Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.
Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.
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DOI：
10.3389/fonc.2021.615963
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发表时间：
2021
影响因子：
4.7
通讯作者：
Sun C
中科院分区：
文献类型：
--
作者：
Qin R;Peng W;Wang X;Li C;Xi Y;Zhong Z;Sun C
皮肤黑色素瘤（CM）是皮肤癌死亡的主要原因，通常在晚期诊断，导致预后不良。肿瘤微环境（TME）在肿瘤发生和CM进展中起着重要作用，但免疫和基质成分的动态调节尚未完全了解。在本研究中，我们定量免疫和基质成分之间的比例和肿瘤浸润免疫细胞（TIC）的比例，基于ESTIMATE和CIBERSORT计算方法，在471例皮肤CM（SKCM）从癌症基因组图谱（TCGA）数据库中获得。通过单变量考克斯回归分析、最小绝对收缩和选择算子（LASSO）回归分析和多变量考克斯回归分析来分析差异表达基因（DEG），以鉴定肿瘤相关基因。所开发的预后模型包含10个基因，这些基因对患者的预后都至关重要。在训练数据集中，1年、3年、5年和10年时开发的预后模型的曲线下面积（AUC）值分别为0.832、0.831、0.880和0.857。GSE 54467数据集用作验证集以确定预后特征的预测能力。利用蛋白质相互作用（PPI）分析和加权基因共表达网络分析（WGCNA）验证与TME密切相关的“真实的”枢纽基因。通过免疫组织化学（IHC）分析验证这些枢纽基因的差异表达。总之，本研究可能为CM提供潜在的诊断和预后生物标志物。
Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.
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影响因子：
10.9
作者：
Mushtaq MU;Papadas A;Pagenkopf A;Flietner E;Morrow Z;Chaudhary SG;Asimakopoulos F
通讯作者：
Asimakopoulos F
影响因子：
--
作者：
Botía JA;Vandrovcova J;Forabosco P;Guelfi S;D'Sa K;United Kingdom Brain Expression Consortium;Hardy J;Lewis CM;Ryten M;Weale ME
通讯作者：
Weale ME
影响因子：
7.5
作者：
Chen, Lih-Chyang;Chen, Chia-Chun;Hsueh, Chuen
通讯作者：
Hsueh, Chuen
DOI：
10.1038/nrclinonc.2017.88
发表时间：
2017-11
期刊：
Nature reviews. Clinical oncology
影响因子：
--
作者：
Nishino M;Ramaiya NH;Hatabu H;Hodi FS
通讯作者：
Hodi FS
C3、C3AR1、HLA-DRA 和 HLA-E 作为肾透明细胞癌的潜在预后生物标志物。
影响因子：
2
作者：
Chu G;Jiao W;Yang X;Liang Y;Li Z;Niu H
通讯作者：
Niu H