Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma.
Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma.
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肺鳞状细胞癌蛋白质预后模型的构建和验证
DOI：
10.7150/ijms.47224
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发表时间：
2020
影响因子：
3.6
通讯作者：
Liu G
中科院分区：
文献类型：
--
作者：
Fang X;Liu X;Weng C;Wu Y;Li B;Mao H;Guan M;Lu L;Liu G
肺鳞状细胞癌(LUSCC)是肺癌的主要类型，具有较高的发病率和死亡率。与肺腺癌相比，LUSCC的预后指标少得多。此外，蛋白质生物标志物还具有经济、准确、稳定等优点。本研究的目的是构建LUSCC的蛋白质预后模型。LUSCC的蛋白质表达数据从癌症蛋白图谱(TCPA)数据库下载。LUSCC患者的临床资料从癌症基因组图谱(TCGA)数据库下载。根据TCPA和TCGA数据库，从325例LUSCC患者中鉴定出237种蛋白质。根据Kaplan-Meier生存分析、单因素和多因素Cox分析，建立了由6种蛋白(CHK1_pS345、Chk2、IRS1、paxlin、BRCA2和BRAF_pS445)组成的预后预测模型。根据风险模型中各蛋白的系数计算出每个患者的风险值后，将LUSCC患者分为高风险组和低风险组。生存分析显示，两组间差异有统计学意义(p=4.877e-05)。受试者工作特征曲线的曲线下面积(AUC值)为0.699，提示该预后风险模型可以有效地预测喉癌患者的生存。单因素和多因素分析表明，该模型可作为LUSCC患者的独立预后因素。蛋白质共表达分析表明，在风险模型中有21个蛋白质与这些蛋白质共表达。综上所述，本研究构建了一个能够有效预测LUSCC患者预后的蛋白质预后模型。
Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.
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影响因子：
5.9
作者：
Enroth, Stefan;Berggrund, Malin;Gyllensten, Ulf
通讯作者：
Gyllensten, Ulf
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