Identification Of LncRNAs For Predicting Prognosis Of Oral Squamous Cell Carcinoma Based On Bioinformatics Analysis

Background:Oral cancer is one of the most common malignancies in the world,and oral squamous cell carcinoma(OSCC)is the most common type of Oral cancer.According to the statistics,oral cancer is the 8th most common cancer in male and the 14 th most common cancer in female.The main treatments for OSCC include surgery,radiotherapy and chemotherapy.About half of the patients had metastases by the time they were diagnosed with OSCC,the metastasis of the tumor is one of the important factors of poor prognosis.In the past few decades,the survival rate and prognosis of OSCC patients have not significantly improved.Therefore,exploring biomarkers that can predict the prognosis of OSCC has great significance.It will facilitate the development of individualized therapy and provide potential therapeutic targets.Long non-coding RNA(lnc RNA)has been proved to play an important role in a variety of tumorigenesis processes,participating in promoting or inhibiting tumorigenesis.Studies have shown that a variety of lnc RNAs are abnormally expressed in OSCC and play a role in tumor metastasis and invasion.Therefore,lnc RNA as a biomarker of tumor has great significance and will be beneficial to the treatment and prognosis of cancer.Although a large number of studies on lnc RNA have been carried out in OSCC,there is no effective biomarker to predict the prognosis of OSCC patients currently.Objective: Based on The TCGA(The Cancer Genome Atlas)database,used bioinformatics methods to search for reliable lnc RNA as a biomarker to predict the prognosis of OSCC patients.The expression of lnc RNA in cancer and adjacent tissues of OSCC patients was detected by q PCR.The co-expression network of differentially expressed lnc RNAs(DElnc RNAs)and differentially expressed protein-coding RNA(DEpc RNAs)was constructed to screen potential therapeutic targets of OSCC.Methods: Downloaded the m RNA expression profile data of OSCC and corresponding clinical data from TCGA database in this study.By analyzing and processing,screen out DElnc RNAs and DEpc RNAs in cancer tissues and normal tissues.The R package pheatmap was used to generate heat maps and volcano maps to visualize DElnc RNAs.The survival data were extracted by perl script tool,the clinical data were combined with the expression of DElnc RNAs,the univariate R was used to establish a univariate Cox regression model.A multivariate Cox regression model was constructed by using Delnc RNAs(P<0.01)from univariate Cox regression analysis,HR and 95% confidence interval(CI)of the model were calculated.Patients were divided into high-risk group and low-risk group according to the median risk score.In order to evaluate the predictive ability of Cox regression model,plotted the receiver operating characteristic(ROC)curve,calculated the area under ROC(AUC)and calculated the C-index.The Kaiter-Meier survival curve was plotted for the survival analysis of the DElnc RNAs screened by multivariate Cox regression analysis,and several lnc RNAs were finally determined as biomarkers to predict the prognosis of OSCC patients.QPCR was used to detect the expression of finally selected lnc RNAs in clinical samples.Predictive target gene and functional enrichment analysis were performed.The co-expression network of DElnc RNA and DEpc RNA was established by using WGCNA method,the potential therapeutic targets of OSCC were selected.Results:(1)The data downloaded from the TCGA database included 19 normal tissues and 261 tumor tissues.A total of 1296 DElnc RNAs were screened,916 lnc RNAs were up-regulated and 380 lnc RNAs were down-regulated.(2)There are 44 significant DElnc RNAs were screened by univariate Cox regression analysis(P<0.05),27 DElnc RNAs were low risk and 14 DElnc RNAs were high risk.5 DElnc RNAs with P<0.01 were selected to construct a multivariate Cox regression model.The area under the ROC curve(AUC)was 0.728 and the C-index was 0.64,indicating that the model has credibility.By multivariate Cox regression analysis,4 DElnc RNAs were considered as candidate prognostic signals for predicting survival time in OSCC patients.(3)Kaplan-Meier method was used to analyze the overall survival of 4 candidate DElnc RNAs.The results showed that the 4 candidates DElnc RNAs: CDKN2A-DT,PRKG1-AS1,AC009226.1 and LINC00689 were significantly correlated with the survival of OSCC patients.CDKN2A-DT and LINC00689 were prognostic protective factors,AC009226.1 and PRKG1-AS1 were prognostic risk factors.(4)Tissue q PCR showed that the expression level of AC009226.1,CDKN2A-DT,PRKG1-AS1 in cancer tissues were higher than normal tissues(P<0.05),and the expression levels of LINC00689 were lower than normal tissues(P<0.05).(5)The target gene prediction of 4 lnc RNAs showed that CDKN2A-DT had 2significantly positive correlation target genes,and PRKG1-AS1 had only one positive correlation target gene.AC00926.1 had 121 significantly correlated target genes,including 62 positively correlated genes and 59 negatively correlated genes.LINC000689 had 219 significantly correlated target genes,including 213 positively correlated genes and 6 negatively correlated genes.The GO functional enrichment analysis of the AC009226.1 target genes revealed that most of the genes had enriched into DNA-binding transcription activator activity,actin-dependent ATPase activity etc.The enrichment analysis of KEGG pathway revealed that most genes were also involved in focal adhesion,PI3K-Akt signaling pathway and Apelin signaling pathway.GO functional annotation analysis of the LINC00689 target genes revealed that most of the genes were enriched in metal ion transmembrane transporter activity,voltage gated ion channel activity,potassium channel activity.The enrichment analysis of KEGG pathway revealed that most genes were also involved in metabolism pathway and calcium signaling pathway etc.(6)The co-expression network of DElnc RNAs and DEpc RNAs was established by WGCNA method.The survival analysis of the genes in the modules with the highest correlation showed that five genes,AL117338.1,LCMT1.AS2,OPRPN,RSPH4 A and TESC could be used as the potential therapeutic targets of OSCC.Conclusion:(1)The following 4 lnc RNAs: CDKN2A-DT,PRKG1-AS1,AC009226.1 and LINC00689 can be used as biomarkers to predict the prognosis of OSCC patients.CDKN2A-DT and LINC00689 are prognostic protective factors,while AC009226.1 and PRKG1-AS1 are prognostic risk factors.(2)The functional analysis of the LINC00689 and AC009226.1 target genes revealed that they play a role in multiple biological functions and signaling pathways in the development of OSCC.(3)Five genes,AL117338.1,LCMT.AS2,OPRPN,RSPH4 A and TESC were selected as potential therapeutic targets of OSCC by establishing co-expression network of DElnc RNA and DEpc RNA.