Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment
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摘要:
目的 基于生物信息学探究内吞体运输必需分选复合物（endosomal sorting complex required for transport，ESCRT）相关基因对骨肉瘤（osteosarcoma，OS）预后的价值评估。 方法 对基于TARGET数据库下载的88份骨肉瘤测序样本（死亡结局29例）数据及257份患者临床信息进行了预处理；并使用survival包构建Cox比例风险模型，筛选与生存相关的ESCRT基因。使用STRING数据库构建蛋白相互作用网络，并基于PPI筛选出核心基因；对筛选出的节点数5个以上的核心基因进行KEGG富集分析。运用Lasso回归分析技术，识别与骨肉瘤患者预后更为紧密相关的ESCRT相关基因。 结果 筛选到与ESCRT相关基因 1486 个；与生存相关的ESCRT基因164个。CLTC、MYC、INSR、PTPN1、TNFRSF1A可作为骨肉瘤患者预后相关的核心基因。将OS患者随机分为训练集（n = 44）和验证集（n = 44），在训练集中，被归为高风险组的骨肉瘤患者的总生存期明显短于被归为低风险组的患者（P < 0.05），验证集同样得到相似结果（P < 0.01）。ROC（receiver operating characteristic curve，ROC）曲线显示，在训练集中，AUC为0.846；在验证集中，AUC为0.877。对核心基因的预后生存分析与差异分析结果显示：在验证集中，MYC在高低风险组间无差异；在训练集中INSR无差异。在总的数据集中，所有预后核心基因均有差异（P < 0.05）。采用R包Survival对核心基因进行生存分析显示，除MYC基因生存率没有差异外（P > 0.05），其他4个基因CLTC、INSR、PTPN1、TNFRSF1A生存率均存在明显差异（P < 0.05）。经单因素独立预后分析，发现存在3个基因（TNFRSF1A、PTPN1、MYC）与骨肉瘤患者的生存存在相关性。经多因素独立预后分析并最终得到2个关键基因（TNFRSF1A、PTPN1）是影响骨肉瘤生存预后的独立因子，与骨肉瘤患者的生存预后密切相关。结论 应用生物信息学成功构建了基于TNFRSF1A、PTPN1 2个关键基因表达的骨肉瘤生存预后风险评分模型，可为骨肉瘤的临床治疗及生存预后评估提供更多的选择。 Abstract:Objective To evaluate the prognostic value of endosomal sorting complex required for transport （ESCRT）-related genes in osteosarcoma （OS） based on bioinformatics. Methods Preprocessing was performed on 88 OS sequencing samples （with 29 death outcomes） downloaded from the TARGET database and 257 patient clinical information. The Cox proportional hazards model was constructed using the survival package to screen ESCRT genes related to survival. The STRING database was used to construct a protein-protein interaction （PPI） network, and core genes were selected based on PPI. KEGG enrichment analysis was performed on the selected core genes with more than 5 nodes. Lasso regression analysis was applied to identify ESCRT-related genes more closely related to the prognosis of OS patients. Results A total of 1486 ESCRT-related genes were identified, of which 164 were associated with survival. CLTC, MYC, INSR, PTPN1, and TNFRSF1A were identified as core genes related to the prognosis of OS patients. OS patients were randomly divided into a training set （n = 44） and a validation set （n = 44）. In the training set, OS patients in the high-risk group had significantly shorter overall survival than those in the low-risk group （P < 0.05）, and similar results were obtained in the validation set （P < 0.01）. The ROC （receiver operating characteristic） curve showed an AUC of 0.846 in the training set and 0.877 in the validation set. Prognostic survival analysis and differential analysis of core genes revealed no difference in MYC between high- and low-risk groups in the validation set, and no difference in INSR in the training set. In the overall dataset, all prognostic core genes showed significant differences （P < 0.05）. Survival analysis of core genes using the R package Survival showed significant differences in survival rates for four genes （CLTC, INSR, PTPN1, TNFRSF1A） except MYC （P > 0.05）. Univariate independent prognostic analysis identified three genes （TNFRSF1A, PTPN1, MYC） associated with OS survival. Multivariate independent prognostic analysis ultimately identified two key genes （TNFRSF1A, PTPN1） as independent factors influencing OS survival prognosis and closely related to OS patient survival. Conclusion A risk scoring model for OS survival prognosis based on the expression of two key genes, TNFRSF1A and PTPN1, was successfully constructed using bioinformatics, providing more options for clinical treatment and survival prognosis assessment of OS.-
Key words:
- Osteosarcoma /
- ESCRT /
- TNFRSF1A /
- PTPN1 /
- Prognosis
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图 2 KEGG富集分析结果
A：核心基因通路富集表达列表；B：富集分析柱状图。
Figure 2. Results of KEGG enrichment analysis
图 3 lasso筛选骨肉瘤核心基因和交叉验证结果
A：交叉验证误差图；B：系数路径图。
Figure 3. lasso screen for osteosarcoma core genes and cross-validation results
图 4 预后核心基因的评估
A：训练集高低风险组的生存分析；B：验证集高低风险组的生存分析；C：训练集的ROC曲线（横坐标为假阳性率，纵坐标为真阳性率）；D：验证集的ROC曲线（横坐标为假阳性率，纵坐标为真阳性率）。
Figure 4. Evaluation of prognostic core genes
图 5 预后模型的评估
A：训练集的风险评分；B：训练集生存状态；C：训练集表达热图；D：验证集的风险评分；E：验证集生存状态；F：验证集表达热图。
Figure 5. Evaluation of prognostic models
图 6 预后核心基因在高低风险组中的差异分析
A：验证集中核心基因差异性分析；B：训练集中核心基因差异性分析；C：总数据集中核心基因差异性分析。
Figure 6. Differential analysis of prognostic core genes in high and low risk groups
图 7 预后核心基因的单基因生存分析
A：MYC基因生存曲线；B：CLTC基因生存曲线；C：INSR基因生存曲线；D：PTPN1基因生存曲线；E：TNFRSF1A基因生存曲线。
Figure 7. Single-gene survival analysis of prognostic core genes
图 8 预后核心基因的独立预后分析
A：单因素独立预后分析；B：多因素独立预后分析。
Figure 8. Independent prognostic analyses of prognostic core gene
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