Dai Dongjun, Xie Lanyu, Shui Yongjie, Li Jinfan, Wei Qichun
Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Clinical Medicine, Fuzhou Medical College of Nanchang University, Jiangxi, China.
Front Genet. 2021 Feb 1;12:620705. doi: 10.3389/fgene.2021.620705. eCollection 2021.
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).
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.
We selected 255 patients with SARC for our analysis. The Kaplan-Meier method found that higher immune ( = 0.0018) or stromal scores ( = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ ( = 0.0012) and CD8+ T cells ( = 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 ( < 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 (, , , , , , , , , , and ). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis ( < 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.
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.
浸润肿瘤微环境（TME）的免疫细胞与癌症预后相关。本研究旨在利用癌症基因组图谱（TCGA）的数据，确定与肉瘤（SARC）预后相关的TME相关基因特征。
使用表达数据算法通过估计恶性肿瘤组织中的基质细胞和免疫细胞来计算免疫和基质评分。然后使用基于最小绝对收缩和选择算子（lasso）的cox模型选择关键生存基因。使用风险评分模型和列线图预测SARC患者的总生存期。
我们选择了255例SARC患者进行分析。Kaplan-Meier法发现较高的免疫评分（=0.0018）或基质评分（=0.0022）与SARC的较好预后相关。通过肿瘤免疫估计资源估计，总生存期较好的SARC患者中CD4+（=0.0012）和CD8+T细胞水平较高（=0.017）。基于差异表达基因（DEG）分析，我们在免疫和基质评分之间确定了393个上调基因和108个下调基因（<0.05，变化倍数>4）。对每个交叉DEG进行单变量Cox分析，并随后使用基于lasso的Cox模型确定了11个关键生存基因（、、、、、、、、、和）。然后，构建了基于关键生存基因的风险评分基因特征；较高的风险评分预测SARC预后较差（<0.0001）。包含风险评分、免疫/基质评分和临床因素的列线图对SARC总生存期显示出良好的预测价值（C指数=0.716）。最后，连通性图谱分析确定组蛋白去乙酰化酶抑制剂曲古抑菌素A和伏立诺他可能有潜力逆转SARC患者有害的TME。
本研究为TME与SARC之间的关联提供了新的线索。确定了SARC的TME相关生存基因列表和潜在治疗药物。
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