文献类型：期刊文献

英文题名：Identification of 17 mRNAs and a miRNA as an integrated prognostic signature for lung squamous cell carcinoma

作者：Zhang, Jingyun[1,2];Bing, Zhitong[1,2,3];Yan, Peijing[4];Tian, Jinhui[1,2];Shi, Xiue[5,6];Wang, Yongfeng[7];Yang, Kehu[1,2,4,6]

第一作者：Zhang, Jingyun

通信作者：Wang, YF[1];Yang, KH[2]

机构：[1]Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou, Gansu, Peoples R China;[2]Key Lab Evidence Based Med & Knowledge Translat G, Lanzhou, Gansu, Peoples R China;[3]Chinese Acad Sci, Inst Modern Phys, Dept Computat Phys, Lanzhou, Gansu, Peoples R China;[4]Gansu Prov Hosp, Inst Clin Res & Evidence Based Med, Lanzhou, Gansu, Peoples R China;[5]Gansu Rehabil Ctr Hosp, Lanzhou, Gansu, Peoples R China;[6]Gansu Evidence Based Rehabil Med Ctr, Lanzhou, Gansu, Peoples R China;[7]Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China

第一机构：Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou, Gansu, Peoples R China

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China;[2]corresponding author), Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou 730000, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2019

卷号：21

期号：8

外文期刊名：JOURNAL OF GENE MEDICINE

收录：;Scopus(收录号：2-s2.0-85070677130);WOS:【SCI-EXPANDED(收录号:WOS:000481908100004)】；

语种：英文

外文关键词：data mining; gene signatures; lung squamous cell carcinoma; meta-analysis; prognosis

摘要：Background Gene signatures for predicting the outcome of lung squamous cell carcinoma (LUSC) have been employed for many years. However, various signatures have been applied in clinical practice. Therefore, in the present study, we aimed to filter out an effective LUSC prognostic gene signature by simultaneously integrating mRNA and microRNA (miRNA). Methods First, based on data from the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga), mRNAs and miRNAs that were related to overall survival of LUSC were obtained by the least absolute shrinkage and selection operator method. Subsequently, the predicting effect was tested by time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival analysis. Next, related clinical indices were added to evaluate the efficiency of the selected gene signatures. Finally, validation and comparison using three independent gene signatures were performed using data from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo). Results Our data showed that the prognostic index (PI) contained 17 mRNAs and one miRNA. According to the best normalized cut-off of PI (0.0247), the hazard ratio of the PI was 3.40 (95% confidence interval = 2.33-4.96). Moreover, when clinical factors were introduced, the PI was still the most significant index. In addition, only two Gene Ontology terms with p < 0.05 were reported. Furthermore, validation implied that, using our 18-gene signature, only hazard ratio = 1.36 (95% confidence interval = 1.01-1.83) was significant compared to the other three groups of gene biomarkers. Conclusions The 18-gene signature selected based on data from the TCGA database had an effective prognostic value for LUSC patients.