Identification of hub genes associated with adult acute myeloid leukemia progression through weighted gene co-expression network analysis

Identification of two key biomarkers CD93 and FGL2 associated with survival of acute myeloid leukaemia by weighted gene co-expression network analysis

Acute myeloid leukaemia (AML) is a biologically heterogeneous haematological malignancy. This study was performed to identify the potential biomarkers for the prognosis and treatment of AML. We applied weighted gene co-expression network analysis to identify key modules and hub genes related to the prognosis of AML using data from The Cancer Genome Atlas (TCGA). In total, 1581 differentially expressed genes (1096 upregulated and 485 downregulated) were identified between AML patients and healthy controls, with the blue module being the most significant among 14 modules associated with AML morphology. Through functional enrichment analysis, we identified 217 genes in the blue module significantly enriched in 'neutrophil degranulation' and 'neutrophil activation involved in immune response' pathways. The survival analysis revealed six genes (S100A9, S100A8, HK3, CD93, CXCR2 and FGL2) located in the significantly enriched pathway that were notably related to AML survival. We validated the expression of these six genes at gene and single-cell levels and identified methylation loci of each gene, except for S100A8. Finally, in vitro experiments were performed to demonstrate whether the identified hub genes were associated with AML survival. After knockdown of CD93 and FGL2, cell proliferation was significantly reduced in U937 cell line over 5 days. In summary, we identified CD93 and FGL2 as key hub genes related to AML survival, with FGL2 being a novel biomarker for the prognosis and treatment of 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.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Up-Regulation of TRIM32 Associated With the Poor Prognosis of Acute Myeloid Leukemia by Integrated Bioinformatics Analysis With External Validation

Background

Acute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis.

Methods

AML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro.

Results

A total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients (p<0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro, knockdown of TRIM32 can inhibit the proliferation of AML cell lines.

Conclusion

TRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future.

Circular RNA circ_0004277 Inhibits Acute Myeloid Leukemia Progression Through MicroRNA-134-5p / Single stranded DNA binding protein 2

Circular RNAs (circRNAs) are crucial non-coding RNAs in the process of tumorigenesis. Nevertheless, the biological function of circ_0004277 in acute myeloid leukemia (AML) is blurred. Microarray data of circRNAs were utilized to evaluate circRNAs’ differential expression in AML. Quantitative real-time polymerase chain reaction (qRT-PCR) was executed to determine circ_0004277 and microRNA-134-5p (miR-134-5p) expression levels. The growth, migration and invasion of AML cells were tested by the cell counting kit-8 and Transwell experiment. Dual-luciferase reporter gene experiment, RNA immunoprecipitation (RIP) experiment and RNA pull-down experiment were executed to determine the targeting relationship between circ_0004277 and miR-134-5p. Western blot assay was used to detect single stranded DNA binding protein 2 (SSBP2) expression. We observed that circ_0004277 was down-regulated in AML, while miR-134-5p was up-regulated. Functionally, circ_0004277 overexpression or inhibition of miR-134-5p remarkably suppressed AML cell viability, migration and invasion. Furthermore, miR-134-5p served as a direct downstream target of circ_0004277 and SSBP2 was identified as a target of miR-134-5p. Compensation experiments showed that miR-134-5p mimics abolished the biological function of circ_0004277 on malignant phenotypes of AML cells. Collectively, circ_0004277 impedes AML development by adsorbing miR-134-5p and up-regulating SSBP2.