Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome.

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814-7.630) and 2.047 (1.013-4.138) in the training cohort and validation cohort, respectively. The AUC of 3-year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high-risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.

Screening for malignant tumor cells in serous effusions with an automatic hematology analyzer using a novel diagnostic algorithm.

Background: Due to the high false-positive rate of the high-fluorescence body fluid (HF-BF) cell parameter of the hematology analyzer in BF mode, a novel algorithm based on the Mindray BC-6800 Plus hematology analyzer (BC-6800Plus), with higher diagnostic accuracy compared to that of the traditional HF-BF algorithm, was used to screen for malignant tumor cells in clinical BF samples. In this study, the body fluid mode of BC-6800Plus was applied to investigate the ability of its available parameters and characteristic regional particles in tumor cells screening. Methods: A total of 220 BF samples (including pleural effusion and ascites) were randomly classified into a training cohort (154 samples) and a validation cohort (66 samples), and detected on the BC-6800Plus in BF mode. Based on the scatter plot analysis of the instrument, a novel gating algorithm, malignant cell algorithm-body fluid (MA-BF), was designed to detect the aggregated cells expressing highest fluorescence (FL) signals and side-scatter (SS) signals than other cells. BF collection and analyses were performed in compliance with the CLSI H56-A guideline. tumor cell-positive samples were defined as greater than or equal to confirIIIb (Papanicolaou class system) by the pathological examination. The diagnostic accuracy of HF-BF and MA-BF were determined by the receiver operating characteristic (ROC) curve analysis. Results: When the cutoff values of the absolute count (HF-BF#) and relative count (HF-BF%) were set as 0.022×109/L and 3.0%, respectively, the area under curve (AUC), sensitivity, and specificity were 0.76, 0.85 and 0.55 for HF-BF#, and were 0.70, 0.85, and 0.49 for HF-BF%, respectively. The new parameters, the absolute tumor cell count (MA-BF#) and relative count (MA-BF%), were established in the training cohort using the novel algorithm. We confirmed the cutoff values of MA-HF# and MA-HF% in BF were set as 0.006×109/L and 0.2% in the training cohort, respectively. In the validation cohort, the AUC, sensitivity, and specificity were 0.89, 0.93, and 0.78 for MA-BF#, and were 0.89, 0.87 and 0.75 for MA-BF%, respectively. Conclusions: The MA-BF parameters of the novel algorithm output had better diagnostic accuracy for BF tumor cells than the traditional HF-BF parameters.

An Immune Risk Score Predicts Survival of Patients with Acute Myeloid Leukemia Receiving Chemotherapy.

PURPOSE: Prediction models for acute myeloid leukemia (AML) are useful, but have considerable inaccuracy and imprecision. No current model includes covariates related to immune cells in the AML microenvironment. Here, an immune risk score was explored to predict the survival of patients with AML. EXPERIMENTAL DESIGN: We evaluated the predictive accuracy of several in silico algorithms for immune composition in AML based on a reference of multi-parameter flow cytometry. CIBERSORTx was chosen to enumerate immune cells from public datasets and develop an immune risk score for survival in a training cohort using least absolute shrinkage and selection operator Cox regression model. RESULTS: Six flow cytometry-validated immune cell features were informative. The model had high predictive accuracy in the training and four external validation cohorts. Subjects in the training cohort with low scores had prolonged survival compared with subjects with high scores, with 5-year survival rates of 46% versus 19% (P < 0.001). Parallel survival rates in validation cohorts-1, -2, -3, and -4 were 46% versus 6% (P < 0.001), 44% versus 18% (P = 0.041), 44% versus 24% (P = 0.004), and 62% versus 32% (P < 0.001). Gene set enrichment analysis indicated significant enrichment of immune relation pathways in the low-score cohort. In multivariable analyses, high-risk score independently predicted shorter survival with HRs of 1.45 (P = 0.005), 2.12 (P = 0.004), 2.02 (P = 0.034), 1.66 (P = 0.019), and 1.59 (P = 0.001) in the training and validation cohorts, respectively. CONCLUSIONS: Our immune risk score complements current AML prediction models.

Prognosis and regulation of an adenylyl cyclase network in acute myeloid leukemia.

We explored the roles of adenylyl cyclases (ADCYs) in acute myeloid leukemia (AML). Expression ADCYs in AML and their effect on prognosis was analyzed using data from Oncomine, GEPIA and cBioPortal databases. Frequently altered neighbor genes (FANGs) of ADCYs were detected using the 3D Genome Browser, after which the functions of these FANGs were predicted using Metascape tools. Cell viability and apoptosis were assessed using CCK-8 and Annexin V-FITC/PI kits. Expression levels of ADCYs were higher in AML cells lines and in bone marrow-derived mononuclear cells from AML patients than in control cells, and were predictive of a poor prognosis. A total of 58 ADCY FANGs were identified from the topologically associating domains on the basis of the Hi-C data. Functional analysis of these FANGs revealed abnormal activation of the MAPK signaling pathway. Drug sensitivity tests showed that fasudil plus trametinib or sapanisertib had a synergistic effect suppressing AML cell viability and increasing apoptosis. These findings suggest that dysregulation of ADCY expression leads to altered signaling in the MAPK pathway in AML and that the ADCY expression profile may be predictive of prognosis in AML patients.

The Role of the HOXA Gene Family in Acute Myeloid Leukemia.

The HOXA gene family is associated with various cancer types. However, the role of HOXA genes in acute myeloid leukemia (AML) have not been comprehensively studied. We compared the transcriptional expression, survival data, and network analysis of HOXA-associated signaling pathways in patients with AML using the ONCOMINE, GEPIA, LinkedOmics, cBioPortal, and Metascape databases. We observed that HOXA2-10 mRNA expression levels were significantly upregulated in AML and that high HOXA1-10 expression was associated with poor AML patient prognosis. The HOXA genes were altered in ~18% of the AML samples, either in terms of amplification, deep deletion, or elevated mRNA expression. The following pathways were modulated by HOXA gene upregulation: GO:0048706: embryonic skeletal system development; R-HSA-5617472: activation of HOX genes in anterior hindbrain development during early embryogenesis; GO:0060216: definitive hemopoiesis; hsa05202: transcriptional mis-regulation in cancer; and GO:0045638: negative regulation of myeloid cell differentiation, and they were significantly regulated due to alterations affecting the HOXA genes. This study identified HOXA3-10 genes as potential AML therapeutic targets and prognostic markers.