The Second People's Hospital of Hunan Province

Osteoporosis (OP) is a metabolic disease caused by osteogenesis and bone resorption disorders. Promyelocytic leukemia protein (PML) was a vital regulator of cellular functions. However, the function of PML in OP remains unknown. Our research aimed to illustrate the molecular mechanism of PML in bone marrow mesenchymal stem cells (BMSCs) osteogenic differentiation. The BMSCs were identified by using flow cytometry analysis. The osteoblast differentiation ability of BMSCs was assessed through using alkaline phosphatase and Alizarin red S stainings. The relationship between hypoxia-inducible factor-1α (HIF1α) and superoxide dismutase 3 (SOD3) were confirmed by using chromatin immunoprecipitation and dual-luciferase reporter assays. The binding association between PML and hypoxia-inducible factor 1α inhibitor (HIF1AN) proteins was verified by using co-immunoprecipitation assay and immunofluorescence staining. Western blot was used for protein detection. PML was up-regulated in osteogenic differentiation of BMSCs. Functionally, PML negatively regulated HIF1AN expression by enhancing HIF1AN ubiquitination degradation. PML knockdown or HIF1AN up-regulation suppressed the osteogenic differentiation of BMSCs. Furthermore, HIF1α directly bound to the SOD3 promoter region. PML or SOD3 overexpression remarkably promoted the BMSCs osteoblast differentiation under osteogenic medium, which was reversed by LY294002. PML acts as a significant regulator in the BMSCs osteogenic differentiation by regulating the HIF1AN/HIF1α/SOD3 axis and phosphatidylinositol 3 kinase/ protein kinase B pathway.

Background: Identifying biomarkers that predict immunotherapy efficacy and discovering new targets for combination therapies are critical elements for improving the prognosis of bladder cancer (BLCA) patients. Methods: Firstly, we explored the expression patterns of TBX3 in normal and pan-cancer tissues and the correlation between TBX3 and the immune microenvironment using data from multiple public databases. Then, we combined various techniques, including bulk RNA sequencing, single-cell RNA sequencing, high-throughput cytokine arrays, functional experiments, ProcartaPlex multiplex immunoassays and TissueFAXS panoramic tissue quantification assays, to demonstrate that TBX3 shapes an immunosuppressive tumor microenvironment (TME) in BLCA. Results: We identified TBX3 as a key factor associated with the immunosuppressive microenvironment in BLCA through a systematic multi-omics analysis. We found that TBX3 is primarily expressed in malignant cells, where TBX3^high tumor cells increase the secretion of TGFβ1, which promotes the infiltration of cancer-associated fibroblasts (CAFs), thereby forming an immunosuppressive microenvironment. We further demonstrated that TBX3 enhances TGFβ1 expression by binding to the TGFβ1 promoter, and blocking TGFβ1 counteracts the immunosuppressive effects of TBX3. Moreover, TBX3 reduced the cancer-killing efficiency of CD8⁺ T cells by decreasing the proportion of GZMB⁺ CD8⁺ T cells, and knocking down TBX3 combined with anti-PD-1 treatment increased CD8⁺ T cell infiltration and reduced CAFs in vivo. We also validated the inverse relationship between TBX3⁺ malignant cells and CD8⁺ T cells and the positive relationship with CAFs in tissue microarrays. Lastly, we found that TBX3 predicted immunotherapy efficacy in our real-world immunotherapy cohort and multiple public cohorts. Conclusion: In summary, TBX3 promotes BLCA progression and immunotherapy resistance by inducing an immunosuppressive microenvironment, and targeting TBX3 could enhance the efficacy of immunotherapy for BLCA.

This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD).

Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes.

A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers.

Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.