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引用次数: 1
摘要
染色体不稳定性(CIN)与几种临床肿瘤的早期发现密切相关。在本研究中,作者首先基于the cancer genome atlas-skin skin melanoma (TCGA-SKCM)和GSE65904队列的数据集,利用CIN枢纽基因建立了一种新的黑色素瘤预后模型。根据我们模型的风险评分,高危组的疾病特异性生存(DSS)预后较差。结合风险评分、分期、年龄、溃疡、clark等因素,生成Nomogram预测1、3、5年生存率,临床有效性较好。我们的发现还显示了“活化CD8 T细胞”和“效应记忆CD8 T细胞”的高/低风险与肿瘤浸润水平之间的相关性。此外,作者首先使用TCGA-SKCM病例进行了基于cin的肿瘤聚类分析,并确定了两个黑色素瘤簇,它们表现出不同的DSS预后和CD8 T细胞的肿瘤浸润水平。综上所述,我们的研究首次建立了一个有希望的与cin相关的黑色素瘤病例预后特征和聚类。
Chromosome instability-associated prognostic signature and cluster investigation for cutaneous melanoma cases
Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of ‘activated CD8 T cells’ and ‘effector memory CD8 T cells’. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.
期刊介绍:
IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells.
The scope includes the following topics:
Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.