Lv Ji, Guo Ying, Yan Lili, Lu Yang, Liu Dongfeng, Niu Jia
Department of Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China.
Department of Obstetrics and Gynecology, Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, Hebei, China.
J Cell Biochem. 2020 Aug;121(8-9):3780-3793. doi: 10.1002/jcb.29518. Epub 2019 Nov 3.
Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients' overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.
在包括结肠癌在内的大量人类癌症中,已发现长链非编码RNA(lncRNA)失调。因此,实施具有预后预测价值的潜在lncRNA生物标志物非常重要。从基因表达综合数据库下载GSE39582数据集。通过NetAffx注释文件对lncRNA表达谱进行重新注释分析。单变量和多变量Cox比例分析有助于选择预后lncRNA。采用随机生存森林变量搜索(RSF-VH)算法并结合逐步多变量Cox比例分析来建立lncRNA特征。进行对数秩检验以分析和比较患者总生存期(OS)的Kaplan-Meier生存曲线。采用受试者工作特征(ROC)分析,基于lncRNA风险评分比较生存预测的特异性和敏感性,随后计算曲线下面积(AUC)值。采用单样本基因集富集分析(ssGSEA)来描述与该特征相关的生物学功能。最后,为了确定该模型的稳健性,我们使用了包括GSE17536和癌症基因组图谱数据集在内的验证集。对lncRNA进行重新注释分析后,通过单变量和多变量Cox比例分析共获得14个lncRNA探针。然后,RSF-VH算法和逐步多变量Cox分析有助于构建结肠癌的五lncRNA预后特征。高风险组患者的生存时间明显短于低风险组患者,AUC为0.75。此外,五lncRNA特征可用于独立预测结肠癌患者的生存情况。ssGSEA分析显示,随着风险评分的增加,细胞外基质-受体相互作用等通路被激活。这些发现确定了该五lncRNA特征对结肠癌预后预测价值的强大能力。