Department of Dermatology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China.
J Immunol Res. 2021 Mar 8;2021:6664791. doi: 10.1155/2021/6664791. eCollection 2021.
To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated.
We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model's essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model.
The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN- pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk score = 0.25NOTCH3 + 0.008 DBN1 - 0.031KDELC2 - 0.032STAB1. The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability.
We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.
为了提高黑色素瘤的免疫治疗效果,探索了黑色素瘤中 M2 巨噬细胞的共表达网络和关键基因。建立了 M2 相关共表达基因的预后风险评估模型,并阐明了 M2 巨噬细胞在黑色素瘤免疫微环境中的作用。
我们从癌症基因组图谱皮肤黑色素瘤(TCGA-SKCM)的黑色素瘤和肿瘤周围组织样本中获取了 mRNA 数据。然后,我们使用 CIBERSORT 计算 M2 巨噬细胞的比例。通过分析加权基因共表达网络,确定了 TCGA-SKCM 中与 M2 巨噬细胞最相关的共表达模块,并建立了共表达网络。在生存分析后,将具有显著结果的因素纳入 Cox 回归分析,建立模型。利用功能富集、GSEA 和亚组和总癌分析模型的关键基因。最后,使用外部数据集 GSE65904 和 GSE78220 验证了预后风险模型。
TCGA-SKCM 中与 M2 巨噬细胞最相关的共表达模块为黄-绿色模块;NOTCH3、DBN1、KDELC2 和 STAB1 被确定为促进黑色素瘤中 M2 巨噬细胞浸润的关键基因。这些基因集中在抗原处理和呈递、趋化因子、细胞因子、T 细胞受体途径和 IFN-途径。对这些因素进行了生存分析,将具有显著结果的因素纳入 Cox 回归分析。根据这些方法,建立了与 M2-TAM 共表达基因相关的模型,公式为风险评分=0.25NOTCH3+0.008DBN1-0.031KDELC2-0.032STAB1。新模型用于进行亚组评估和外部队列验证。结果表明具有良好的预后能力。
我们提出了一个与黑色素瘤 M2 巨噬细胞共表达基因相关的 Cox 比例风险回归模型,该模型可能为黑色素瘤患者生成预后评分提供一种测量方法。与 M2 巨噬细胞共表达的四个基因与高水平的 M2 巨噬细胞浸润相关。我们的研究结果可能为黑色素瘤的治疗和监测提供有意义的候选生物标志物。