Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

Abstract

Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

“…Recently, the advancements and applications in bioinformatics tools made it easy to mine the potential gene signatures associated with the OS and prognosis of the cancers including osteosarcoma from publically available databases such as TCGA, TARGET, and GEO ( Cao et al, 2020 ; Chen et al, 2020 ; Song et al, 2020 ; Wen et al, 2020 ; Xiao et al, 2020 ; Yang et al, 2021 ). Various signature genes have been found associated with the OS of osteosarcoma patients and tumor development.…”

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

“…In addition, more and more studies are involved. For example, in the nine studies (Cao et al, 2020;Chen Z. et al, 2020;Song et al, 2020;Wen et al, 2020;Xiao et al, 2020;Yu et al, 2020;Zhu et al, 2020;Fu et al, 2021;Yang et al, 2021) we included in the study based on the inclusion criteria, they all looked for potential survival-related OS gene signatures from public datasets.However, after we validated them in the training and validation cohorts, we found that the nine-gene signature we generated was superior to others in predicting OS prognosis. Among the nine studies, the one by Yang et al (2021)revealed a gene signature that closely resembled ours.…”

Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes

“…Another study also validated that low-risk OS patients had significantly higher CD79A expression 32 . In addition, Cao et al 33 demonstrated that APBB1IP may act as a key to predicting OS prognosis and new biomarkers are of great significance for understanding the therapeutic targets of OS. Fan et al 34 clustered patients based on the immune cell infiltration content to identify a 9-prognostic gene signature gene model, including PLD3 for OS microenvironment exploration.…”