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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-7882740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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