Development and Validation of Novel Prognostic Models for Immune-Related Genes in Osteosarcoma

Abstract

Immunotherapy has shown excellent therapeutic effects on various malignant tumors; however, to date, immunotherapy for osteosarcoma is still suboptimal. In this study, we performed comprehensive bioinformatic analysis of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs). Datasets of differentially expressed IRGs were extracted from the GEO database (GSE16088). The functions and prognostic values of these differentially expressed IRGs were systematically investigated using a series of bioinformatics methods. In addition, CCK8 and plate clone formation assays were used to explore the effect of PGF on osteosarcoma cells, and twenty-nine differentially expressed IRGs were identified, of which 95 were upregulated and 34 were downregulated. Next, PPI was established for Identifying Hub genes and biology networks by Cytoscape. Six IRGs (APLNR, TPM2, PGF, CD86, PROCR, and SEMA4D) were used to develop an overall survival (OS) prediction model, and two IRGs (HLA-B and PGF) were used to develop a relapse-free survival (RFS) prediction model. Compared with the low-risk patients in the training cohort (GSE39058) and TARGET validation cohorts, high-risk patients had poorer OS and RFS. Using these identified IRGs, we used OS and RFS prediction nomograms to generate a clinical utility model. The risk scores of the two prediction models were associated with the infiltration proportions of some TIICs, and the activation of memory CD4 T-cells was associated with OS and RFS. CD86 was associated with CTLA4 and CD28 and influenced the infiltration of different TIICs. In vitro experiments showed that the knockdown of PGF inhibited the proliferation and viability of osteosarcoma cells. In conclusion, these findings help us better understand the prognostic roles of IRGs and TIICs in osteosarcoma, and CD86 and PGF may serve as specific immune targets.

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 genes contributing to cisplatin resistance in osteosarcoma cells

“…Several oncogenes, including SEMA4D and SEMA6D, have been involved in axon guidance in human osteosarcomas [ 18 ]. SEMA4D is one of the immune‐related genes (IRGs) used to generate clinical utility models [ 19 ]. These reports demonstrated that the enriched genes identified through our library screening might play an important role in osteosarcoma cisplatin resistance and our screening strategy efficacy.…”

“…Cisplatin is a key drug in osteosarcoma therapy. It has been reported that several genes or pathways contribute to cisplatin resistance [ 19 , 20 , 21 , 22 ]. In this study, we discovered that 78 genes might be responsible for cisplatin resistance using 143B osteosarcoma cells.…”

SLC17A9 expression levels in a pan‑cancer panel and validation of the role of SLC17A9 as a novel prognostic biomarker for osteosarcoma

“…RT-qPCR and WB were performed as described previously ( 18 ). RT-qPCR reagents such as AG RNAex Pro Reagent, SYBR Green Premix Pro Taq HS qPCR kit and the Evo M-MLV RT Premix kit were purchased from Accurate Biology.…”