Novel prognostic genes and subclasses of acute myeloid leukemia revealed by survival analysis of gene expression data

Mutations of RAS genes identified in acute myeloid leukemia affect glycerophospholipid metabolism pathway

Background

Acute myeloid leukemia (AML) is a malignant disease originating from myeloid hematopoietic stem cells. Recent studies have shown that certain gene mutations promote tumor cell survival and affect the prognosis of patients by affecting metabolic mechanisms in tumor cells. RAS gene mutations are prevalent in AML, and the RAS signaling pathway is closely related to many metabolic pathways. However, the effects of different RAS gene mutations on AML cell metabolism are unclear.

Objectives

The main purpose of this study was to explore the effect of RAS gene mutation on the metabolic pathway of tumor cells.

Methods

In this study, we first used a retrovirus carrying a mutant gene to prepare Ba/F3 cell lines with RAS gene mutations, and then compared full-transcriptome data of Ba/F3 cells before and after RAS gene mutation and found that differentially expressed genes after NRASQ61K and KRASG12V mutation.

Results

We found a total of 1899 differentially expressed genes after NRASQ61K and KRASG12V mutation. 1089 of these genes were involved in metabolic processes, of which 167 genes were enriched in metabolism-related pathways. In metabolism-related pathways, differential genes were associated with the lipid metabolism pathway. Moreover, by comparing groups, we found that the expression of the DGKzeta and PLA2G4A genes in the glycerophospholipid metabolism pathway was significantly upregulated.

Conclusion

In conclusion, our study revealed that RAS gene mutation is closely related to the glycerophospholipid metabolism pathway in Ba/F3 cells, which may contribute to new precision therapy strategies and the development and application of new therapeutic drugs for AML.

Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis

The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes (ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples ( $n=162$ ) and control healthy whole blood samples ( $n=337$ ). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis ( $n=142$ ), posttreatment ( $n=42$ ), and recurrent ( $n=12$ ) stages. The results showed a significant increase of ANXA3, CEACM1, RGL4, RILPL1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

m6A regulator-based methylation modification patterns and characterization of tumor microenvironment in acute myeloid leukemia

RNA N6-methyladenosine (m6A) is the most common and intensively studied RNA modification that critically regulates RNA metabolism, cell signaling, cell survival, and differentiation. However, the overall role of multiple m6A regulators in the tumor microenvironment (TME) has not yet been fully elucidated in acute myeloid leukemia (AML). In our study, we explored the genetic and transcriptional alterations of 23 m6A regulators in AML patients. Three distinct molecular subtypes were identified and associated with prognosis, patient clinicopathological features, as well as TME characteristics. The TME characterization revealed that m6A patterns were highly connected with metabolic pathways such as biosynthesis of unsaturated fatty acids, cysteine and methionine metabolism, and citrate cycle TCA cycle. Then, based on the differentially expressed genes (DEGs) related to m6A molecular subtypes, our study categorized the entire cohort into three m6A gene clusters. Furthermore, we constructed the m6Ascore for quantification of the m6A modification pattern of individual AML patients. It was found that the tumor-infiltrating lymphocyte cells (TILs) closely correlated with the three m6A clusters, three m6A gene clusters, and m6Ascore. And many biological processes were involved, including glycogen degradation, drug metabolism by cytochrome P450, pyruvate metabolism, and so on. Our comprehensive analysis of m6A regulators in AML demonstrated their potential roles in the clinicopathological features, prognosis, tumor microenvironment, and particularly metabolic pathways. These findings may improve our understanding of m6A regulators in AML and offer new perspectives on the assessment of prognosis and the development of anticancer strategy.