Single-cell multi-omics analysis reveals dysfunctional Wnt signaling of spermatogonia in non-obstructive azoospermia

BackgroundNon-obstructive azoospermia (NOA) is the most severe type that leads to 1% of male infertility. Wnt signaling governs normal sperm maturation. However, the role of Wnt signaling in spermatogonia in NOA has incompletely been uncovered, and upstream molecules regulating Wnt signaling remain unclear.MethodsBulk RNA sequencing (RNA-seq) of NOA was used to identify the hub gene module in NOA utilizing weighted gene co-expression network analyses (WGCNAs). Single-cell RNA sequencing (scRNA-seq) of NOA was employed to explore dysfunctional signaling pathways in the specific cell type with gene sets of signaling pathways. Single-cell regulatory network inference and clustering (pySCENIC) for Python analysis was applied to speculate putative transcription factors in spermatogonia. Moreover, single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) determined the regulated genes of these transcription factors. Finally, spatial transcriptomic data were used to analyze cell type and Wnt signaling spatial distribution.ResultsThe Wnt signaling pathway was demonstrated to be enriched in the hub gene module of NOA by bulk RNA-seq. Then, scRNA-seq data revealed the downregulated activity and dysfunction of Wnt signaling of spermatogonia in NOA samples. Conjoint analyses of the pySCENIC algorithm and scATAC-seq data indicated that three transcription factors (CTCF, AR, and ARNTL) were related to the activities of Wnt signaling in NOA. Eventually, spatial expression localization of Wnt signaling was identified to be in accordance with the distribution patterns of spermatogonia, Sertoli cells, and Leydig cells.ConclusionIn conclusion, we identified that downregulated Wnt signaling of spermatogonia in NOA and three transcription factors (CTCF, AR, and ARNTL) may be involved in this dysfunctional Wnt signaling. These findings provide new mechanisms for NOA and new therapeutic targets for NOA patients

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets

Introduction:Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies.Methods:Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified.Results:Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified.Conclusion:In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention