AUTHOR=Yang Ronghua , Wang Zhengguang , Li Jiehua , Pi Xiaobing , Gao Runxing , Ma Jun , Qing Yi , Zhou Sitong TITLE=The Identification of the Metabolism Subtypes of Skin Cutaneous Melanoma Associated With the Tumor Microenvironment and the Immunotherapy JOURNAL=Frontiers in Cell and Developmental Biology VOLUME=9 YEAR=2021 URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.707677 DOI=10.3389/fcell.2021.707677 ISSN=2296-634X ABSTRACT=

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.