Department of Dermatology, The First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, The First Hospital of China Medical University and Key Laboratory of Immunodermatology, Ministry of Health and Ministry of Education, Shenyang, China.
Front Immunol. 2021 Oct 1;12:711145. doi: 10.3389/fimmu.2021.711145. eCollection 2021.
Glycolytic effects and immune microenvironments play important roles in the development of melanoma. However, reliable biomarkers for prognostic prediction of melanoma as based on glycolysis and immune status remain to be identified.
Glycolysis-related genes (GRGs) were obtained from the Molecular Signatures database and immune-related genes (IRGs) were downloaded from the ImmPort dataset. Prognostic GRGs and IRGs in the TCGA (The Cancer Genome Atlas) and GSE65904 datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Glycolysis expression profiles and the infiltration of immune cells were analyzed and compared. Finally, experiments were performed to assess the expression and function of these CIGI genes.
Four prognostic glycolysis- and immune-related signatures ( and ) were identified for use in constructing a comprehensive glycolysis and immune (CIGI) model. CIGI proved to be a stable, predictive method as determined from different datasets and subgroups of patients and served as an independent prognostic factor for melanoma patients. In addition, patients in the high-CIGI group showed increased levels of glycolytic gene expressions and exhibited immune-suppressive features. Finally, and may function as tumor suppressor genes, while and may function as oncogenes in melanoma as revealed from results of experiments.
In this report we present our findings on the development and validation of a novel prognostic classifier for use in patients with melanoma as based on glycolysis and immune expression profiles.
糖酵解效应和免疫微环境在黑色素瘤的发展中起着重要作用。然而，基于糖酵解和免疫状态的黑色素瘤预后预测的可靠生物标志物仍有待确定。
从分子特征数据库中获取糖酵解相关基因（GRGs），从 ImmPort 数据集下载免疫相关基因（IRGs）。在 TCGA（癌症基因组图谱）和 GSE65904 数据集识别预后 GRGs 和 IRGs。使用最小绝对收缩和选择算子（LASSO）Cox 回归和多变量 Cox 回归进行模型构建。分析和比较糖酵解表达谱和免疫细胞的浸润。最后，进行实验评估这些 CIGI 基因的表达和功能。
确定了四个用于构建综合糖酵解和免疫（CIGI）模型的预后糖酵解和免疫相关特征（和）。CIGI 被证明是一种稳定的、可预测的方法，从不同的数据集中确定，并且适用于不同的患者亚组，是黑色素瘤患者的独立预后因素。此外，高 CIGI 组患者的糖酵解基因表达水平升高，并表现出免疫抑制特征。最后，实验结果表明，和可能作为肿瘤抑制基因，而和可能作为黑色素瘤的癌基因。
本报告介绍了我们在基于糖酵解和免疫表达谱开发和验证用于黑色素瘤患者的新型预后分类器方面的发现。
