A diagnostic signature developed based on the necroptosis-related genes and its association with immune infiltration...

Osteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death.

The RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs.

Three clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model.

Our prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.