A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma

Weiguang Yang; Haiyang Wu; Linjian Tong; Yulin Wang; Qiang Guo; Lixia Xu; Hua Yan; Chengliang Yin; Zhiming Sun

doi:10.3389/fonc.2022.1015094

A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma

Front Oncol. 2022 Oct 6:12:1015094. doi: 10.3389/fonc.2022.1015094. eCollection 2022.

Authors

Weiguang Yang^{1

2}, Haiyang Wu^{1

2}, Linjian Tong^{1

2}, Yulin Wang^{1

2}, Qiang Guo³, Lixia Xu⁴, Hua Yan^{1

4}, Chengliang Yin⁵, Zhiming Sun^{1

6}

Affiliations

¹ Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
² Department of Graduate School, Tianjin Medical University, Tianjin, China.
³ Department of Orthopaedics, Baodi Clinical College of Tianjin Medical University, Tianjin, China.
⁴ Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin Huanhu Hospital, Tianjin, China.
⁵ Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao SAR, China.
⁶ Department of Orthopaedics, Tianjin Huanhu Hospital, Tianjin, China.

Abstract

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.

Keywords: bioinformatics analysis; cuproptosis; osteosarcoma; prognosis; tumor microenvironment.