黑色素瘤中具有预后相关性的基因特征的鉴定和验证。
Identification and Validation of Prognostically Relevant Gene Signature in Melanoma.
机构信息
Department of Dermatology, The First Hospital of China Medical University, 110001 Shenyang, China.
NHC Key Laboratory of Immunodermatology, China Medical University, 110001 Shenyang, China.
出版信息
Biomed Res Int. 2020 May 8;2020:5323614. doi: 10.1155/2020/5323614. eCollection 2020.
BACKGROUND
Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis.
METHOD
The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis.
RESULTS
We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (, , , , , and ) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients.
CONCLUSIONS
We constructed a 6-gene signature (, , , , , and ) as a novel prognostic marker for predicting the survival of melanoma patients.
背景
目前，有效的遗传标志物仅限于预测黑色素瘤的临床预后。高通量多组学测序数据为鉴定与癌症预后相关的基因提供了一种有价值的方法。
方法
从癌症基因组图谱（TCGA）中获取黑色素瘤患者的多维数据，包括临床、基因组和转录组数据。然后将这些样本随机分为两组，一组用于训练数据集，另一组用于验证数据集。为了筛选可靠的生物标志物，我们筛选了与预后相关的基因、拷贝数变异基因和 SNP 变异基因，并在训练数据集中使用随机森林进一步筛选特征。我们筛选了稳健的生物标志物，并建立了一个基因相关的预后模型。最后，我们使用 qRT-PCR 和免疫组织化学分析在测试集（GSE19234 和 GSE65904）和从黑色素瘤患者中提取的临床样本中验证了所选的生物标志物。
结果
我们获得了 1569 个与预后相关的基因，以及 1101 个拷贝数扩增、1093 个拷贝数缺失和 92 个显著突变的基因组变异基因。这些基因组变异基因与肿瘤的发生和整合基因组变异的基因密切相关。从与预后相关的基因中获得了总共 141 个候选基因。通过随机森林特征选择，选择了 6 个特征基因（、、、、和），其中许多基因已被报道与肿瘤进展有关。Cox 回归分析用于建立 6 个基因特征。通过 qRT-PCR 和免疫组织化学染色的实验验证，证明这些选定的基因在表达水平上确实明显高于正常组织。该特征构成了黑色素瘤患者独立的预后因素。
结论
我们构建了一个 6 个基因特征（、、、、和）作为预测黑色素瘤患者生存的新的预后标志物。
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