In silico analysis of the immune microenvironment in bladder cancer
In silico analysis of the immune microenvironment in bladder cancer
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DOI：
10.1186/s12885-020-06740-5
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发表时间：
2020-03-30
期刊：
影响因子：
3.8
通讯作者：
Lin, Ming-en
中科院分区：
文献类型：
--
作者：
Zhang, Ye;Ou, De-hua;Lin, Ming-en
背景浸润性免疫细胞和基质细胞是膀胱癌 (BC) 微环境的重要组成部分，可显着影响 BC 的进展和结果。然而，肿瘤浸润免疫细胞的每个子集的贡献尚不清楚。本研究的目的是对肿瘤免疫微环境进行细胞表型和转录谱分析，并分析不同细胞亚群和基因与 BC 预后的关联。方法从癌症基因组图谱下载 412 名 BC 患者的临床数据以及 433 个正常组织和癌症组织的转录文件。使用CIBERSORT算法确定每个样本中22种免疫细胞类型的相对丰度，并使用ESTIMATE算法识别BC肿瘤微环境中的差异表达基因，并对这些基因进行功能富集和蛋白质-蛋白质相互作用（PPI）分析。通过Cox回归分析和Kaplan-Meier方法检查细胞亚群和差异表达基因与患者生存和临床参数的关联。结果静息自然杀伤细胞和活化的记忆CD4(+)和CD8(+) T细胞与良好的患者预后相关，而静息记忆CD4(+) T细胞与不良预后相关。差异表达分析揭示了 1334 个基因影响免疫和基质细胞评分；其中，97 项可预测 BC 患者的总生存期。在PPI网络中前10个具有统计学意义的中心基因中，CXCL12、FN1、LCK和CXCR4被发现与BC预后相关。结论肿瘤浸润免疫细胞和癌症微环境相关基因可以影响患者的预后，并且可能是BC预后和免疫治疗反应的重要决定因素。
BackgroundInfiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis.MethodsClinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method.ResultsResting natural killer cells and activated memory CD4(+) and CD8(+) T cells were associated with favourable patient outcome, whereas resting memory CD4(+) T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis.ConclusionTumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC.