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生物信息学分析筛选膀胱癌肿瘤微环境中的关键预后基因。

Bioinformatics Analysis to Screen the Key Prognostic Genes in Tumor Microenvironment of Bladder Cancer.

机构信息

Department of Urology, Qilu Hospital of Shandong University, Wenhuaxi Road 107#, Jinan 250012, China.

出版信息

Biomed Res Int. 2020 Feb 17;2020:6034670. doi: 10.1155/2020/6034670. eCollection 2020.

Abstract

Bladder cancer (BLCA) is the fifth most common cancer and has the features of low survival rate and high morbidity and mortality. The Cancer Genome Atlas (TCGA) is a pool of global gene expression profile and contains huge amounts of cancer genomics data, which makes it possible to inquire the relationship between gene expression and prognosis of a series of malignant tumors including BLCA. Immune and stromal cells are two major components of tumor microenvironment (TME) which play an important role in judging the prognosis of tumor and influencing the progression of malignant, inflammatory, and metabolic disorders. In our study, we conducted a quantitative analysis of immune and stromal elements based on the ESTIMATE algorithm and thus divided BLCA cases into high and low groups. Then the differentially expressed genes closely related to tumor prognosis between groups were identified and had been shown to correlate with immune response and stromal alterations, which was further confirmed by functional enrichment analysis and protein-protein interaction networks. We validated those genes through BLCA dates downloaded from ArrayExpress and thus got the marker genes to predict prognosis of BLCA. Additionally, immune cell infiltration analysis explored the correlation between the verified genes and immune cells. In conclusion, we identified a series of TME-related genes that assess the prognosis and explored the interaction between TME and tumor prognosis to guide clinical individualized treatment.

摘要

膀胱癌(BLCA)是第五大常见癌症,具有生存率低、发病率和死亡率高的特点。癌症基因组图谱(TCGA)是一个全球基因表达谱数据库,包含大量的癌症基因组学数据,这使得查询基因表达与包括 BLCA 在内的一系列恶性肿瘤的预后之间的关系成为可能。免疫细胞和基质细胞是肿瘤微环境(TME)的两个主要组成部分,它们在判断肿瘤预后和影响恶性、炎症和代谢紊乱的进展方面发挥着重要作用。在我们的研究中,我们基于 ESTIMATE 算法对免疫和基质成分进行了定量分析,从而将 BLCA 病例分为高分组和低分组。然后,我们鉴定了两组之间与肿瘤预后密切相关的差异表达基因,并通过功能富集分析和蛋白质-蛋白质相互作用网络进一步证实了这些基因与免疫反应和基质改变相关,我们通过从 ArrayExpress 下载的 BLCA 数据验证了这些基因,从而得到了预测 BLCA 预后的标志物基因。此外,免疫细胞浸润分析探讨了验证基因与免疫细胞之间的相关性。总之,我们鉴定了一系列与 TME 相关的基因,以评估预后,并探索了 TME 与肿瘤预后之间的相互作用,以指导临床个体化治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3925/7048919/ff6ebd81151e/BMRI2020-6034670.001.jpg

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