Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma - Fan, Lei; Ru, Jingtao; Liu, Tao; Ma, Chao (Frontiers in Cell and Developmental Biology, 2021)
Background: The tumor microenvironment (TME) chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with the stromal component. Recent studies have shown that tumor immune cell infiltration (ICI) is associated with the clinical outcomes of osteosarcoma (OS) patients. This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes. Methods: The TARGET-OS dataset was used as the training cohort, and the GSE21257 dataset was used as the validation cohort. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan–Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO Cox regression model and produced a gene signature, which was then assessed with the Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. Functional annotation and immune infiltration analyses were performed to better study the functional annotation of the signature and the role of each kind of immune cell. Results: Data from the training cohort were used to generate a nine-gene signature, which was then validated in the validation cohort by Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS, confirming the signature's ability and independence in predicting the outcomes of OS patients. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis detailed the exact mechanism related to the gene signature, and immune-infiltration analyses revealed crucial roles for activated mast cells in the prognosis of OS. Conclusions: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can accurately predict OS prognosis and is closely related to activated mast cells.