通过共表达网络鉴定M1巨噬细胞相关基因以构建预测甲状腺癌预后的四基因风险评分模型。
Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.
作者信息
Zhuang Gaojian, Zeng Yu, Tang Qun, He Qian, Luo Guoqing
机构信息
The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China.
Department of Thyroid and Neck Tumor, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
出版信息
Front Genet. 2020 Oct 29;11:591079. doi: 10.3389/fgene.2020.591079. eCollection 2020.
Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.
巨噬细胞是肿瘤微环境中的关键固有免疫细胞,可调节原发性肿瘤生长、血管生成、转移扩散及对治疗的反应。巨噬细胞可极化成为具有不同表型和功能的两种不同状态(M1和M2)。为研究已知的M1巨噬细胞的杀肿瘤作用,我们从癌症基因组图谱甲状腺癌(TCGA-THCA)获取了RNA表达谱和临床数据。使用CIBERSORT评估肿瘤样本中免疫细胞的比例,并采用加权基因共表达网络分析(WGCNA)来识别与M1巨噬细胞相关的模块。进行单变量Cox分析和LASSO-Cox回归分析,并通过GEPIA选择了四个具有显著差异表达的基因(SPP1、DHRS3、SLC11A1和CFB)。这四个基因可被视为枢纽基因。四基因风险评分模型可能是THCA患者的独立预后因素。验证队列和整个队列证实了结果。进行单变量和多变量Cox分析以识别THCA的独立预后因素。最后,基于整个队列构建了预后列线图,并且在预测总生存方面,将风险评分与临床预后因素相结合的列线图优于仅包含个体临床预后因素的列线图。时间依赖性ROC曲线和决策曲线分析(DCA)证实了联合列线图是有用的。基因集富集分析(GSEA)用于阐明高危组的潜在分子功能。我们的研究鉴定了与M1巨噬细胞相关的四个基因,并建立了一个预测THCA患者总生存的预后列线图,这可能有助于确定不同患者的临床治疗方案。