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Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

AIM: Immune 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). METHODS: Immune and stro...

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Autores principales: Dai, Dongjun, Xie, Lanyu, Shui, Yongjie, Li, Jinfan, Wei, Qichun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882740/
https://www.ncbi.nlm.nih.gov/pubmed/33597971
http://dx.doi.org/10.3389/fgene.2021.620705
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author Dai, Dongjun
Xie, Lanyu
Shui, Yongjie
Li, Jinfan
Wei, Qichun
author_facet Dai, Dongjun
Xie, Lanyu
Shui, Yongjie
Li, Jinfan
Wei, Qichun
author_sort Dai, Dongjun
collection PubMed
description AIM: Immune 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). METHODS: Immune 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. RESULTS: We 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. CONCLUSION: The 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.
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spelling pubmed-78827402021-02-16 Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma Dai, Dongjun Xie, Lanyu Shui, Yongjie Li, Jinfan Wei, Qichun Front Genet Genetics AIM: Immune 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). METHODS: Immune 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. RESULTS: We 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. CONCLUSION: The 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. Frontiers Media S.A. 2021-02-01 /pmc/articles/PMC7882740/ /pubmed/33597971 http://dx.doi.org/10.3389/fgene.2021.620705 Text en Copyright © 2021 Dai, Xie, Shui, Li and Wei. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Dai, Dongjun
Xie, Lanyu
Shui, Yongjie
Li, Jinfan
Wei, Qichun
Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title_full Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title_fullStr Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title_full_unstemmed Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title_short Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma
title_sort identification of tumor microenvironment-related prognostic genes in sarcoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882740/
https://www.ncbi.nlm.nih.gov/pubmed/33597971
http://dx.doi.org/10.3389/fgene.2021.620705
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AT lijinfan identificationoftumormicroenvironmentrelatedprognosticgenesinsarcoma
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