Bojia Peng

The hypoxic tumor microenvironment significantly impacts cellular behavior and intercellular communication, with extracellular vesicles (EVs) playing a crucial role in promoting angiogenesis, metastasis, and host immunosuppression, and presumed cancer progression and metastasis are closely associated with the aberrant surface N-glycan expression in EVs. We hypothesize that hypoxic tumors synthesize specific hypoxia-induced N-glycans in response to or as a consequence of hypoxia. This study utilized nano-LC–MS/MS to integrate quantitative proteomic and N-glycomic analyses of both cells and EVs derived from the MDA-MB-231 breast cancer cell line cultured under normoxic and hypoxic conditions. Whole N-glycome and proteome profiling revealed that hypoxia has an impact on the asparagine N-linked glycosylation patterns and on the glycolysis/gluconeogenesis proteins in cells in terms of altered N-glycosylation for their adaptation to low-oxygen conditions. Distinct N-glycan types, high-mannose glycans like Man3 and Man9, were highly abundant in the hypoxic cells. On the other hand, alterations in the sialylation and fucosylation patterns were observed in the hypoxic cells. Furthermore, hypoxia-induced EVs exhibit a signature consisting of mono-antennary structures and specific N-glycans (H4N3F1S2, H3N3F1S0, and H7N4F3S2; H8N4F1S0 and H8N6F1S2), which are significantly associated with poor prognoses for breast tumors, presumably altering the interactions within the tumor microenvironment to promote tumorigenesis and metastasis. Our findings provide an overview of the N-glycan profiles, particularly under hypoxic conditions, and offer insights into the potential biomarkers for tracking tumor microenvironment dynamics and for developing precision medicine approaches in oncology.

The role of estrogen receptor β (ERβ) in bone health is closely associated with its function in vivo, and ERβ^−/− mice have been widely utilized to explore the related influences. In this study, ERβ^−/− female mice were established to investigate the differential expression of circular RNAs (circRNAs) by RNA-Sequencing (RNA-Seq). Among these circRNAs, mmu_circ_0011379 (named Circ-Spen) exhibited high expression in ERβ^−/− female mice. However, the precise mechanism by which Circ-Spen regulates bone health remained unclear. This study identified Circ-Spen as a positive regulator of mouse bone marrow mesenchymal stem cell (mBMSC) viability. The expression of Circ-Spen was markedly increased in ERβ^−/− mice femurs tested by RT-qPCR. Moreover, Circ-Spen exhibited an enhanced expression during the bone formation process of mBMSCs. Qualitative experiments also demonstrated that Circ-Spen possessed a circular structure and was localized within the nucleus of mBMSCs. Functionally, it inhibited apoptosis via caspase-3, BCL-2, and BAX, while also promoting autophagy through BECN1 and P62 in mBMSCs tested by MTT assays, transmission electron microscopy (TEM), and Western blotting. These findings reveal the potential of targeting Circ-Spen as a promising therapeutic strategy for rejuvenating senescent mBMSCs and enhancing the efficiency of mBMSC transplantation, which lays the foundation for advancements in the field of bone therapy.

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.