Department of Dermatology, Dongying People's Hospital, Dongying 257091, China.
College of Integrated Chinese and Western Medicine, Liaoning University of traditional Chinese Medicine, Shenyang 110079, China.
Anal Cell Pathol (Amst). 2021 Jan 16;2021:4743971. doi: 10.1155/2021/4743971. eCollection 2021.
In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK-STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages ( < 0.01). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages ( = 0.023) and activated mast cells ( = 0.005).
在本研究中,我们旨在研究黑色素瘤中的免疫相关特征和免疫浸润。从癌症基因组图谱数据库下载黑色素瘤的转录组谱和临床数据,并从基因型-组织表达数据库获得其匹配的正常样本。使用 Perl 合并基因组表达数据后,使用 limma 软件包进行数据归一化。我们筛选差异表达基因(DEGs),并从 InnateDB 数据库中的免疫相关特征列表获得与黑色素瘤相关的免疫特征。使用单变量 Cox 回归分析识别潜在的预后免疫基因,并用 LASSO 分析识别枢纽基因。接下来,基于多变量 Cox 回归分析的结果,我们构建了黑色素瘤风险模型。我们研究了风险评分与患者临床特征和总生存期(OS)之间的相关性。基于 TIMER 数据库,评估了选定的免疫特征与免疫细胞分布之间的相关性。接下来,使用 CIBERSORT 进行 Wilcoxon 秩和检验,证实了不同风险组之间免疫浸润细胞的差异分布。我们获得了 91 个差异表达的免疫相关特征列表。功能富集分析表明,这些免疫相关 DEGs 参与了几个免疫相关相互作用的领域,包括细胞因子-细胞因子受体相互作用、JAK-STAT 信号通路、趋化因子信号通路和 Th17 细胞分化通路。基于多变量 Cox 分析结果建立了风险模型,并进行了 Kaplan-Meier 分析。Kruskal-Wallis 检验表明,高风险评分表明 OS 较差,与美国癌症联合委员会肿瘤分期(AJCC-TNM)较高和病理分期较晚(<0.01)相关。此外,在特定的肿瘤样本中评估了枢纽免疫特征与免疫细胞分布之间的关系。使用 Wilcoxon 秩和检验估计两组的免疫浸润密度,结果表明,高危组的浸润密度较低,主要的免疫细胞包括 M0 巨噬细胞(=0.023)和激活的肥大细胞(=0.005)。