Development of a Risk Score Model for Osteosarcoma Based on DNA Methylation-Driven Differentially Expressed Genes

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Identification of the shared gene MXD3 signatures and biological mechanism in patients with hip pain and prostate cancer

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein-protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein-protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

Background

Despite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.

Methods

Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.

Results

The malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.

Conclusions

Overall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.