Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Abstract

AimImmune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).MethodsImmune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.ResultsWe selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.ConclusionThe current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

“…62 In STS too, recent clinicogenomic models have been published as nomograms through analysis of immune genes in the TCGA series, but without effort of validation in an independent set. [28][29][30] Our model is the first one to show robustness in a validation set (339 samples), in which we confirmed better prediction accuracy than that achieved by using either clinical data or genomic signature alone.…”

“…In a reanalysis of the TCGA dataset, an association between shorter overall survival and lower immune scores, CD4+ T cells and CD8+ T cells estimated using ESTIMATE and TIMER deconvolution algorithms was reported. 30 In a study dedicated to UPS, two groups ('immune-high' and 'immune-low') were identified from gene expression profiles and confirmed at the proteomic level. 27 When compared with the 'immune-low' group, the 'immune-high' group showed longer overall survival, higher tumor mutational burden, and lower copy number alterations rate, and lower sensitivity to FGFR inhibition.…”

“…25 Several other prognostic immune signatures were then reported. [26][27][28][29][30] To our knowledge, none of them is currently used in clinical practice.…”

CSPG4 Expression in GIST Is Associated with Better Prognosis and Strong Cytotoxic Immune Response

“…Because CSPG4 is the target of cellular immunotherapy, we searched for correlations of its expression in tumors with several immune variables. In each data set separately, several immunity-related multigene classifiers were applied to each tumor: the 24 Bindea’s innate and adaptative immune cell subpopulations [ 27 ], the Immunologic Constant of Rejection (ICR) classifier [ 28 ], and metagenes associated representative of T-cell-inflamed signature (TIS) [ 29 ], of tertiary lymphoid structures (TLS) signature [ 30 ], of cytolytic activity score [ 31 ], of IFNα and IFNγ pathways activation score [ 32 ], the antigen processing machinery (APM) score [ 33 ], and ESTIMATE scores (Immune infiltration, Stromal infiltration, Tumor purity) [ 34 ].…”

m6A regulator-mediated methylation modification patterns and tumor immune microenvironment in sarcoma

“…At present, the molecular regulatory mechanisms of sarcoma have not yet been fully elucidated. Notably, an increasing number of studies have shown that changes in the tumor immune microenvironment (TIME) play a key role in the occurrence and development of sarcoma [ 9 , 10 ].…”