Open AccessConstruction and analysis of a competing endogenous RNA network to reveal potential prognostic biomarkers for Oral Floor Squamous Cell Carcinoma
Author(s) -
Wenjing Zhang,
Shuai Xu,
Laner Shi,
Zhangzhi Zhu,
Xinying Xie
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0238420
Subject(s) - competing endogenous rna , kegg , biology , survival analysis , proportional hazards model , microrna , computational biology , gene regulatory network , oncology , gene , bioinformatics , lasso (programming language) , transcriptome , medicine , gene expression , long non coding rna , rna , genetics , computer science , world wide web
Background Patients diagnosed with Oral Floor Squamous Cell Carcinoma (OFSCC) face considerable challenges in physiology and psychology. This study explored prognostic signatures to predict prognosis in OFSCC through a detailed transcriptomic analysis. Method We built an interactive competing endogenous RNA (ceRNA) network that included lncRNAs, miRNAs and mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to predict the gene functions and regulatory pathways of mRNAs. Least absolute shrinkage and selection operator algorithm (LASSO) analysis and Cox regression analysis were used to screen prognosis factors. The Kaplan-Meier method was used to analyze the survival rate of prognosis factors. Risk score was used to assess the reliability of the prediction model. Results A specific ceRNA network consisting of 56 mRNAs, 16 miRNAs and 31 lncRNAs was established. Three key genes (HOXC13, TGFBR3, KLHL40) and 4 clinical factors (age, gender, TNM, and clinical stage) were identified and effectively predicted the for survival time. The expression of a gene signature was validated in two external validation cohorts. The signature (areas under the curve of 3 and 5 years were 0.977 and 0.982, respectively) showed high prognostic accuracy in the complete TCGA cohort. Conclusions Our study successfully developed an extensive ceRNA network for OFSCC and further identified a 3-mRNA and 4-clinical-factor signature, which may serve as a biomarker.