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Background <p>Osteosarcoma (OS) is a malignant primary bone tumor developing from primitive mesenchymal cells. Glycosylation is important in the adhesion, metastasis and transformation of cancer cells. Nevertheless, the investigation of glycosylation-related genes (GRGs) in OS has been infrequently described.</p> Methods <p>The OS-related datasets (GSE36001, TARGET-OS and GSE21257), scRNA-seq dataset (GSE152048) and 582 GRGs were contained in this research. The identification of candidate genes was conducted by differential expression analysis and weighted gene co-expression network analysis (WGCNA). The prognostic genes filtering method was univariate cox regression, and risk score were generated by the implementation of LASSO. Samples comprising the TARGET-OS and GSE21257 datasets were categorized into high- and low-risk according to the risk score. Through applying various cox regression analysis, independent prognostic variables were identified. Furthermore, functional enrichment analysis and immune microenvironment analysis were performed. Finally, the key cells were identified, and the cell-cell communication and pseudo-time analyses of key cells were conducted.</p> Results <p>The analysis of scRNA-seq data identified cell types in OS and detected the expression of prognostic genes. Seven glycosylation-related genes (<i>DCN</i>, <i>RENBP</i>, <i>UAP1</i>, <i>B4GALNT1</i>, <i>CCDC115</i>, <i>TUBA1A</i> and <i>B3GALT4</i>) were identified as prognostic genes. The low-risk group exhibited a comparatively elevated rate of survival and 1-year OS patients had the highest survival rate. Immune and apoptosis-related signaling pathways were activated in the high-risk group, indicating a worse prognosis. The analysis of scRNA-seq data revealed DCN and TUBA1A were high- expressed in osteoblastic cells. Thus, the osteoblastic cells were identified as key cells. Osteoblastic cells exhibited frequent interactions with other annotated cells. Pseudo-time analysis elucidated the differentiation direction of key cells from left to right.</p> Conclusions <p>we identified seven prognostic genes for OS. The prognostic models were constructed, and underlying molecular mechanisms were explored based on diagnostic genes, which served as a benchmark for OS prognosis and treatment.</p>

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Transcriptome combined with single-cell data to construct a prognostic model for glycosylation-related genes in osteosarcoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • Ning Ding,
  • Jinlong Li,
  • Songbo Shi,
  • Jizu Wang,
  • Yinliang Ding,
  • Qingshan Yang

摘要

Background

Osteosarcoma (OS) is a malignant primary bone tumor developing from primitive mesenchymal cells. Glycosylation is important in the adhesion, metastasis and transformation of cancer cells. Nevertheless, the investigation of glycosylation-related genes (GRGs) in OS has been infrequently described.

Methods

The OS-related datasets (GSE36001, TARGET-OS and GSE21257), scRNA-seq dataset (GSE152048) and 582 GRGs were contained in this research. The identification of candidate genes was conducted by differential expression analysis and weighted gene co-expression network analysis (WGCNA). The prognostic genes filtering method was univariate cox regression, and risk score were generated by the implementation of LASSO. Samples comprising the TARGET-OS and GSE21257 datasets were categorized into high- and low-risk according to the risk score. Through applying various cox regression analysis, independent prognostic variables were identified. Furthermore, functional enrichment analysis and immune microenvironment analysis were performed. Finally, the key cells were identified, and the cell-cell communication and pseudo-time analyses of key cells were conducted.

Results

The analysis of scRNA-seq data identified cell types in OS and detected the expression of prognostic genes. Seven glycosylation-related genes (DCN, RENBP, UAP1, B4GALNT1, CCDC115, TUBA1A and B3GALT4) were identified as prognostic genes. The low-risk group exhibited a comparatively elevated rate of survival and 1-year OS patients had the highest survival rate. Immune and apoptosis-related signaling pathways were activated in the high-risk group, indicating a worse prognosis. The analysis of scRNA-seq data revealed DCN and TUBA1A were high- expressed in osteoblastic cells. Thus, the osteoblastic cells were identified as key cells. Osteoblastic cells exhibited frequent interactions with other annotated cells. Pseudo-time analysis elucidated the differentiation direction of key cells from left to right.

Conclusions

we identified seven prognostic genes for OS. The prognostic models were constructed, and underlying molecular mechanisms were explored based on diagnostic genes, which served as a benchmark for OS prognosis and treatment.