Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma
Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma
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DOI：
10.3389/fgene.2020.00978
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发表时间：
2020-08-28
影响因子：
3.7
通讯作者：
Xin, Wang
中科院分区：
文献类型：
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作者：
Fu, Denggang;Zhang, Biyu;Xin, Wang
肺鳞癌(LSCC)是非小细胞肺癌中最常见的亚型。近年来，免疫疗法已成为一种有效的治疗方法，但患者对目前的治疗方法表现出不同的反应。识别潜在的免疫基因组特征对于预测患者的预后是至关重要的。从TCGA数据库下载具有临床信息的喉癌患者的表达谱。用Edger算法提取差异表达的免疫相关基因，功能富集度分析表明这些免疫相关基因主要富含在炎症和免疫相关过程中。“细胞因子-细胞因子受体相互作用”和“PI3K-AKT信号通路”是最丰富的KEGG通路。单因素COX回归分析显示，27个差异表达的IRG与患者的总生存期(OS)显著相关。通过多变量Cox逐步回归分析，得到了一个由7个IRG(GCCR、FGF8、CLEC4M、PTH、SLC10A2、NPPC和FGF4)组成的预后风险信号，具有有效的预测性能。最重要的是，在调整了临床病理参数后，该信号可能是一个独立的预后预测因子，并在两个独立的喉癌队列(GSE4573和GSE17710)中得到验证。通过计算生物学对这些IRG的潜在分子机制和肿瘤免疫格局进行了研究。对肿瘤浸润性淋巴细胞和免疫检查点分子的分析显示，高危组和低危组具有明显的免疫格局。本研究首次构建了基于IRG的免疫标志物，用于识别喉癌患者的疾病进展和预后。
Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.