Development and Validation of an Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma.
Development and Validation of an Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma.
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皮肤黑色素瘤免疫相关基因对特征的开发和验证
DOI：
10.2147/ccid.s281364
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Chen L
中科院分区：
文献类型：
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作者：
Xie R;Dong S;Jiang J;Yang C;Li L;Zhao S;Li Y;Wang C;Li S;Xiao Y;Chen L
皮肤黑色素瘤(SKCM)是世界范围内常见的皮肤恶性肿瘤，其转移率和死亡率较高。肿瘤-免疫相互作用所表现出的分子特征引起了研究人员的关注。因此，更多的知识和新的策略来识别有效的免疫相关生物标志物可能会通过提供更准确的预后信息来改善SKCM的临床治疗。在这项研究中，我们建立了一个预后免疫相关基因对(IRGP)信号来预测SKCM患者的生存。癌症基因组图谱(TCGA)和基因表达总览(GEO)数据库提供了基因表达谱和临床信息，样本被随机分为三组，包括训练数据集、测试数据集和验证数据集。最小绝对收缩和选择算子的回归模型(LASSO)有助于识别与SKCM患者生存显著相关的13-IRGP信号。训练集、TCGA集和独立集的曲线下面积平均值分别为0.79、0.76和0.82。此外，这种13-IRGP信号可以明显地将SKCM患者分为高风险组和低风险组，其预后显著不同。在我们的IRGP签名中，基因家族等许多生物学活性都得到了丰富。在分析风险特征和临床特征时，T分期与肿瘤分期在风险评分上存在较大差异。最后，我们构建了风险评分和临床特征的诺模图和森林图。总之，我们在SKCM中开发了一个强大的13-IRGP预后标志物，它可以识别并提供对免疫生物标记物的新见解。
Skin cutaneous melanoma (SKCM) is a common skin malignancy worldwide, and its metastasis and mortality rates are high. The molecular characteristics exhibited by tumor–immune interactions have drawn the attention from researchers. Therefore, increased knowledge and new strategies to identify effective immune-related biomarkers may improve the clinical management of SKCM by providing more accurate prognostic information. In this study, we established a prognostic immune-related gene pair (IRGP) signature for predicting the survival of SKCM patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided gene expression profiles together with clinical information, and the samples were randomly divided into three groups including the training, testing, and validation datasets. The regression model of least absolute shrinkage and selection operator (LASSO) helped to identify a 13-IRGP signature with a significant relation to the survival of SKCM patients. The training, TCGA, and independent sets have an average value of area under the curve of 0.79, 0.76, and 0.82, respectively. In addition, this 13-IRGP signature can noticeably divide SKCM patients into high-risk group and low-risk group with significantly different prognoses. Many biological activities such as gene family were enriched among the genes in our IRGP signature. While analyzing the risk signature and clinical characteristics, there was a large difference in the risk score between T stage and tumor stage grouping. Finally, we constructed a nomogram and forest plots of the risk score and clinical features. In summary, we developed a robust 13-IRGP prognostic signature in SKCM, which can identify and provide new insights into immunological biomarkers.