Ye Zaisheng, Zheng Miao, Zeng Yi, Wei Shenghong, Wang Yi, Lin Zhitao, Shu Chen, Xie Yunqing, Zheng Qiuhong, Chen Luchuan
Department of Gastrointestinal Surgical Oncology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, China.
Department of Clinical Laboratory, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Front Genet. 2020 Dec 11;11:595477. doi: 10.3389/fgene.2020.595477. eCollection 2020.
Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. Tumor purity and infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the ESTIMATE R package and CIBERSORT method, respectively. Differentially expressed genes (DEGs) between the high and low mRNAsi groups were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the high and low mRNAsi groups. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.
癌症干细胞(CSCs)具有无限增殖和自我更新的特征,给肿瘤治疗带来了巨大挑战。对其可塑性、动态不稳定性以及免疫微环境相互作用的研究可能有助于克服这一障碍。从癌症基因组图谱(TCGA)和加州大学圣克鲁兹分校(UCSC)Xena浏览器获取了关于干性指数(mRNAsi)、基因突变、拷贝数变异(CNV)、肿瘤突变负荷(TMB)以及相应临床特征的数据。分别使用ESTIMATE R包和CIBERSORT方法预测胃腺癌(STAD)组织中的肿瘤纯度和浸润免疫细胞。利用加权基因共表达网络分析(WGCNA)和套索回归,将高mRNAsi组和低mRNAsi组之间的差异表达基因(DEGs)用于构建预后模型。在STAD中评估了癌症干性、基因突变和免疫反应之间的关联。在高mRNAsi组和低mRNAsi组之间共鉴定出6739个DEGs。基于Cox回归,使用棕色(包含19个基因)和蓝色(包含209个基因)共表达模块中的DEGs进行生存分析。从178个与生存相关的DEGs构建了一个九基因特征预后模型(ARHGEF38-IT1、CCDC15、CPZ、DNASE1L2、NUDT10、PASK、PLCL1、PRR5-ARHGAP8和SYCE2),这些基因与STAD的总生存、临床特征、肿瘤微环境免疫细胞、TMB以及癌症相关通路显著相关。基因相关性在预后模型、CNV和药物敏感性之间具有显著性。我们的研究结果提供了一个预后模型,并突出了对靶向癌症干细胞有用处的潜在机制和相关因素(免疫微环境和突变状态)。