Department of Dermatology, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, P. R. China.
Mol Omics. 2021 Apr 1;17(2):307-316. doi: 10.1039/d0mo00159g. Epub 2021 Feb 24.
Melanoma is one of the highly malignant skin tumors, the incidence and death of which continue to increase. The hypoxic microenvironment drives tumor growth, progression, and heterogeneity; it also triggers a cascade of immunosuppressive responses and affects the levels of T cells, macrophages, and natural killer cells. Here, we aim to develop a hypoxia-based gene signature for prognosis evaluation and help evaluate the status of hypoxia and the immune microenvironment in melanoma. Based on the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, we performed integrated bioinformatics to analyze the hypoxia-related genes. Using Lasso Cox regression, a hypoxia model was constructed. The receiver operating characteristic and the Kaplan-Meier curve were used to evaluate the predictive capacity of the model. With the CIBERSORT algorithm, the abundance of 22 immune cells in the melanoma microenvironment was analyzed. A total of 20 hypoxia-related genes were significantly related to prognosis in the log-rank test. Lasso regression showed that FBP1, SDC3, FOXO3, IGFBP1, S100A4, EGFR, ISG20, CP, PPARGC1A, KIF5A, and DPYSL4 displayed the best features. Based on these genes, a hypoxia model was established, and the area under the curve for the model was 0.734. Furthermore, the hypoxia score was identified as an independent prognostic factor. Besides, the hypoxia score could also predict the immune microenvironment in melanoma. Down-regulated activated CD4 memory T cells, CD8 T cells, and M1-like macrophages, and up-regulated Tregs were observed in patients with a high hypoxia score. The hypoxia-related genes were identified, and the hypoxia score was found to be a prognostic factor for overall survival and a predictor for the immune microenvironment. Our findings provide new ideas for evaluation and require further validation in clinical practice.
黑色素瘤是一种高度恶性的皮肤肿瘤，其发病率和死亡率持续上升。缺氧微环境驱动肿瘤生长、进展和异质性；它还触发一连串的免疫抑制反应，并影响 T 细胞、巨噬细胞和自然杀伤细胞的水平。在这里，我们旨在开发一种基于缺氧的基因特征，用于预后评估，并帮助评估黑色素瘤中的缺氧和免疫微环境状态。基于癌症基因组图谱 (TCGA) 和基因表达综合数据库 (GEO) 的数据，我们进行了综合的生物信息学分析，以研究与缺氧相关的基因。使用 Lasso Cox 回归构建了缺氧模型。使用接收器工作特征和 Kaplan-Meier 曲线评估模型的预测能力。使用 CIBERSORT 算法分析了黑色素瘤微环境中 22 种免疫细胞的丰度。在对数秩检验中，有 20 个与预后显著相关的缺氧相关基因。Lasso 回归显示，FBP1、SDC3、FOXO3、IGFBP1、S100A4、EGFR、ISG20、CP、PPARGC1A、KIF5A 和 DPYSL4 显示出最佳特征。基于这些基因，建立了一个缺氧模型，模型的曲线下面积为 0.734。此外，缺氧评分被确定为一个独立的预后因素。此外，缺氧评分还可以预测黑色素瘤的免疫微环境。在缺氧评分较高的患者中，观察到下调的激活 CD4 记忆 T 细胞、CD8 T 细胞和 M1 样巨噬细胞，以及上调的 Tregs。确定了与缺氧相关的基因，并且发现缺氧评分是总生存率的预后因素和免疫微环境的预测因子。我们的研究结果为评估提供了新的思路，需要在临床实践中进一步验证。
