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| 基于肿瘤干性相关基因的肾癌预后模型的构建 |
| 江爱民1△,王安邦2△,顾迪1,董凯1,富智斌1,吴震杰1,刘冰3,王林辉1* |
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(1. 海军军医大学(第二军医大学)长海医院泌尿外科, 上海 200433;
2. 海军军医大学(第二军医大学)长征医院泌尿外科, 上海 200003;
3. 海军军医大学(第二军医大学)东方肝胆外科医院泌尿外科, 上海 201805
*共同第一作者
*通信作者) |
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| 摘要: |
| 目的 通过挖掘基因表达汇编（GEO）数据库中肾癌干细胞芯片数据，寻找肾癌细胞干性标志物，并联合癌症基因组图谱（TCGA）数据库中的肾癌临床及转录组数据构建一种评估肾癌预后的模型。方法 从GEO数据库GSE48550数据集中下载芯片数据，筛选肾癌干细胞和正常肾小管上皮细胞之间的差异表达基因，通过基因本体（GO）和基因集富集分析进行功能及通路分析，通过蛋白质-蛋白质相互作用（PPI）网络构建确定肾癌干细胞核心基因。从TCGA数据库下载肾癌患者年龄、临床分期、预后情况及相关基因的表达水平，通过单因素及多因素Cox回归分析筛选肾癌预后的独立危险因素，构建预测肾癌患者总生存期的列线图模型。结果 通过分析肾癌干细胞和正常肾小管上皮细胞的芯片数据，发现差异表达基因富集在细胞趋化、细胞外基质形成及受体配体活性等模块，炎症反应通路、P53通路及TNF-α/NF-κB通路在肾癌干细胞中显著激活。单因素及多因素Cox回归分析结果表明，年龄、临床分期为肾癌预后的独立危险因素，趋化因子家族中的C-X3-C基序趋化因子配体1（CX3CL1）是肾癌预后的独立保护因素。通过评估模型区分度，发现基于年龄、临床分期及CX3CL1表达水平的风险模型可准确预测肾癌患者总体生存率，其中C指数为达到0.803。结论 通过GEO和TCGA数据库联合分析筛选肾癌干性相关基因，构建了一种联合患者年龄、临床分期及CX3CL1表达水平的新模型，新模型可用于评估肾癌患者预后。 |
| 关键词: 肾肿瘤 肿瘤干细胞 数据库 趋化因子CX3CL1 预后 |
| DOI：10.16781/j.0258-879x.2021.11.1231 |
| 投稿时间：2020-12-21修订日期：2021-04-13 |
| 基金项目:国家自然科学基金（8172074），上海市科技创新行动计划（11951500）. |
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| Construction of a renal cancer prognostic model based on tumor stemness-related genes |
| JIANG Ai-min1△,WANG An-bang2△,GU Di1,DONG Kai1,FU Zhi-bin1,WU Zhen-jie1,LIU Bing3,WANG Lin-hui1* |
(1. Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai 200433, China;
2. Department of Urology, Changzheng Hospital, Naval Medical University (Second Military Medical University), Shanghai 200003, China;
3. Department of Urology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University (Second Military Medical University), Shanghai 201805, China
*Co-first authors.
* Corresponding author) |
| Abstract: |
| Objective To mine stemness-related biomarkers of renal cancer based on the renal cancer stem cell microarray data from Gene Expression Omnibus (GEO) database, and to construct a new model for the prognosis of renal cancer with the clinical and transcriptome data of renal cancer in the Cancer Genome Atlas (TCGA) database. Methods The microarray data were downloaded from the GSE48550 dataset of GEO database to screen the differentially expressed genes between renal cancer stem cells and normal renal tubular epithelium cells. Gene function and pathway were identified by Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). The hub genes of renal cancer stem cells were identified by protein-protein interaction (PPI) network construction. Age, clinical stage, prognosis and expression levels of related genes of patients with renal cancer were downloaded from the TCGA database. The independent risk factors of prognosis of renal cancer were screened by univariate and multivariate Cox regression analyses, and a nomogram model for predicting the overall survival of patients with renal cancer was constructed. Results By analyzing the microarray data of renal cancer stem cells and normal renal tubular epithelial cells, we found that the differentially expressed genes were enriched in the biological processes such as cell chemotaxis, extracellular matrix formation and receptor ligand activity; and inflammatory response, P53 and tumor necrosis factor α (TNF-α)/nuclear factor κB (NF-κB) pathways were significantly activated in renal cancer stem cells. Univariate and multivariate Cox regression analyses showed that age and clinical stage were independent risk factors for the prognosis of renal cancer, and C-X3-C motif chemokine ligand 1 (CX3CL1) in chemokine family was an independent protective factor for the prognosis of renal cancer. The risk model based on age, clinical stage, and CX3CL1 expression level could accurately predict the overall survival rate of patients with renal cancer, with a C-index of 0.803. Conclusion Stemness-related genes of renal cancer is screened through the joint analysis of GEO and TCGA. A new model combining patient age, clinical stage and CX3CL1 expression level is constructed to evaluate the prognosis of renal cancer patients. |
| Key words: kidney neoplasms neoplastic stem cells database chemokine CX3CL1 prognosis |
