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Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma
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Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma
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Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma
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Journal Article
Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma
2023
Overview
Osteoclasts (OCs) and regulatory CD4+ T cells (CD4+Tregs) are important components in the tumor microenvironment (TME) of osteosarcoma. In this study, we collected six osteosarcoma samples from our previous study (GSE162454). We also integrated a public database (GSE152048), which included single cell sequencing data of 11 osteosarcoma patients. We obtained 138,192 cells and then successfully identified OCs and CD4+Tregs. Based on the interaction gene set between OCs and CD4+Tregs, patients from GSE21257 were distinguished into two clusters by consensus clustering analysis. Both the tumor immune microenvironment and survival prognosis between the two clusters were significantly different. Subsequently, five model genes were identified by protein–protein interaction network based on differentially upregulated genes of cluster 2. Quantitative RT‐PCR was used to detect their expression in human osteoblast and osteosarcoma cells. A prognostic model was successfully established using these five genes. Kaplan–Meier survival analysis found that patients in the high‐risk group had worse survival (p = 0.029). Therefore, our study first found that cell–cell communication between OCs and CD4+Tregs significantly alters TME and is connected to poor prognosis of OS. The model we constructed can accurately predict prognosis for osteosarcoma patients.
This research found that cell–cell communication between osteoclasts and regulatory CD4+ T cells significantly alter the tumor microenvironment and is connected to poor prognosis of osteosarcoma. The model can accurately predict patient prognosis.
