Yang Ronghua, Wang Zhengguang, Li Jiehua, Pi Xiaobing, Gao Runxing, Ma Jun, Qing Yi, Zhou Sitong
Department of Burn Surgery and Skin Regeneration, The First People's Hospital of Foshan, Foshan, China.
Department of Orthopedics, The First Affiliated Hospital of China Medical University, Shenyang, China.
Front Cell Dev Biol. 2021 Aug 12;9:707677. doi: 10.3389/fcell.2021.707677. eCollection 2021.
Skin cutaneous melanoma (SKCM) is a highly aggressive and resistant cancer with immense metabolic heterogeneity. Here, we performed a comprehensive examination of the diverse metabolic signatures of SKCM based on non-negative matrix factorization (NMF) categorization, clustering SKCM into three distinct metabolic subtypes (C1, C2, and C3). Next, we evaluated the metadata sets of the metabolic signatures, prognostic values, transcriptomic features, tumor microenvironment signatures, immune infiltration, clinical features, drug sensitivity, and immunotherapy response of the subtypes and compared them with those of prior publications for classification. Subtype C1 was associated with high metabolic activity, low immune scores, and poor prognosis. Subtype C2 displayed low metabolic activity, high immune infiltration, high stromal score, and high expression of immune checkpoints, demonstrating the drug sensitivity to PD-1 inhibitors. The C3 subtype manifested moderate metabolic activity, high enrichment in carcinogenesis-relevant pathways, high levels of CpG island methylator phenotype (CIMP), and poor prognosis. Eventually, a 90-gene classifier was produced to implement the SKCM taxonomy and execute a consistency test in different cohorts to validate its reliability. Preliminary validation was performed to ascertain the role of SLC7A4 in SKCM. These results indicated that the 90-gene signature can be replicated to stably identify the metabolic classification of SKCM. In this study, a novel SKCM classification approach based on metabolic gene expression profiles was established to further understand the metabolic diversity of SKCM and provide guidance on precisely targeted therapy to patients with the disease.
皮肤黑色素瘤（SKCM）是一种具有高度侵袭性和耐药性的癌症，具有巨大的代谢异质性。在此，我们基于非负矩阵分解（NMF）分类对SKCM的多种代谢特征进行了全面检查，将SKCM聚类为三种不同的代谢亚型（C1、C2和C3）。接下来，我们评估了这些亚型的代谢特征、预后价值、转录组特征、肿瘤微环境特征、免疫浸润、临床特征、药物敏感性和免疫治疗反应的元数据集，并将它们与先前发表的用于分类的数据集进行比较。C1亚型与高代谢活性、低免疫评分和不良预后相关。C2亚型表现出低代谢活性、高免疫浸润、高基质评分和免疫检查点的高表达，显示出对PD-1抑制剂的药物敏感性。C3亚型表现出中等代谢活性、在致癌相关途径中的高富集、高水平的CpG岛甲基化表型（CIMP）和不良预后。最终，生成了一个90基因分类器来实施SKCM分类法，并在不同队列中进行一致性测试以验证其可靠性。进行了初步验证以确定SLC7A4在SKCM中的作用。这些结果表明，90基因特征可以被复制以稳定地识别SKCM的代谢分类。在本研究中，建立了一种基于代谢基因表达谱的新型SKCM分类方法，以进一步了解SKCM的代谢多样性，并为该疾病患者的精准靶向治疗提供指导。
