Hypoxia Prognostic Genes Identification And Immune Infiltration Analysis In Osteosarcoma

Background and objective: Osteosarcoma is one of the most common primary bone malignant tumors,which often occurs in children and adolescents.Osteosarcoma has the characteristics of high invasion,easy metastasis,tolerance to radiotherapy and chemotherapy,and poor prognosis.At present,the 5-year overall survival rate under the standard treatment regimen is about 65-70%.Local tumor recurrence,metastasis and drug resistance are the biggest obstacles to treatment.Studies have found that hypoxic microenvironment plays a key role in promoting tumor invasion,immune escape and drug resistance,which leads to poor clinical prognosis of tumor.At present,there is no related literature to report the use of effective hypoxia-related prognostic markers to predict the clinical prognosis of patients with osteosarcoma.By identifying and verifying the prognostic markers of hypoxia-related genes in osteosarcoma,this study aims to construct a new risk prognostic model to predict the clinical prognosis of patients and guide the clinical treatment of osteosarcoma.At the same time,by exploring the interaction between hypoxia and immune microenvironment in osteosarcoma,the role of hypoxia in tumor immunosuppression and immune escape was further explained.Research methods: 1.The RNA-seq data and clinical data of 85 cases of osteosarcoma were downloaded from TCGA database as training set,and the RNA-seq data and clinical data of 87 cases of osteosarcoma were downloaded from GEO.Download hypoxia gene set from MSig DB,containing 334 hypoxia-related genes;2.The hypoxia genes related to prognosis were screened by univariate COX regression analysis and LASSO regression,and the risk prognosis model was constructed and grouped according to the risk score.3.Kaplan-Meier was used to analyze the survival between high-risk and low-risk groups,and the accuracy of the model was evaluated by ROC curve.4.The independent prognostic ability of the risk prognostic model was verified by univariate and multivariate COX regression analysis,and the survival differences of risk groups among clinical subgroups were analyzed.5.GSEA method was used to analyze the cell signal pathway enrichment in high and low risk groups,and tumor immune infiltration in samples between risk groups was analyzed by combining ss GSEA,x Cell,ESTIMATE and other methods.6.The gene differences between groups were analyzed by "limma" package,and the functional enrichment of differential genes by KEGG and GO were analyzed.7.Based on the risk prognosis model,the samples of GEO dataset were grouped into risk groups and analyzed for inter-group survival.The prediction accuracy of the evaluation model was verified by ROC curve,and the applicability of the prognostic risk model was further verified.Results: Eight hypoxia genes related to prognosis(BNIP3,CALD1,COL5A2,MXI1,P4HA1,STC2,PSMC4,PSMD10)were screened by univariate COX regression analysis and LASSO regression analysis of TCGA osteosarcoma RNA-seq data,and the risk prognostic models were constructed,which were divided into high risk group and low risk group.The results of interrisk survival analysis showed that the overall survival time of patients in the high-risk group was significantly higher than that in the low-risk group(P < 0.0001).The results of),ROC curve confirmed that the risk model had better prediction accuracy.Univariate and multivariate COX regression analysis showed that risk score could be used as an independent prognostic index for patients with osteosarcoma.The results of gene function enrichment analysis among risk groups showed that the degree of enrichment of hypoxia-related genes was high in the high-risk group,and the apoptosis signals and immune-related signal pathways were significantly enriched in the low-risk group.The results of tumor immune infiltration analysis showed that the degree of immune infiltration in the low-risk group was significantly higher than that in the high-risk group.Through the analysis of gene expression differences among risk groups,a total of 101 differential genes were obtained.The results of KEGG and GO functional enrichment analysis showed that they were highly enriched in immune response-related biological processes and cell autophagy.Finally,the prediction effect of the prognosis model is like that of the training set in the verification set.Conclusion: In this study,a new risk prognosis model with 8 hypoxia-related genes was constructed and verified in osteosarcoma by bioinformatics method.This model has good accuracy and universality,and can be used to predict the clinical outcome of patients with osteosarcoma.to provide reference for treatment.At the same time,the study found that the anoxic microenvironment of osteosarcoma affects the degree of tumor immune infiltration through a variety of mechanisms,and they play an important role in promoting the invasion,progression,metastasis and immune escape of osteosarcoma,but further experimental exploration is needed.