| 孙晨,夏云红.基于生物信息学构建胃癌患者预后比例风险回归模型.[J].中南医学科学杂志.,2023,(5):670-674. |
| 基于生物信息学构建胃癌患者预后比例风险回归模型 |
| The prognostic proportional risk regression signature of gastric cancer patients based on bioinformatics |
| 投稿时间：2022-12-07 修订日期：2023-08-29 |
| DOI：10.15972/j.cnki.43-1509/r.2023.05.010 |
| 中文关键词: 胃癌 生物信息学 功能富集分析 预测效能 预后 [ |
| 英文关键词:gastric canler bioinformatics functional enrichment analysis prediction prognosis |
| 基金项目:安徽省自然科学基金面上项目(2108085MH289) |
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| 摘要点击次数: 319 |
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| 中文摘要: |
| 目的探讨胃癌(GC)发生发展中的关键基因和通路,并构建胃癌患者预后预测模型。 方法从癌症基因组图谱(TCGA)数据库收集胃癌和癌旁正常组织的基因表达数据和临床指标,使用R包分析基因表达差异并做基因功能富集分析。采用比例风险回归模型Cox分析法构建胃癌患者预后预测模型；ROC曲线检测预测模型的准确性并绘制列线图。 结果获得930个差异表达基因,包含436个上调基因,494个下调基因。上调基因主要富集在细胞周期、信号通路、细胞因子-细胞因子受体相互作用等通路,下调基因主要富集在神经活性配体-受体相互作用、细胞色素P450代谢、cAMP信号等通路。Cox法确定了10个生存期相关的差异表达基因,并构建多基因预后模型,具有良好的预测效能。Cox分析结果显示,预后模型为胃癌患者预后的独立因素。 结论基于生物信息学构建的多基因预后模型对胃癌患者具有良好的预后预测效能,可能成为胃癌临床预后评估的指标以及靶向治疗的新靶点。 |
| 英文摘要: |
| AimTo explore the key genes and pathways in the occurrence and development of gastric cancer (GC), and to construct a prognostic signature for GC patients. MethodsThe gene expression data and clinical indicators of GC and normal adjacent tissues were collected from the cancer genome atlas (TCGA) database. The gene expression differences were analyzed by R package, and gene function enrichment analysis was performed. Multivariate Cox proportional risk regression analysis was used to construct prognosis prediction signature. ROC curve software package was used to test the accuracy of the prediction model and draw a rotograph. Results930 DEG were identified in GC tissues and adjacent normal tissues, including 436 up-regulated genes and 494 down-regulated genes. Up-regulated genes were mainly enriched in cell cycle, signaling pathway and cytokine-cytokine receptor interaction, while down-regulated genes were mainly enriched in neural active ligand-receptor interaction, metabolism of cytochrome P450 to exogenous substances, cAMP signaling pathway and other pathways. Cox regression algorithm was used to identify 10 DEG associated with overall survival, and a multi-gene prognosis signature was established. The prognostic signature was positively correlated with tumor grade and stage, survival curve and time-dependent ROC. Cox regression analysis showed that the prognostic signature was an independent factor affecting the prognosis of GC patients. ConclusionThe multi gene prognostic model based on bioinformatics has good prognostic prediction efficiency for gastric cancer patients, and may become an new target for clinical prognosis evaluation of gastric cancer and a new target for targeted therapy. |
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