Signature Micrornas And Long Noncoding Rnas In Laryngeal Cancer Recurrence Identified Using A Competing Endogenous RNA Network

BackgroudLaryngeal cancer is one of the commonest cancer in the head and neck region.Squamous cell carcinoma is the predominant physiological type of laryngeal cancer,as it originates from the glottic region of the larynx.Official data from the World Health Organization(WHO)show that although the incidence and mortality of laryngeal cancer have declined slightly in recent years,the number of cases has been rising steadily.Because of the morbidity associated with laryngeal cancers and its multimodality treatment,clinical research emphasis over the last decades has focused on efforts to preserve laryngeal function through improvements in single-modality treatment of patients with limited cancers and increasing use of combinations of chemotherapy and radiotherapy in the majority of patients with advanced disease as an alternative to total laryngectomy.Unfortunately,these efforts have not met with improvements in overall survival rates even though preservation of laryngeal function can be achieved in > 50% of patients.In the United States laryngeal cancer is one of a few oncologic diseases in which the5-year survival rate has decreased over the past 40 years,from 66% to 63%.The main reason of treatment failure in laryngeal cancer is recurrence.Recurrent laryngeal cancer have a very poor prognosis.Understanding of the pathogenesis of the disease may contribute to the development of novel and more effective therapeutic strategies.ObjectiveThe aim of the present study was to identify novel microRNA(miRNA)or long noncoding RNA(lncRNA)signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy.MethodsDatasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas(TCGA)database as training dataset.The gene expression datasets GSE27020 and GSE25727 were downloaded from the Gene Expression Omnibus(GEO)database and used as validation datasets.The differentially expressed RNAs,including differentially expressed mRNAs(DEGs),differentially expressed lncRNAs(DE-lncRs)and differentially expressed miRNAs(DE-miRs),between recurrent and nonrecurrent laryngeal cancer samples were analyzed.The DE-lncRs that were significantly differentially expressed between recurrent and nonrecurrent samples were used to perform univariate Cox regression analysis,which was used to select the lncRNAs associated with recurrence.Kaplan-Meier analysis was performed to examine the association between the upregulated or downregulated lncRNAs,and recurrence and survival status.The identified DE-lncRs,DEGs and DE-miRs between recurrent andnonrecurrent samples were included in these regulatory interactions to obtain the lncRNA-miRNA and miRNA-mRNA regulatory networks.Subsequently,the interactions identified were combined into a competing endogenous RNA(ceRNA)regulatory network.Feature genes in the miRNA-mRNA network were identified via topological analysis and a recursive feature elimination algorithm.The candidate genes that were significantly differentially expressed between recurrent and nonrecurrent samples were selected,and the unsupervised clustering classification method was used to validate the sample classification performance of these feature-coding genes.The 100 DEGs exhibiting the highest betweenness centrality(BC)values were used to identify the optimal feature-coding gene set using the recursive feature elimination(RFE)algorithm.Through the iterative random feature combination,classification assessment and determination of the performance of various samples,the optimal feature-coding gene set was obtained.Subsequently,the set of optimal feature-coding genes was used to construct an SVM classifier,which considered the expression levels of the feature genes within the samples as the feature value to classify and distinguish the samples.The recurrence status of the samples was predicted using the SVM classifier.The classification effect was tested using two validation datasets.Furthermore,the candidate feature coding genes selected from the ceRNA network were analyzed using Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)to investigate the enriched molecular functions and signaling pathways in recurrent laryngeal cancer.ResultsA total of 21 DE-lncRs and 507 DEGs were identified between recurrent and nonrecurrent samples(FDR<0.05).In addition,55 DE-miRs were identified(p<0.05).The lncRNA-miRNA regulatory network comprised four DE-lncRs associated with recurrence and seven DE-miRs.An miRNA-mRNA regulatory network was established comprising six miRNAs and 193 mRNAs.The SVM classifier exhibited an accuracy of 94.05%(79/84)for sample classification prediction in the TCGA dataset,and 92.66 and 91.07% in the two validation datasets.The ceRNA regulatory network comprised 203 nodes,corresponding to mRNAs,miRNAs and lncRNAs,and 346 lines,corresponding to the interactions among RNAs.The multiple hypotheses testing correction results for the GO molecular function and KEGG pathway categories were not statistically significant(data not shown).The ceRNA regulatory network corresponding to the 32 optimal feature coding genes was established and consisted of six DE-miRs,four DE-lncRs and32 feature-coding genes.In this ceRNA network,the interactions between HLA complex group 4(HCG4)-miR-33 b,HOX transcript antisense RNA(HOTAIR)-miR-1-MAGE family member A2(MAGEA2),EMX2 opposite strand/antisense RNA(EMX2OS)-miR-124-calcitonin related polypeptide α(CALCA)and EMX2OS-miR-124-γ-aminobutyric acid type A receptor γ2subunit(GABRG2)exhibited the highest scores.The SVM classifier based on32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples.The Kaplan-Meier survival curves of these six DE-miRs suggested asignificant association between recurrence-free survival times and the expression levels of the miRNAs examined,including hsa-miR-33 b,hsa-miR-124,hsa-miR-133 a and hsa-miR-208a(p<0.05).ConclusionIn conclusion,an SVM classifier composed of 32 feature-coding genes classified recurrent and nonrecurrent laryngeal cancer samples with high accuracy.miR-1,miR-33 b,miR-124,HOTAIR,HCG4 and EMX2 OS may represent a signature of noncoding RNAs in recurrent laryngeal cancer.The interactions HCG4-miR-33 b,HOTAIR-miR-1-MAGEA2 and EMX2OSmiR-124-CALCA/GABRG2 may be important in the regulation of the molecular mechanisms underlying the development of recurrent laryngeal cancer.