Related Experiment Video
Updated: Sep 26, 2025
Modeling Osteosarcoma Using Li-Fraumeni Syndrome Patient-derived Induced Pluripotent Stem Cells
Published on: June 13, 2018
Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients
Kangsong Tian1, Wei Qi1, Qian Yan1
1Department of West hospital orthopaedic trauma, ZiBo Central Hospital, No. 54 Gongqingtuan West Road, Zhangdian District, 255036, Zibo, Shandong, P. R. China.
Background:
Glycolysis and tumor immunity were interrelated. In present study, we aimed to construct a prognostic model based on glycolysis-immune-related genes (GIGs) of osteosarcoma (OS) patients.
Methods:
The mRNA expression data of OS patients were downloaded from GEO and TARGET databases. The hub genes were screened from 305 differentially expressed genes by univariate cox regression analysis and used to further establish a prognostic Risk Score. The independence of the Risk Score prognostic prediction model based on five genes was tested by multivariate Cox regression analysis. Finally, CIBERSORT and LM22 feature matrix were used to estimate the differences in immune infiltration of OS patients.
Results:
A total of 141 OS patients' mRNA expression data and 296 glycolysis-associated genes were analyzed. Based on these 296 genes, all patients could be divided into two clusters: high glycolysis state and low glycolysis state. In the group with high glycolysis status, patients had low immune scores, indicating that glycolysis status was negatively correlated with immune function. The OS patients with high glycolysis and low immunity had the worst prognosis. Next, the Risk Score was constructed by 5 GIGs, including RAI14, MAF, CLEC5A, TIAL1 and CENPJ. Moreover, the Risk Score was shown to be an independent prognostic model, and high Risk Score patients had a greater risk of death.
Conclusions:
The Risk Score based on GIG could predict the prognosis of OS patients.
