The Relationship Between M5C Related LncRNA And The Prognosis Of Non-M3 Acute Myeloid Leukemia Was Analyzed Based On TCGA Database

Objective:Non-M3 acute myeloid leukemia(non-M3 AML)is a hematological malignancy with poor prognosis and high molecular heterogeneity.Long non-coding RNA(lncRNA)associated with 5-methylcytosine(m5C)play an important role in a variety of malignant diseases,but their relationship with the prognosis of non-M3 AML remains unclear.The purpose of this study was to use the data downloaded from The Cancer Genome Atlas(TCGA)database to construct the characteristics of lncRNAs associated with m5 C,in order to predict the prognosis of non-M3 AML patients and provide evidence for finding new therapeutic targets.Methods: 1.RStudio software was applied to collate,merge and screen the clinical data and RNA expression profile data of AML patients downloaded from the TCGA database,and the lncRNAs associated with m5 C were determined by Pearson correlation analysis.2.Univariate regression analysis was used to obtain m5C-related lncRNAs associated with prognosis,and ceRNA regulatory network was constructed based on the obtained lncRNAs.3.Lasso regression and multivariate Cox regression were used to further obtain lncRNAs with independent prognostic value and construct prognostic risk scoring models.4.Kaplan-Meier curve and ROC curve were drawn to evaluate the predictive performance of the model.5.The 1,2 and 3-year survival rates of non-M3 AML patients were quantitatively predicted by using column charts.6.GO and KEGG enrichment analysis were performed for differentially expressed genes between the two risk groups.Results: 1.A total of 980 lncRNAs associated with m5 C were obtained by Pearson correlation analysis.2.Univariate Cox regression analysis showed that a total of 20m5C-related lncRNAs were selected that were associated with prognosis.A ceRNA regulatory network containing 2 m5C-associated lncRNAs related to prognosis,12 mi RNAs and 11 m5 C regulatory genes was constructed.3.The samples were randomly divided into train and test data sets.The Lasso regression analysis results suggested that a total of 8 lncRNAs correlated with the overall survival time of patients with train data sets were obtained.4.Multivariate Cox regression analysis showed that 3 out of 8lncRNAs(AC008906.1,AC090152.1,URB1-AS1)had independent prognostic value in non-M3 AML,and prognostic risk scoring models were successfully constructed.5. According to the median risk score,the samples were divided into high and low risk groups,and the Kaplan-Meier method was used to draw a curve,which showed that the overall survival time of high and low risk groups was significantly different.ROC curve analysis shows that this model has good predictive performance.6.The 1,2 and 3-year survival probabilities of non-M3 AML patients were quantitatively predicted by the histogram.7.A total of 1444 differential genes were selected for the high-low risk group.The results of GO enrichment analysis were mainly concentrated in the biological process group,while the results of KEGG pathway enrichment analysis were mainly concentrated in protein digestion and absorption,proteoglycan in cancer,extracellular matrix-receptor interaction and other pathways.Conclusions: In this study,a novel prognostic risk score model for non-M3 AML was established and validated using m5C-related lncRNA,which provided new ideas for predicting the prognosis of non-M3 AML and finding therapeutic targets.