Acute myeloid leukemia (AML) is a malignant tumor of the immature myeloid hematopoietic cells in the bone marrow (BM). It is a highly heterogeneous disease, with rising morbidity and mortality in older patients. Although researches over the past decades have improved our understanding of AML, its pathogenesis has not yet been fully elucidated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) are three noncoding RNA (ncRNA) molecules that regulate DNA transcription and translation. With the development of RNA-Seq technology, more and more ncRNAs that are closely related to AML leukemogenesis have been discovered. Numerous studies have found that these ncRNAs play an important role in leukemia cell proliferation, differentiation, and apoptosis. Some may potentially be used as prognostic biomarkers. In this systematic review, we briefly described the characteristics and molecular functions of three groups of ncRNAs, including lncRNAs, miRNAs, and circRNAs, and discussed their relationships with AML in detail.

Carnitine palmitoyl transferase 1A (CPT1A) protein catalyzes the rate-limiting step of Fatty-acid oxidation (FAO) pathway, which can promote cell proliferation and suppress apoptosis. Targeting CPT1A has shown remarkable anti-leukemia activity. But, its prognostic value remains unclear in Acute Myeloid Leukemia (AML). In two independent cohorts of cytogenetically normal AML (CN-AML) patients, compared to low expression of CPT1A (CPT1Alow), high expression of CPT1A (CPT1Ahigh) was significantly associated with adverse outcomes, which was also shown in European Leukemia Network (ELN) Intermediate-I category. Multivariable analyses adjusting for known factors confirmed CPT1Ahigh as a high risk factor. Significant associations between CPT1Ahigh and adverse outcomes were further validated whether for all AML patients (OS: P=0.008; EFS: P=0.002, n=334, no M3) or for National Comprehensive Cancer Network (NCCN) Intermediate-Risk subgroup (OS: P=0.021, EFS: P=0.024, n=173). Multiple omics analysis revealed aberrant alterations of genomics and epigenetics were significantly associated with CPT1A expression, including up- and down-regulation of oncogenes and tumor suppressor, activation and inhibition of leukemic (AML, CML) and immune activation pathways, hypermethylation enrichments on CpG island and gene promoter regions. Combined with the previously reported anti-leukemia activity of CPT1A's inhibitor, our results proved CPT1A as a potential prognosticator and therapeutic target for AML.

Background: The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. Methods: The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. Results: After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy. Conclusion: A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

The mammalian target of rapamycin (mTOR) inhibitor, DNA damage inducible transcript 4 (DDIT4), has inducible expression in response to various cellular stresses. In multiple malignancies, studies have shown that DDIT4 participates in tumorigenesis and impacts patient survival. We aimed to study the prognostic value of DDIT4 in acute myeloid leukaemia (AML), which is currently unclear. Firstly, The Cancer Genome Atlas was screened for AML patients with complete clinical characteristics and DDIT4 expression data. A total of 155 patients were included and stratified according to the treatment modality and the median DDIT4 expression levels. High DDIT4 expressers had shorter overall survival (OS) and event‐free survival (EFS) than the low expressers among the chemotherapy‐only group (all P < .001); EFS and OS were similar in the high and low DDIT4 expressers of the allogeneic haematopoietic stem cell transplantation (allo‐HSCT) group. Furthermore, in the DDIT4^high group, patients treated with allo‐HSCT had longer EFS and OS than those who received chemotherapy alone (all P < .01). In the DDIT4^low group, OS and EFS were similar in different treatment groups. Secondly, we analysed two other cytogenetically normal AML (CN‐AML) cohorts derived from the Gene Expression Omnibus database, which confirmed that high DDIT4 expression was associated with poorer survival. Gene Ontology (GO) enrichment analysis showed that the genes related to DDIT4 expression were mainly concentrated in the acute and chronic myeloid leukaemia signalling pathways. Collectively, our study indicates that high DDIT4 expression may serve as a poor prognostic factor for AML, but its prognostic effects could be outweighed by allo‐HSCT.