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Background <p>Amino acid metabolism is crucial for tumor cell proliferation and survival. However, its role in osteosarcoma (OS) remains incompletely characterized. The current study aimed to investigate the role of the amino acid metabolism-associated risk signature in prognostication and immune cell infiltration in osteosarcoma.</p> Methods <p>The clinical and transcriptome data of patients with OS were retrieved using the TARGET and GEO databases. Consensus clustering of amino acid metabolism-related genes (AAMRGs) stratified the osteosarcoma patients. Least absolute shrinkage and selection operator regression and Cox analysis were performed to build prognostic models. The expression levels of genes comprising the risk model were validated in vitro. Subsequently, the model was validated in an external cohort.</p> Results <p>The risk model developed using five AAMRGs demonstrated appreciable ability to predict overall survival in osteosarcoma patients compared to traditional clinical factors. qPCR validated expression of the five AAMRGs comprising the risk model. Single-cell RNA sequencing and drug prediction analyses extend understanding of modeling AAMRGs in OS.</p> Conclusion <p>The current prognostic signature utilizing five AAMRGs can accurately predict prognosis in osteosarcoma and may provide new insights into amino acid metabolism in OS.</p>

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Amino acid metabolism-related genes signature correlates with immune infiltration and predicts clinic prognosis in osteosarcoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Deshuang Xi,
  • Yilin Teng,
  • Shaobo Wang,
  • Meng Zhang,
  • Gang Liu,
  • Bo Han,
  • Yunpeng Zhang,
  • Xiao Tian,
  • Chunxin Cheng,
  • Shouguo Wang,
  • Weidong Liu,
  • Peng Sun

摘要

Background

Amino acid metabolism is crucial for tumor cell proliferation and survival. However, its role in osteosarcoma (OS) remains incompletely characterized. The current study aimed to investigate the role of the amino acid metabolism-associated risk signature in prognostication and immune cell infiltration in osteosarcoma.

Methods

The clinical and transcriptome data of patients with OS were retrieved using the TARGET and GEO databases. Consensus clustering of amino acid metabolism-related genes (AAMRGs) stratified the osteosarcoma patients. Least absolute shrinkage and selection operator regression and Cox analysis were performed to build prognostic models. The expression levels of genes comprising the risk model were validated in vitro. Subsequently, the model was validated in an external cohort.

Results

The risk model developed using five AAMRGs demonstrated appreciable ability to predict overall survival in osteosarcoma patients compared to traditional clinical factors. qPCR validated expression of the five AAMRGs comprising the risk model. Single-cell RNA sequencing and drug prediction analyses extend understanding of modeling AAMRGs in OS.

Conclusion

The current prognostic signature utilizing five AAMRGs can accurately predict prognosis in osteosarcoma and may provide new insights into amino acid metabolism in OS.