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核不均一核糖核蛋白C在肺腺癌中的表达及对临床预后预测价值
作者姓名:高军  朱珍  李虹  林欣  宋一祎  孔志斌
作者单位:1. 201306 上海交通大学附属第六人民医院病理科2. 201306 上海交通大学附属第六人民医院呼吸内科
基金项目:国家自然科学基金(82004432)
摘    要:目的探讨核不均一核糖核蛋白C(HNRNPC)在肺腺癌(LUAD)中的表达及对临床预后的意义。 方法通过癌症基因组图谱数据库(TCGA)及临床蛋白质组肿瘤分析协作组(CPTAC)公共数据库,收集535例LUAD患者的临床病理资料、HNRNPC mRNA表达数据及111例LUAD患者HNRNPC蛋白表达数据,通过基因型-组织表达数据库(GETx)收集288例健康人肺组织HNRNPC mRNA表达数据。以HNRNPC mRNA中位数值为界值,将LUAD患者分为高表达组和低表达组,利用R语言比较LUAD组织与正常肺组织HNRNPC mRNA及蛋白水平的差异,分析HNRNPC mRNA高低表达与患者临床病理特征及预后的关系。运用受试者工作特征(ROC)曲线分析HNRNPC表达对LUAD的诊断价值;通过基因集富集分析(GSEA)HNRNPC调控的相关信号通路。 结果HNRNPC mRNA在LUAD组织中的表达明显高于正常组织,差异具有统计学意义(均P<0.01);LUAD组织中HNRNPC蛋白水平也显著上调(P<0.05)。HNRNPC表达水平与LUAD患者吸烟史、临床分期、T分期、远处转移有关(均P<0.05)。Log-rank检验结果显示,HNRNPC高表达组LUAD患者中位生存期(42.3个月)明显低于低表达组患者(59.9个月),差异具有统计学意义(P<0.01)。多因素Cox回归分析显示,淋巴结转移和HNRNPC mRNA表达水平是LUAD患者预后的独立影响因素。ROC曲线示HNRNPC mRNA表达水平对诊断LUAD具有一定的预测效能,AUC为0.843,95%CI:0.817~0.869。GSEA功能富集分析示Ras相似物GTP酶(Rho GTPases)途径、TP53信号途径、DNA损伤修复、细胞周期及M期调控在HNRNPC高表达组明显富集。 结论HNRNPC在LUAD组织中表达显著上调,高表达患者预后不良,HNRNPC可能是评估LUAD患者诊断与预后的新的潜在生物学分子标志物。

关 键 词:核不均一核糖核蛋白C  肺腺癌  生物信息学  预后  
收稿时间:2021-12-16

Heterogeneous nuclear ribonucleoprotein C expression in lung adenocarcinoma and its prognostic value
Authors:Jun Gao  Zhen Zhu  Hong Li  Xin Lin  Yiyi Song  Zhibin Kong
Institution:1. Department of Pathology, Sixth People′s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 201306, China2. Department of Respiratory Medicine, Sixth People′s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 201306, China
Abstract:ObjectiveTo investigate the expression of heterogeneous nuclear ribonucleoprotein C(HNRNPC) and discuss its clinical significance in lung adenocarcinoma(LUAD). MethodsThe Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) public databases were used to collect clinicopathological data and HNRNPC mRNA expression data from 535 patients with LUAD, as well as protein expression data from 111 patients with LUAD. The Genotype-tissue expression (GETx) database was used to obtain HNRNPC mRNA expression data from 288 healthy lung tissues. Based on the median value of HNRNPC mRNA level as the boundary value, LUAD patients were divided into high expression group and low expression group. By the R programming language, the difference of mRNA and protein levels of HNRNPC in LUAD and normal lung tissues were compared, and the link between HNRNPC mRNA expression and clinicopathological characteristics and patient prognosis was investigated. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of HNRNPC expression in lung adenocarcinoma. Gene set enrichment analysis (GSEA) was used to look into the relevant signaling pathways regulated by HNRNPC. ResultsThe expression of HNRNPC mRNA in LUAD was significantly higher than that in normal lung tissues (all P<0.01). The protein expression level of HNRNPC in LUAD was also significantly up-regulated (P<0.05). Univariate analysis showed that the expression of HNRNPC mRNA was correlated with smoking history, clinical stage, T stage and distant metastasis(all P<0.05). Log-rank test showed that the median survival time of patients with LUAD in the HNRNPC high expression group (42.3 months) was significantly lower than that in the low expression group (59.9 months) (P<0.01). The multivariate Cox regression analysis revealed that lymph node metastasis and the high expression of HNRNPC mRNA were independent prognostic factors of LUAD. ROC curve analysis showed that HNRNPC mRNA expression level could predict the diagnosis of LUAD (AUC=0.843, 95%CI=0.817-0.869). GSEA showed that Rho GTPases pathway, TP53 signaling pathway, DNA repair, cell cycle and M phase regulation were significantly enriched in HNRNPC highly expressed phenotype. ConclusionsThe expression level of HNRNPC in LUAD is significantly higher than that in normal lung tissue, and high HNRNPC expression group indicates poor prognosis in patients with LUAD. HNRNPC maybe a novel potential biomarker for the diagnosis and prognosis of patients with LUAD.
Keywords:Heterogeneous nuclear ribonucleoprotein C  Lung adenocarcinoma  Bioinformatics  Prognosis  
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