Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, P.R. China.
Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana.
J Cell Physiol. 2020 Feb;235(2):1025-1035. doi: 10.1002/jcp.29018. Epub 2019 Jun 25.
Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein-protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8 T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.
皮肤恶性黑色素瘤(以下简称黑色素瘤)是一种侵袭性最强的癌症之一,其全球发病率和死亡率呈上升趋势。在本研究中,我们对与黑色素瘤相关的肿瘤微环境和遗传因素进行了系统研究,以鉴定黑色素瘤的预后生物标志物。我们使用 ESTIMATE 算法从癌症基因组图谱(TCGA)计算了黑色素瘤患者的免疫和基质评分,并发现它们与患者的预后密切相关。然后基于免疫和基质评分获得差异表达基因,并进一步鉴定预后免疫相关基因。功能分析和蛋白质-蛋白质相互作用网络进一步表明,这些基因富集在许多免疫相关的生物学过程中。此外,我们使用 TIMER 算法分析了预后免疫相关基因中六种浸润性免疫细胞的丰度。使用免疫细胞比例进行无监督聚类分析揭示了具有不同生存模式的八个聚类,表明树突状细胞在微环境中最为丰富,CD8 T 细胞和中性粒细胞与患者的预后显著相关。最后,我们在来自基因表达综合数据库的三个独立队列中验证了这些基因。总之,本研究全面分析了肿瘤微环境,并鉴定了黑色素瘤的预后免疫相关生物标志物。
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