Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning

Autor: Bo Wen, Jian Chen, Tianqi Ding, Zhiyou Mao, Rong Jin, Yirui Wang, Meiqin Shi, Lixun Zhao, Asang Yang, Xianyun Qin, Xuewei Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-69638-3
Popis: Abstract Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signalling in OS and further constructed novel hypoxia-related gene signatures for OS prediction and prognosis. This study employed Gene Set Enrichment Analysis (GSEA), Weighted correlation network analysis (WGCNA) and Least absolute shrinkage and selection operator (LASSO) analyses to identify Stanniocalcin 2 (STC2) and Transmembrane Protein 45A (TMEM45A) as the diagnostic biomarkers, which further assessed by Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves in training and test dataset. Univariate and multivariate Cox regression analyses were used to construct the prognostic model. STC2 and metastasis were devised to forge the OS risk model. The nomogram, risk score, Kaplan Meier plot, ROC, DCA, and calibration curves results certified the excellent performance of the prognostic model. The expression level of STC2 and TMEM45A was validated in external datasets and cell lines. In immune cell infiltration analysis, cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. And the immune infiltration of CAFs was negatively associated with the expression of STC2 (P
Databáze: Directory of Open Access Journals
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