MLN4924, also known as pevonedistat, is the first-in-class inhibitor of NEDD8-activating enzyme, which blocks the entire neddylation modification of proteins. Previous preclinical studies and current clinical trials have been exclusively focused on its anticancer property. Unexpectedly, we show here, to our knowledge for the first time, that MLN4924, when applied at nanomolar concentrations, significantly stimulates in vitro tumor sphere formation and in vivo tumorigenesis and differentiation of human cancer cells and mouse embryonic stem cells. These stimulatory effects are attributable to (i) c-MYC accumulation via blocking its degradation and (ii) continued activation of EGFR (epidermal growth factor receptor) and its downstream pathways, including PI3K/AKT/mammalian target of rapamycin and RAS/RAF/MEK/ERK, via inducing EGFR dimerization. Finally, MLN4924 accelerates EGF-mediated skin wound healing in mouse and stimulates cell migration in an in vitro culture setting. Taking these data together, our study reveals that neddylation modification could regulate stem cell proliferation and differentiation and that a low dose of MLN4924 might have a therapeutic value for stem cell therapy and tissue regeneration. Significance MLN4924, a potent small-molecule inhibitor of NEDD8-activating enzyme, blocks cullin-RING ligase activity through inhibiting cullin neddylation. MLN4924 is widely used in both preclinical and clinical settings for an anticancer application. We report here an unexpected finding: MLN4924 at nanomolar concentration stimulates stem cell proliferation, self-renewal, and differentiation in both tumor and normal stem cell models and promotes skin wound healing in a mouse model and cell migration in vitro. Mechanistic studies revealed that MLN4924 causes c-MYC accumulation and promotes EGFR (epidermal growth factor receptor) dimerization to activate the EGFR signaling pathway. Our study raises a concern in anticancer application of MLN4924, but at the same time provides an opportunity for future development of MLN4924 as an agent for stem cell therapy and tissue regeneration.

Background: Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods: Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results: An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions: In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.

A number of culture-dependent and -independent studies have reported that the number and significance of bacterial species in semen may have been underestimated. The aim of the present study was to profile the seminal microbiome in patients with obstructive or non‑obstructive azoospermia. A high‑throughput sequencing method was adopted to sequence genomic DNA extracted from the semen of healthy people (C group), patients with obstructive azoospermia (OA group) and patients with non‑obstructive azoospermia (NOA group). The results revealed that Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria species comprised the majority of bacteria in the C (98.14%), OA (98.26%) and NOA (90.96%) groups. Patients in the OA and NOA groups exhibited an increase in Bacteroidetes and Firmicutes, whereas the number of Proteobacteria and Actinobacteria were decreased compared with the C group. A total of 398 common operational taxonomic units were identified, of which 27 belonged to the genus Lactobacillus. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States analysis indicated that the pathogenic species and reduced biodiversity in the semen of patients with azoospermia may result in an increased risk of metabolic, infectious and immune diseases. In the present study, the seminal microbiome of patients with obstructive or non‑obstructive azoospermia was explored, which may be useful for developing novel treatments against azoospermia as well as for its diagnosis.

Background: The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated. Methods: We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature. Results: A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8⁺ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group. Conclusions: We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.