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

Development and validation of a novel prognosis prediction model for patients with myelodysplastic syndrome

Background

Somatic mutations are widespread in patients with Myelodysplastic Syndrome (MDS) and are associated with prognosis. However, a practical prognostic model for MDS that incorporates somatic mutations urgently needs to be developed.

Methods

A cohort of 201 MDS patients from the Gene Expression Omnibus (GEO) database was used to develop the model, and a single-center cohort of 115 MDS cohorts from Northwest China was used for external validation. Kaplan-Meier analysis was performed to compare the effects of karyotype classifications and gene mutations on the prognosis of MDS patients. Univariate and multivariate Cox regression analyses and Lasso regression were used to screen for key prognostic factors. The shinyapps website was used to create dynamic nomograms with multiple variables. The time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the model’s discrimination, accuracy and clinical utility.

Results

Six risk factors (age, bone morrow blast percentage, ETV6, TP53, EZH2, and ASXL1) were considered as predictor variables in the nomogram. The nomogram showed excellent discrimination, with respective the area under the ROC curve (AUC) values of 0.850, 0.839, 0.933 for the training cohort at 1 year, 3 years and 5 years; 0.715, 0.802 and 0.750 for the testing cohort at 1 year, 3 years and 5 years; and 0.668, 0.646 and 0.731 for the external validation cohort at 1 year, 3 years and 5 years. The calibration curves and decision curve showed that the nomogram had good consistency and clinical practical benefit. Finally, a stratified analysis showed that MDS patients with high risk had worse survival outcomes than patients with low risk.

Conclusion

We developed a nomogram containing six risk factors, which provides reliable and objective predictions of prognosis for MDS patients.

Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes

BACKGROUND

Several studies scatteredly identified the myelodysplastic syndromes' transcriptomic profiles (MDS). However, the exploration of transcriptional signatures, key signalling pathways, and their association with prognosis and diagnosis in the integrated multiple datasets remains lacking.

METHODS

We integrated the GSE4619, GSE19429, GSE30195, and GSE58831 microarray datasets of CD34 + cells for identifying the differentially expressed genes (DEGs) in the MDS. The series of bioinformatics methods are applied to identify the key hub genes, gene clusters, prognostic hub genes, and genes associated with diagnostic efficacy. Finally, we validated the expression differences of hub genes in the GSE114922 dataset.

RESULTS

We explored the DEGs related to gene ontology enrichment and KEGG pathways. We identified significant hub genes, including 168 upregulated hub genes (such as STAT1, IFIH1, EPRS, GRB2, RAC2, MAPK14, CASP1, and SPI1) and 52 downregulated hub genes (such as CREBBP, HIF1A, PIK3CA, EZH2, PIK3R1, MDM2, IRF4, CXCR4, PCNA, and CD19) in the MDS. In addition, we identified six significant molecular complex detection (MCODE)-derived upregulated gene clusters and one downregulated gene cluster, respectively. Moreover, we found that the higher expression level of MX2, GBP2, PXN, IFI44, FDXR, PLCB2, ASS1, ERCC4, PML, and RRAGD and the lower expression level of CD19, PAX5, TCF3, LEF1, NUSAP1, and TIMELESS hub genes are significantly correlated with shorter survival times of MDS patients. Furthermore, the area value under the ROC curve (AUC) of PXN, FDXR, PLCB2, PML, CD19, PAX5, and LEF1 prognostic genes are more than 0.80, indicating that these genes could be effectively used for the diagnostic efficacy of MDS patients.

CONCLUSIONS

Identifying key hub genes and their association with the prognosis and diagnostic efficacy may provide substantial clues for the treatment and diagnosis of MDS patients.

Myelodysplastic Syndromes and Metabolism

Myelodysplastic syndromes (MDS) are acquired clonal stem cell disorders exhibiting ineffective hematopoiesis, dysplastic cell morphology in the bone marrow, and peripheral cytopenia at early stages; while advanced stages carry a high risk for transformation into acute myeloid leukemia (AML). Genetic alterations are integral to the pathogenesis of MDS. However, it remains unclear how these genetic changes in hematopoietic stem and progenitor cells (HSPCs) occur, and how they confer an expansion advantage to the clones carrying them. Recently, inflammatory processes and changes in cellular metabolism of HSPCs and the surrounding bone marrow microenvironment have been associated with an age-related dysfunction of HSPCs and the emergence of genetic aberrations related to clonal hematopoiesis of indeterminate potential (CHIP). The present review highlights the involvement of metabolic and inflammatory pathways in the regulation of HSPC and niche cell function in MDS in comparison to healthy state and discusses how such pathways may be amenable to therapeutic interventions.

Autophagy Gene Panel-Based Prognostic Model in Myelodysplastic Syndrome

Abnormal autophagy is related to the pathogenesis and clinical symptoms of myelodysplastic syndrome (MDS). However, the effect of autophagy-related genes (ARGs) on the prognosis of MDS remains unclear. Here, we examined the expression profile of 108 patients with MDS from the GSE58831 dataset, and identified 22 genes that were significantly associated with overall survival. Among them, seven ARGs were screened and APIs were calculated for all samples based on the expression of the seven ARGs, and then, MDS patients were categorized into high- and low-risk groups based on the median APIs. The overall survival of patients with high-risk scores based on these seven ARGs was shorter than patients with low-risk scores in both the training cohort (P = 2.851e-06) and the validation cohort (P = 9.265e-03). Additionally, API showed an independent prognostic indicator for survival in the training samples [hazard ratio (HR) = 1.322, 95% confidence interval (CI): 1.158–1.51; P < 0.001] and the validation cohort (HR = 1.05, 95% CI: 1–1.1; P < 0.01). The area under the receiver operating characteristic curve (AUROC) of API and IPSS were 43.0137 and 66.0274 in the training cohorts and the AUC of the validation cohorts were 41.5361 and 72.0219. Our data indicate these seven ARGs can predict prognosis in patients with MDS and could guide individualized treatment.

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.