Knockdown of TMPRSS3 inhibits cell proliferation, migration/invasion and induces apoptosis of glioma cells

Proteome profiling of salivary small extracellular vesicles in glioblastoma patients

BACKGROUND

Extracellular vesicles (EVs) play a critical role in intercellular communication under physiological and pathological conditions, including cancer. EVs cargo reflects their cell of origin, suggesting their utility as biomarkers. EVs are detected in several biofluids, and their ability to cross the blood-brain barrier has highlighted their potential as prognostic and diagnostic biomarkers in gliomas, including glioblastoma (GBM). Studies have demonstrated the potential clinical utility of plasma-derived EVs in glioma. However, little is known about the clinical utility of saliva-derived EVs in GBM.

METHODS

Small EVs were isolated from whole mouth saliva of GBM patients pre- and postoperatively. Isolation was performed using differential centrifugation and/or ultracentrifugation. EVs were characterized by concentration, size, morphology, and EVs cell-surface protein markers. Protein cargo in EVs was profiled using mass spectrometry.

RESULTS

There were no statistically significant differences in size and concentration of EVs derived from pre- and post GBM patients' saliva samples. A higher number of proteins were detected in preoperative samples compared to postoperative samples. The authors found four highly abundant proteins (aldolase A, 14-3-3 protein ε, enoyl CoA hydratase 1, and transmembrane protease serine 11B) in preoperative saliva samples from GBM patients with poor outcomes. Functional enrichment analysis of pre- and postoperative saliva samples showed significant enrichment of several pathways, including those related to the immune system, cell cycle and programmed cell death.

CONCLUSIONS

This study, for the first time, demonstrates the feasibility of isolating and characterizing small EVs from pre- and postoperative saliva samples from GBM patients. Preliminary findings encourage further large cohort validation studies on salivary small EVs to evaluate prognosis in GBM.

Selection of viral capsids and promoters affects the efficacy of rescue of Tmprss3-deficient cochlea

Adeno-associated virus (AAV)-mediated gene transfer has shown promise in rescuing mouse models of genetic hearing loss, but how viral capsid and promoter selection affects efficacy is poorly characterized. Here, we tested combinations of AAVs and promoters to deliver Tmprss3, mutations in which are associated with hearing loss in humans. Tmprss3tm1/tm1 mice display severe cochlear hair cell degeneration, loss of auditory brainstem responses, and delayed loss of spiral ganglion neurons. Under the ubiquitous CAG promoter and AAV-KP1 capsid, Tmprss3 overexpression caused striking cytotoxicity in vitro and in vivo and failed to rescue degeneration or dysfunction of the Tmprss3tm1/tm1 cochlea. Reducing the dosage or using AAV-DJ-CAG-Tmprss3 diminished cytotoxicity without rescue of the Tmprss3tm1/tm1 cochlea. Finally, the combination of AAV-KP1 capsid and the EF1α promoter prevented cytotoxicity and reduced hair cell degeneration, loss of spiral ganglion neurons, and improved hearing thresholds in Tmprss3tm1/tm1 mice. Together, our study illustrates toxicity of exogenous genes and factors governing rescue efficiency, and suggests that cochlear gene therapy likely requires precisely targeted transgene expression.

Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer

DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods. The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. Results. We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. Conclusion. We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.