The incidence rate of renal cell carcinoma (RCC) is about 3% of all adult cancers. Of these, the Kidney clear cell renal cell carcinoma (KIRC) is the most common type, accounting for about 70%-75% of RCC. KIRC is difficult to be detected in time clinically. KIRC still has no effective treatment at this stage. We combined high-throughput bioinformatics analysis to obtained the structural sequence transcriptome data, relevant clinical information, and m6 A gene map of KIRC patients from genomics TCGA database. Pearson's correlation analysis was used to explore m6 A related gene long noncoding RNAs (lncRNAs), and then univariate Cox regression analysis was performed to screen the prognostic role of KIRC patients. Lasso-Cox regression was performed to establish the lncRNAs risk model associated with m6 A.LINC02154 and AC016773.2, Z98200.2, AL161782.1, EMX2OS, AC021483.2, CD27-AS1, AC006213.3 were iidentif. Compared with the low-risk group, the overall survival of patients in the high-risk group was significantly worse. Analyzing whether there are differences in immune cells between high-risk and low-risk subgroups. There were CD4 memory resting, Monocytes, Macrophages M1, Dendritic cells activated, Mast cells resting, which had higher infiltrations in the low-risk group. We performed Go enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and gene set enrichment analysis enrichment analysis. Overall, our results suggest that the component of m6A-related lncRNAs in the prognostic signal may be a key mediator in the immune microenvironment of KIRC, which represents a promising therapeutic effect.
Keywords: long noncoding RNA (lncRNA); prognostic model; renal clear cell carcinoma; risk score.
© 2022 International Federation for Cell Biology.