Gene Expression Profiles For A Prognostic Immunoscore In Bone And Soft Tissue Sarcoma

Background: Immune infiltration has been used as a prognostic marker to clinical outcomes in various solid tumors.However,reports focus on bone and soft tissue sarcoma is rare.This study aimed to analyze and identify how immune components influence prognosis,and to develop a novel classification and prognostic system for sarcomas.Methods: We retrieved the gene expression data from 3 online databases(GEO,TCGA and TARGET).The immune fraction was estimated using CIBERSORT algorithm.After that,we re-clustered samples by K-means and constructed immunoscore by least absolute shrinkage and selection operator(LASSO)Cox regression model.Next,to confirm the prognostic value,nomograms were constructed.Results: 334 samples diagnosed with 8 tumor types(including osteosarcoma)were involved in our analysis.The samples were next re-clustered into 3 subgroups(OS,SAR1 and SAR2)through immune composition.Survival analysis showed a significant difference between the two soft tissue groups,patients with a higher proportion of CD8+ T cells,macrophages M1 and mast cells had favorable outcomes(p=0.0018).Immunoscore models were successfully established in OS and SAR2 groups consisting of 12 and 9 cell fractions,respectively.We found immunosocre was an independent factor for overall survival time,patients with higher immunoscore had poor prognosis(p<0.0001).In addition,patients with metastatic lesions scored higher than those counterparts with localized tumors(p<0.05).Conclusions: Immune fractions could be a useful tool for the classification and prognosis of bone and soft tissue sarcoma patients.This proposed immunoscore showed a promising impact on survival prediction.