The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer
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摘要:
目的 探索基于溶酶体相关基因的预后模型在膀胱癌患者中应用的可行性。 方法 通过下载癌症基因组图谱（the cancer genome atlas program，TCGA）数据库中膀胱癌数据和基因表达综合数据库（gene expression omnibus，GEO）中GSE13507数据集。利于R语言通过差异分析、单因素比例风险模型（COX）回归分析筛选出TCGA数据库中与膀胱癌生存相关的差异表达的溶酶体相关基因，采用最小绝对值收敛和选择算子算法（Lasso）回归模型构建出预后模型。根据构建模型风险评分的中位值划分出高、低风险组。使用生存分析比较高、低风险2组患者的生存差异并在GEO数据集中进行验证。采用单因素及多因素Cox回归分析验证风险评分是否为影响膀胱癌患者预后的独立危险因素。受试者工作特征曲线用于评估预后模型预测的准确性。GO及KEGG富集分析用于探索高、低风险组差异基因的生物学功能及信号通路。免疫分析用于探索高、低风险组免疫功能差异。 结果 共筛选出44个差异表达的溶酶体相关基因，其中9个与预后相关基因用于预后模型构建，生存分析显示低风险组预后明显优于高风险组（P < 0.05），并在GEO数据库中得到验证。构建模型预测膀胱癌患者1 a、3 a、5 a生存的ROC曲线下面积（area under the curve，AUC）分别为0.696、0.717、0.738。独立预后分析提示构建模型为膀胱癌患者独立预后影响因素；GO富集分析提示高、低风险组差异基因主要参与细胞结构功能相关；KEGG富集分析提示差异基因主要富集于PI3K-Akt信号通路。免疫分析提示2组患者免疫细胞浸润情况及免疫功能具有明显差异（P < 0.05）。 结论 膀胱癌溶酶体相关基因风险模型能准确有效地预测膀胱癌患者预后。 Abstract:Objective To explore the feasibility of applying the prognosis model based on lysosomal related genes（LRGs） in patients with bladder cancer （BC）. Methods Bladder cancer data were downloaded from The Cancer Genome Atlas （TCGA） program and dataset of GSE13507 were downloaded from Gene Expression Omnibus （GEO）. Differentially expressed LRGs related to the survival of BC in the TCGA database were screened by differential analysis and single factor proportional hazards model （COX） regression analysis via R software. LASSO regression was used to construct a prognostic risk model. BC patients were divided into the high and low risk groups according to the median risk score. Survival analysis were used to compare the survival differences between the high-risk and low-risk groups of BC patients, and validate in GEO database. Univariate and multivariate cox regression analysis were used to verify whether the risk scores were an independent risk factor affecting the prognosis of BC patients. The receiver operating characteristic （ROC） curve was used to evaluate the accuracy of prognostic model predictions. GO and KEGG enrichment analysis were used to explore the biological functions and signaling pathways of differentially expressed genes between the high-risk and low-risk groups. Immunoassay was used to explore the differences in immune function between the high-risk and low-risk groups. Results A total of 44 differentially expressed lysosomal related genes were screened, of which 9 genes related to the prognosis were used to construct the prognosis model in this study. Survival analysis showed that the prognosis of the low-risk group was significantly better than that of the high-risk group （P < 0.05）, which was verified in GEO database. The area under the ROC curve （AUC） of the BC prognosis risk scoring model to predict the 1-, 3- and 5-year survival of patients were 0.696, 0.717 and 0.738, respectively. Independent prognostic analysis showed that this prognostic risk model was an independent prognostic factor for BC patients. GO enrichment analysis indicated that the differential genes between the high-risk and low-risk groups were mainly involved in cell structure and function correlation. KEGG enrichment analysis suggested that the differential genes were mainly enriched in PI3K-Akt signaling pathway. Immunological analysis showed that there were significant differences in immune cell infiltration and immune function between the two groups. Conclusion Risk model of lysosomal related genes in BC can accurately and effectively predict the prognosis of BC patients. -
Key words:
- Bladder cancer /
- Lysosomal related genes /
- Prognostic model /
- Bioinformatics
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图 1 差异表达基因热图（左）及火山图（右）
A：差异表达基因热图；B：火山图。
Figure 1. Heat map （left） and volcanic map （right） of differentially expressed genes
图 3 lasso回归（左）和交叉验证（右）图形
Figure 3. Lasso regression （left） and cross-validation （right） graph
图 4 训练集和验证集的Kaplan-Meier分析曲线
Figure 4. Kaplan-Meier analysis curve of training and verification sets
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