m6A-related lncRNAs predict prognosis and indicate immune microenvironment in acute myeloid leukemia

• Published in: Scientific reports 2022-02, Vol.12 (1), p.1759-1759, Article 1759
• Main Authors: Zhong, Fangmin, Yao, Fangyi, Cheng, Ying, Liu, Jing, Zhang, Nan, Li, Shuqi, Li, Meiyong, Huang, Bo, Wang, Xiaozhong
• Format: Article
• Language: English
• Subjects: 631/114; 631/250; 631/337; 631/67; Acute myeloid leukemia; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Computational Biology - methods; Correlation analysis; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Humanities and Social Sciences; Humans; Immunotherapy; Leukemia; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - immunology; Leukemia, Myeloid, Acute - pathology; Leukemia, Myeloid, Acute - therapy; Malignancy; Medical prognosis; Metastases; Methyltransferases - metabolism; Microenvironments; multidisciplinary; N6-methyladenosine; Non-coding RNA; Patients; Polymerase chain reaction; Prediction models; Prognosis; Regression analysis; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; Science; Science (multidisciplinary); Survival; Tumor Microenvironment; Tumorigenesis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-05797-5

Acute myeloid leukemia (AML) is a complex hematologic malignancy. Survival rate of AML patients is low. N 6-methyladenosine (m 6 A) and long non-coding RNAs (lncRNAs) play important roles in AML tumorigenesis and progression. However, the relationship between lncRNAs and biological characteristics of AML, as well as how lncRNAs influence the prognosis of AML patients, remain unclear. In this study. In this study, Pearson correlation analysis was used to identify lncRNAs related to m 6 A regulatory genes, namely m 6 A-related lncRNAs. And we analyzed their roles and prognostic values in AML. m 6 A-related lncRNAs associated with patient prognosis were screened using univariate Cox regression analysis, followed by systematic analysis of the relationship between these genes and AML clinicopathologic and biologic characteristics. Furthermore, we examined the characteristics of tumor immune microenvironment (TIME) using different IncRNA clustering models. Using LASSO regression, we identified the risk signals related to prognosis of AML patients. We then constructed and verified a risk model based on m 6 A-related lncRNAs for independent prediction of overall survival in AML patients. Our results indicate that risk scores, calculated based on risk-related signaling, were related to the clinicopathologic characteristics of AML and level of immune infiltration. Finally, we examined the expression level of TRAF3IP2-AS1 in patient samples through real-time polymerase chain reaction analysis and in GEO datasets, and we identified a interaction relationship between SRSF10 and TRAF3IP2-AS1 through in vitro assays. Our study shows that m 6 A-related lncRNAs, evaluated using the risk prediction model, can potentially be used to predict prognosis and design immunotherapy in AML patients.