated a new EV with modified functions that contributed to the inhibitory effects on tumorigenesis and metastasis of TNBC. Taken together, our findings suggested that WJ-EV treatment is a promising therapy that results in the generation of wBCC to interrupt the cellular crosstalk in the tumor environment and inhibit the tumor progression in TNBC.

Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.