Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Abstract

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

Probing the Potential of Defense Response-Associated Genes for Predicting the Progression, Prognosis, and Immune Microenvironment of Osteosarcoma

“…These results suggested that osteosarcoma in the high-risk group of DGPS can be classified as a cold tumor subgroup, i.e., with poorer immune infiltration and possibly poorer response to immunotherapy [ 52 , 53 ]. The expression of immune checkpoints LAG3, NRP1, CD40LG, C10orf54, CD86, CD48, CD274, HAVCR2, CD27, CTLA4, CD200R1, LAIR1, LGALS9, CD28, and PDCD1LG2 was significantly higher in the low-risk group than in the high-risk group, and it was previously confirmed in the literature that LAG3 [ 54 ], CD86 [ 55 ], HAVCR2 [ 56 ], CD27 [ 57 ], LAIR1 [ 58 ], and PDCD1LG2 [ 56 ] checkpoints play important roles in osteosarcoma cell therapy. These results suggested that these checkpoints could be potential targets for osteosarcoma therapy and that DGPS could provide a new criteria to guide osteosarcoma immunotherapy.…”

Identification of B cell marker genes based on single-cell sequencing to establish a prognostic model and identify immune infiltration in osteosarcoma

“…PLD3 can regulate inflammatory cytokine response through the degradation of nucleic acid. PLD3 has also been shown to be a prognostic protective gene for osteosarcoma, but this is the first time we have confirmed that it is also a B-cell marker gene for osteosarcoma (12)(13)(14). SNX2 belongs to the sorting nexin family and plays a role in the transport of intracellular cargo proteins.…”

Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma

“…Therefore, there is a need to develop and study prognostic models of osteosarcoma to guide targeted therapy. With the development of bioinformatics and sequencing technology, many scholars have constructed different prognostic models of osteosarcoma to analyze the characteristics of the disease (28)(29)(30). However, most of the parameters used to construct prognostic models consider only the genome or transcriptome and do not consider biological processes.…”