Identification of immune-related tumor antigens and immune subtypes in osteosarcoma.

Purpose: The development of tumor vaccines has become a hot topic in immunotherapy for osteosarcoma (OS); however, more tumor antigens with stronger immunogenicity need to be identified. Methods: We downloaded six sets of gene expression profile data from online databases. The overexpressed genes were analyzed, intersected, and used to calculate the immune infiltration abundance in the TARGET OS dataset based on their expression matrix. Potential tumor antigen genes were identified based on whether they exhibited a high correlation with the antigen-presenting cells (APCs). A total of 1330 immune-related genes (IRGs) from the ImmPort website were retrieved based on their expression, and the Consensus Cluster method was used to obtain immune subtypes of the OS samples. Prognosis, immune microenvironment, and sensitivity to drugs were compared among the immune subtypes. Results: In total, 680 genes were overexpressed in at least two datasets, of which TREM2, TNFRSF12A, and THY1 were positively correlated with different APCs. Based on the expression matrix of 1330 IRGs in TARGET-OS, two immune subtypes, IS1 and IS2, were identified. The prognosis of the IS1 subtype was better than that of IS2, the expression of immune checkpoint (ICP)-related genes was higher in patients with the IS1 subtype, and immune cell infiltration and sensitivity to 16 drugs were generally higher in IS1 subtype patients. Conclusion: We identified three APC-correlated genes that can be considered to code for potential novel tumor antigens for OS vaccines. Two immune subtypes in patients with OS were identified to implement personalized treatments using mRNA vaccines.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration.

Background: Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease. Methods: The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed. Results: A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively. Conclusion: ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

Suppression of MicroRNA-383 Enhances Therapeutic Potential of Human Bone-Marrow-Derived Mesenchymal Stem Cells in Treating Spinal Cord Injury via GDNF.

BACKGROUND/AIMS: Transplantation of bone-marrow-derived mesenchymal stem cells (MSCs) has been used to treat spinal cord injury (SCI) to enhance tissue repair and neural cell regeneration. Glial cell line derived neurotrophic factor (GDNF) is an identified neural growth and survival factor. Here, we examined whether modification of GDNF levels in MSCs may further increase the potential of MSCs in promoting neural cell regeneration and subsequently the therapeutic outcome. METHODS: We examined the mRNA and protein levels of GDNF in human MSCs by RT-qPCR and Western blot, respectively. Bioinformatics analyses were done to predict microRNAs (miRNAs) that target GDNF in MSCs. The functional binding of miRNAs to GDNF mRNA was examined by a dual luciferase reporter assay. MSCs were transduced with adeno-associated virus (AAV) carrying null or antisense for miR-383 (as-miR-383), which were transplanted into nude rats that underwent SCI. The intact tissue, cavity volume, and recovery of locomotor activity were assessed. RESULTS: MSCs expressed very low GDNF protein, but surprisingly high levels of GDNF mRNA. Bioinformatics analyses showed that miR-383 inhibited protein translation of GDNF, through binding to the 3'-UTR of the GDNF mRNA. MSCs transduced with AAV-as-miR-383 further increased the intact tissue percentage, decreased cavity volume, and enhanced the recovery of locomotor activity in nude rats that underwent SCI, compared to MSCs. CONCLUSIONS: Suppression of miR-383 may increase the therapeutic potential of human bone-marrow-derived MSCs in treating SCI via augmentation of GDNF protein levels.