基于 8 个基因的新型风险评分模型和列线图预测骨肉瘤患者的总生存率。
A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma.
机构信息
Departments of Hand Surgery, The Third Hospital of Jilin University, Changchun, Jilin Province, China.
Departments of Orthopedics, The Third Hospital of Jilin University, Changchun, Jilin Province, China.
出版信息
BMC Cancer. 2020 May 24;20(1):456. doi: 10.1186/s12885-020-06741-4.
BACKGROUND
This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients.
METHODS
A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted.
RESULT
Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway.
CONCLUSION
An eight-gene predictive model and nomogram were developed to predict OS prognosis.
背景
本研究旨在建立一个预测模型，以预测骨肉瘤（OS）患者的生存结局。
方法
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）分别获取了一个 RNA 测序数据集（训练集）和一个微阵列数据集（验证集）。在训练集中鉴定转移性和非转移性 OS 样本之间的差异表达基因（DEGs）。通过支持向量机（SVM）递归特征消除筛选与预后相关的 DEGs，并进行优化。构建 SVM 分类器，以对转移性和非转移性 OS 样本进行分类。通过多元回归分析提取独立预后基因，构建风险评分模型，然后通过 Kaplan-Meier（KM）分析在两个数据集进行性能评估。识别独立的临床预后指标，然后进行列线图分析。最后，进行生存相关基因的功能分析。
结果
总共筛选出 345 个 DEGs 和 45 个与预后相关的基因。SVM 分类器可区分转移性和非转移性 OS 样本。一个由八个基因组成的特征是独立的预后标志物，可用于构建风险评分模型。该风险评分模型可将两个数据集（训练集：log-rank p < 0.01，C 指数= 0.805；验证集：log-rank p < 0.01，C 指数= 0.797）中的 OS 样本分为高低风险组。肿瘤转移和 RS 模型状态是独立的预后因素，列线图模型对 OS 的生存预测具有较高的准确性。此外，生存相关基因的功能分析表明，它们与免疫反应和细胞因子-细胞因子受体相互作用途径密切相关。
结论
建立了一个由八个基因组成的预测模型和列线图，用于预测 OS 的预后。
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