Immunogenic Cell Death-Related Classifications Guide Prognosis And Immunotherapy In Osteosarcoma

Immunogenic cell death(ICD)is a novel mechanism of cell death that regulates cell death by stimulating the immune system to generate an immune response against cancer cells through the release of tumor-associated antigens and tumor-specific antigens.ICD occurs with the release of damage-associated molecular patterns(DAMPs),precursor antigens,inflammatory cytokines and inflammatory mediators release and/or increased expression.Therefore,the ability to induce immunogenic death of tumor cells is one of the most important factors affecting the therapeutic outcome.Osteosarcoma(OS)is the most common malignant bone tumor in adolescents.With bone destruction and bone tumor formation,symptoms such as pain,local swelling and functional impairment seriously affect the patients’ living status,while its high malignancy and rapid progression seriously threaten human health.Bioinformatics is an emerging multi-disciplinary interdisciplinary discipline that applies computer science,information science,statistics and information technology to solve complex problems in life sciences and medicine.The main research directions of bioinformatics include genome annotation,evolutionary biology,protein structure prediction,genomics,regulatory analysis,biological system simulation and drug target gene discovery and validation.Objective:This study is a bioinformatics-based approach aimed at developing a risk assessment model based on genes associated with immunogenic cell death to assess the immune microenvironment,overall survival and treatment response in patients with osteosarcoma,thereby helping physicians make important judgments about the treatment of patients with osteosarcoma.Methods:The RNA-seq transcriptome information and clinical information of 85 OS patients in Target database were used as the training set.The RNA-seq transcriptome information and clinical information of 53 OS patients(GSE21257)in the Gene Expression Omnibus(GEO)database were used as the validation set.Immunogenic cell death-related genes were copolymerized using the Concensus Cluster Plus function in R language to determine the molecular subtypes of ICD.Data from the dataset were analyzed using the R/Bio Conductor package Edger to screen out differentially expressed genes and volcano maps were plotted using the R language package ggplot.The differentially expressed genes obtained from the above steps were analyzed for GO function and KEGG signaling pathway enrichment using the R language/Bio Conductor package Cluster Profiler.The risk values of all patients were calculated based on the coefficients of gene expression levels and multivariate Cox regression analysis,and we calculated the risk score for each patient based on the above formula and divided the patients into high-risk and low-risk groups using the median risk score as the cut-off point.Univariate and multivariate Cox regression analyses were used to evaluate the risk score model for patients with osteosarcoma to determine the prognostic model independent of other clinical characteristics,and to plot the nomogram and associated calibration curve.Kaplan-Meier(KM)analysis was performed in R language using Surv Miner and survival packages to compare the overall survival of patients with different osteosarcomas.Results:(1)13 genes were found to be significantly associated with prognosis and survival in patients with osteosarcoma,including CASP1,CD8 A,CXCR3,EIF2AK3,FOXP3,IFNG,IFNGR1,IL-10,LY96,MYD88,NLRP3,PRF1,and TLR4.(2)Different expression patterns of immunogenic cell death genes-ICD-low subtype and ICD-high subtype-were identified.And the results of the ICD-high subtype survival analysis suggested a better prognosis compared to the ICD-low subtype.(3)There were 414 differentially expressed genes(DEGs)for ICD-low subtype and ICD-high subtype.GO enrichment analysis of differential genes: In terms of biological process(BP),differential genes are mainly enriched in the positive regulation of cell activation,positive regulation of leukocyte activation,and leukocyte mediated immunity,etc.In terms of cell component(CC),the differential genes are enriched in the external side of plasma membrane,immunoglobulin complex,endocytic vesicle.In terms of molecular function(MF),antigen binding,immunoglobulin receptor binding,immune receptor activity,etc.The enrichment analysis of the KEGG pathway of differential genes mainly includes the Phagosome,Cytokine-cytokine receptor interaction,Staphylococcus aureus infection,Cell adhesion molecules,and so on.(4)Compared to the ICD-low subtype,the ICD-high subtype had higher interstitial scores,immune scores and estimated scores,but lower tumor purity.patients with the ICD-high subtype had a higher proportion of B cells,CD4 T cells,CD8 T cells,endothelial cells and macrophages.Many immune checkpoint and human leukocyte antigen(HLA)genes were upregulated in the ICD-high subtype,suggesting that the ICD-high subtype is associated with an immune heat phenotype.(5)Both the Target cohort and the GEO cohort showed significantly higher survival rates in the low-risk cohort than in the high-risk cohort population.(6)The expression of the high-risk gene e IF2AK3 was negatively correlated with the degree of macrophage infiltration,whereas the expression of the low-risk genes FOXP3,IFNGR1,and TLR4 was positively correlated with these immune cells.(7)Susceptibility of osteosarcoma patients in the high-risk and low-risk cohorts to immunotherapeutic agents: patients in the low-risk cohort had better sensitivity and lower IC50 to XAV939,GSK2606414,Leflunomide,AZ960,PF-4708671,AZD8055)and Ribociclib.Patients in the high-risk cohort had better sensitivity and lower IC50 for RO-3306,BI-2536,Afuresertib,NVP-ADW742,and SB505124.Conclusion:Two immunogenic cell death-associated subtypes in osteosarcoma were identified,and the ICD-low subtype was found to be associated with good clinical outcome,abundant immune cell infiltration,and highly active immune response signaling.Patients with osteosarcoma based on different risk scores were found to have different susceptibility to different drugs,and a risk score model based on ICD for patients with osteosarcoma allows for better selection of therapeutic agents.