Objective To construct N6-methyladenosine related long non-coding RNA(LncRNA)pairing model for renal cell carcinoma based on bioinformatics analysis of the cancer ganome atlas(TCGA)database and to explore its prognosis value.Methods Transcriptome data of RNA-sep for renal cell carcinoma and its related clinical information were downloaded from the TCGA database.Perl software was used to organize and separate LncRNA and messenger RNA(mRNA)from the transcriptome data.A total of 564 tissues from renal cell carcinoma cases and 72 normal tissues were obtained,and thus 540 renal cancer patients were eventually included.Random data table method was used to divide 540 patients with renal cancer into a training group(n=275)and a validation group(n=265)by caret.M6A related LncRNA pairing models were established based on the single factor and multivariate COX regression analysis.The risk assessment equation was obtained using the LASSO regression algorithm.The risk scores were calculated based on this equation,and the optimal critical point of the median risk value was applied to divide all patients into high-risk and low-risk groups.Kaplan-Meier survival analysis was used to make a survival curve for the differences between high and low risk groups in the overall sample.The gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses were conducted using the Cluster Profiler software package.The relationship between N6-methyladenosine related LncRNA pairing model and immune cell infiltration was analyzed by R software.Results Kaplan-Meier survival analysis showed the total survival time of patients in the low-risk group was significantly higher than that of patients in the high-risk group of the training group(P<0.05).Compared with high risk group,the overall survival time of patients(G1～2,G3～4,Ⅰ～Ⅱ,or Ⅲ～Ⅳ,age≤65 years,or patients>65 years old)in low risk group was higher(P<0.05).Differential gene enrichment analysis was obtained for high and low risk groups,which mainly enriched with many differential genes such as muscle contraction,rhabdomytic cell differentiation,myofibril,receptor activation activity,and vascular smooth muscle contraction.The highest driver genes in high risk group and low risk group exhibited mutation frequency and mutation information,and their risk score was positively correlated with the degree of T cell and plasma cell infiltration(r=0.638,P=0.001).Conclusion Bioinformatics-based analysis of the N6-methyladenosine related LncRNA pairing models can be helpful to predict the prognosis of patients with renal cancer.It provides new ideas for the prognosis evaluation and optimal treatment strategy of renal cancer,and contributes to further analyzing the molecular mechanism of the occurrence and development of gastric cancer in the future.