| Background:Long non-coding RNAs(lncRNAs)are important regulators of tumor immune microenvironment,and lncRNAs can participate in transcriptional regulation through the lncRNA-TF-gene regulatory network.Objective:In this study,by analyzing lncRNAs that are differentially expressed and related to immunity in clear cell Renal cell carcinoma(ccRCC),to find biomarkers that are closely related to the diagnosis and prognosis of ccRCC,and to construct a prognostic model based on the clinical characteristics of patients.Methods:This study extracted high-throughput sequencing data of ccRCC from The Cancer Genome Atlas(TCGA),combined with the LncMAP database and ImmPort database,and screened out differentially expressed lncRNAs that may be related to tumor immunity.The prognostic significance of lncRNAs was preliminarily evaluated using univariate Cox regression analysis.The lncRNAs related to the prognosis was further screened by LASSO Cox regression,and then the regression coefficient was calculated by the multivariate Cox regression analysis.The risk score formula was used to calculate the patient risk score,and divides ccRCC patients into high-risk and low-risk groups.Respectively,the samples of the TCGA dataset are divided into a training set and a testing set,which are used to construct and verify the prognostic risk model.Results:The expressions of CTA-384D8.34,CTD-2035E11.5,FIRRE and RP11-348J24.2 were correlated with T stage,metastasis,clinical stage and pathological grade of the tumor(P<0.05),and four-lncRNA-based risk score shows a higher prognostic value for patients with ccRCC.In addition,we divided cases into two groups(high-risk group and low-risk group)based on the risk score.The K-M survival curve showed that overall survival was significantly lower in the high-risk group than in the low-risk group(P<0.001).CD8+T cells were associated with poor prognosis,and patients in the high-risk group had higher immune cell score(P<0.05).The area under the ROC curve for the 3-year,5-year,and 7-year overall survival rates of the training set patients predicted by the risk score were 0.748,0.781,and 0.749,respectively.Moreover,in the testing set and the entire set,the risk score also showed a favorable predictive performance for the overall survival of the patient.The risk score and clinical stage of the tumor are independent prognostic factors for patients with ccRCC.Among the three sets,the area under the ROC curve predicted by the risk score to the overall survival rate of patients is the largest,followed by the tumor stage,age,and tumor pathological grade.Based on these four factors,a prognostic model was constructed and a nomogram was drawn.In the training set,the areas under the ROC curve of the 3-year,5-year,and 7-year overall survival rates for the ccRCC patients were 0.791,0.809,and 0.767.Similarly,the predictive value of the patient was validated on the testing set and the entire set.The three sets of calibration charts show the model have a good agreement between predicted probability and actual probability of patient survival rates,and actual survival rates is closely related to predicted survival rates.Conclusion:We have developed a prognostic model that includes multiple predictors including patient age,tumor pathological grade,tumor clinical stage,and four-lncRNA-based risk score,and may be able to predict patient prognosis,which may help patient consultation and Personalized management of different patients.In addition,these four lncRNAs have potential as novel prognostic biomarkers for ccRCC. |
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