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

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

Authors

Institutions

Publication Details

Journal
BMC Cancer
Published
2026-03-07
DOI
https://doi.org/10.1186/s12885-026-15746-4
Primary Topic
Ferroptosis and cancer prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Xiao Tian, Y T Zhang, M Zhang, Yilin Teng et al.
BMC Cancer
Ferroptosis and cancer prognosis
article

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

Xiao Tian, Y T Zhang, M Zhang, Yilin Teng, Shouguo Wang, Weidong Liu, Bo Han, Deshuang Xi, Chunxin Cheng, Shaobo Wang, Peng Sun, Gang Liu
article en

Abstract

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

BMC Cancer
Xuzhou Medical College (CN), Huaian First People’s Hospital (CN), Second People’s Hospital of Huai’an (CN), First Affiliated Hospital of GuangXi Medical University (CN)
Good health and well-being
Openalex Percentile: Top 29%
Ferroptosis and cancer prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Amino acid metabolism-related genes signature correlates with immune infiltration and predicts clinic prognosis in osteosarcoma — Xiao Tian, Y T Zhang, et al. · BMC Cancer (2026) | TGRS Research Map | TGRS