Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data

doi:10.21203/rs.3.rs-4916069/v1

Download PDF

Short Report

Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data

https://doi.org/10.21203/rs.3.rs-4916069/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Acute myeloid leukemia (AML) is a malignancy characterized by the uncontrolled proliferation of blood cells. Nowadays the incidence and prevalence of AML is growing rapidly, making more precise diagnostic tools and novel treatments open to urgent exploration. Genetic abnormalities and environmental factors are involved in the pathogenesis of AML and thereby, Microarray analysis have been applied to explore underlying pathways and genetic function. In this study we aimed to identify the differentially expressed genes (DEGs) and assess protein–protein interaction (PPI) to investigate the underpinned molecular and genetic mechanisms of AML.

Methods

The present study applied comprehensive statistical analysis in order to examine gene expression profiles in datasets GSE9476, GSE48558, and GSE63270 from the GEO database. The datasets were selected to provide a broad representation of gene expression changes associated with AML. Through this rigorous analysis, DEGs were identified across three databases. The identified DEGs were then subjected to further scrutiny, and genes such as TRIB2, LGALS1, FLT3, HOMER3, LMNA, CFD, and ABLIM1 were singled out for additional investigation. The mentioned genes were selected based on their potential significance in AML and were further analyzed using Gene Ontology (GO) analysis to understand their biological roles, functions, and the pathways they might be involved in AML.

Results

Our bioinformatics analysis revealed that among the explored genes, CFD and ABLIM1 were linked to AML.

Conclusion

It is concluded that ABLIM1 and CFD genes are associated with the presence and progression of AML, even in different subtypes of the disease.

Acute myeloid leukemia

differentially expressed genes

Bioinformatics analysis

Acute myeloid leukemia (AML) as a diverse clonal disease is identified by the proliferation of immature myeloid precursor cells and a subsequent failure in the function of bone marrow [1]. The latest incidence of AML has been reported as 62,770 cases, 23,670 deaths in USA, beside an overall prevalence of 14.95 cases per 100,000 based on the reports in 2024 [2]. Current evidence suggests that both genders are equally prone to cancer, however, males seem to be more susceptible and the mortality rate is higher in males, compared with females [3]. Regarding the prevalence of the mentioned morbidity in various stages of age, adults are mostly affected with an onset in 18 to 85 years [4]. Meanwhile, regarding the wide spectrum of cancers, leukemia is not common and includes about one percent of the cancers [5].

In line with other types of cancers, environmental factors and genetic predisposition are involved in the pathogenesis of AML [6, 7]. More precisely, environmental factors such as exposure to radioactive radiation, carcinogens, and infections result in the changes in DNA sequence, breakage, and chromosomal rearrangements. On the other hand, genetic factors such as chromosomal breakage syndromes, inactivating mutations of tumor suppressors, contribute to the potent risk of AML [4–8]. The mentioned factors consequently emerge as changes in genome and intermittent-uncontrolled proliferation of blood stem cells and malignancy [9–10].

The current body of knowledge suggests that although the underlying causes of AML are latent in more than 70% of the subjects, early diagnosis and prognosis is vital and promising in total survival rate as well as life expectancy [11]. Previously, the French-American-British (FAB) grouping initially classified the condition based on the percentage of blast cells in blood; however, the World Health Organization (WHO) presented a new classification in 2016, based on chromosomal rearrangements and genetic changes, which highlights the importance of genetic exploration and modifications [12–13].

In this context, gene expression profiles are implemented to closely investigate genetic expression modifications and identify differences and general genetic similarities between healthy individuals and various AML-affected groups [14–15]. According to previous findings, Zhao et al. documented the involvement and the potent genetical target of lymphocyte-specific protein tyrosine kinase (LCK), tumor necrosis factor (TNF), interleukin 7 receptor (IL7R), and immunoglobulin-associated alpha (CD79A) as well as miR-181 and miR-124 in the pathogenesis of AML. They highlighted these findings by bioinformatics analysis on microarray findings of 64 samples, comprised of 26 AML samples and 38 normal samples, respectively [22].

By considering the tremendous increases in worldwide morbidity and mortality from AML and the demand to explore genetic targets to design more effective diagnostic and treatment strategies for AML, conducting newer reviews on the data seems necessary. Hence, we aimed to identify the differentially expressed genes (DEGs) and assess protein-protein interaction (PPI) to explore the underpinned molecular and genetic mechanisms, to be carried out in the development of more precise diagnostic tools and treatments for AML.

Datasets and collection of data

To identify microarray-based gene expression profiles for acute myeloid leukemia (AML), a comprehensive search strategy was employed using the PubMed database (http://www.pubmed.com), the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds/), and the ArrayExpress dataset from the European Molecular Biology Laboratory-European Bioinformatics Institute (http://www.ebi.ac.uk/arrayexpress/). Three datasets were selected from the GEO database for this study: GSE9476 (N = 64, GPL96), GSE48558 (N = 170, GPL6244), and GSE63270 (N = 104, GPL17810). Samples from AML subgroups were selected from these datasets.

Inclusion and exclusion criteria.

To be considered eligible, studies and datasets had to adhere to the following inclusion criteria: i) they must involve human patients and healthy control groups; ii) they should focus on gene expression profiling; iii) they must have comparable experimental conditions and involve untreated samples; and iv) they must include complete raw and processed microarray data. Studies were excluded if they met any of the following conditions: i) they were letters, abstracts, meta-analyses, review articles, or case reports; ii) they utilized cell lines in their experimental design; iii) they relied exclusively on RT-PCR for profiling; and iv) they did not include a healthy control group. All datasets and references that complied with these criteria were meticulously examined. The latest search was carried out on May 12, 2024.

Analysis Method

The analysis was conducted using R software (version 4.3.3; https://www.r-project.org/) [16 − 11]. The "limma" package was utilized for background correction and normalization, while the Robust Multi-array Average (RMA) method was also employed, performing normalization through several steps. Subsequently, the "AgiMicroRna" package was used to prepare a boxplot diagram of gene expression for the selected GSEs, illustrating the median log2 values of their symbols and hierarchical clustering results. Principal Component Analysis (PCA) was conducted to verify normalization, ensuring that gene expression was uniformly analyzed and that sample descriptions were accurate for both groups. The "genefilter" package was then applied to filter expressed genes, eliminating those that were not expressed after the array data was separated into healthy controls and patients with AML [17].

Screening of DEGs

The "limma" package was used to perform statistical analysis in order to identify differentially expressed genes (DEGs). DEGs from the patient group were compared to those from the healthy group, resulting in the determination of the log2 fold change (logFC), adjusted p-value (adj P value), and P value for each gene. For the final analysis, data with a log2 fold change (FC) ≥ 1 and an adjusted P value < 0.05 were selected. A heatmap was generated using data from genes with P values < 0.01 and |logFC| ≥ 1.5, with gene expression visualized using the "pheatmap" package (Fig. 1). Additionally, a volcano plot was created using the "ggplot2" package to further investigate expression changes based on logFC.

PPI Network analysis

The analysis of the PPI network was conducted using Network Analyst [15]. In these networks, proteins are represented as nodes, while edges illustrate the known interactions between them (Fig. 3). This study involved topology and subnetwork analyses to showcase the overall structural features and to highlight areas of the network that exhibited significant changes. The subnetwork analysis was performed in three steps: i) compiling a list of differentially expressed genes (DEGs) through meta-analysis; ii) analyzing this list within the IMEx interactome-based PPI network; and iii) selecting a zero-order and minimum network to avoid the ‘hairball effect’ and ensure a clear presentation of the structure. Furthermore, Network Analyst offers two complementary metrics to identify the most important nodes, known as hub genes: Degree and betweenness centrality. Degree centrality refers to the number of connections a node has with other nodes, while betweenness centrality measures the number of shortest paths that pass through a node. From the main network, the most significant modules of hub genes were extracted for both up-regulated and down-regulated DEGs using the 'module explorer tool,' which is based on the random walks-dependent Walk trap algorithm. [34–35]

Gene set enrichment analysis for identification of overrepresented GO terms

Gene set enrichment analyses conducted for the analysis of overrepresented gene ontology (GO) terms, which are used for analyzing the connections and functions between DEGs and other genes. The online tool and the website https://toppgene.cchmc.org were used to select the genes identified in the previous steps to find the functional enrichment of the gene list using Ontologies (GO, Pathway), Regulome (TFBS and miRNA), Proteome, and Transcriptome. A cutoff of P value < 0.05 was also applied for the correction of the false discovery rate (FDR) and biological pathways. The results of GO: Molecular Function and GO: Cellular Component, as well as interactions visualized using Cystoscope in the Gene Mania app, were displayed, with a maximum of 50 resultant genes and a maximum of 10 resultant attributes.

Microarray data processing

This study designed to investigate three distinct subgroups of AML using microarray audible analysis to identify audible sensitivity within each subgroup as well as audible contrasts between patients with AML and healthy individuals. Total included samples consisted of; GSE9476 [22] (healthy = 38 and AML = 26, respectively), GSE63270 [23] (healthy = 42 and AML = 62, respectively), as well as subjects affected by AML with sub-groups and percentages of various blast cells and GSE48558 [26] (healthy = 43 and AML = 18, respectively) as samples with primary AM, which had sub-diverse groups of AML with samples of encompassing bone marrow and blood and cell lines, as well as samples that show in the hierarchical clustering cluster model Samples that were removed to reduce statistical errors Identification of DEGs in AML.[36–37]

Identification of DEGs

The overlap of DEGs between various datasets was demonstrated using a Venn diagram and a volcano plot. The genes that conform to the prerequisites of p-value < 0.01 and absolute log2 fold change (|logFC|) > 1.5 are determined to be shared DEGs in at least two of the three datasets.[38] To show the amount of expression of these genes, a volcano plot was used (Fig. 2).

The up-regulated genes are documented as below:

LGALS1 (Lectin, galactoside-binding, soluble, 1), FLT3 (FMS-like tyrosine kinase 3), CFD (Complement factor D), LMNA (Lamin A/C), HOMER3 (Homer protein homolog 3), STAB1 (Stabilin 1) and Down-regulated genes are: ABLIM1 (Actin binding LIM protein 1), TRIB2 (Tribble’s homolog 2) Furthermore, these genes have been recognized to express consistently in various subgroups and, taken together, are possibly considered to be significantly involved in the genesis of AML.[45] The volcano plot has a striking exhibit of these genes. In addition, consistent with our findings, the red-marked acute myeloid leukemia CFD and ABLIM1 genes were not further explored (Table 1).

Table 1

Models for Genes with Different Expression
Gene	disease	Pathway	PubMed
CFD	Complement Factor D Deficiency	Complement Activation	[34,35]
TRIB2	acute myeloid leukemia	MAPK pathways	[44]
ABLIM1	Hepatocellular Carcinoma	cell migration	[37]
LGALS1	Trophoblastic Neoplasm	apoptosis	[46]
FLT3	Chronic Myelomonocytic Leukemia	cell proliferation	[47]
HOMER3	breast cancer	beta-Catenin activation	[48]
LMNA	Mandibulofacial Dysplasia	RNA splicing	[49]
STAB1	Lipopolysaccharides	endocytose LPS-HDL complex	[50]

PPI network and modular analysis

Data obtained from toppgene website was applied for gene ontology, and consisted from molecular function and cellular component as well as pathways related to genes with different expression. These data used to obtain gene analysis, which indicates pathways with high expression and encapsulates underlying pathways of the disease.[39] Hence p- value < 0.05 is shown in the table (Table 2 and Table 3).

Table 2

Gene ontology related to genes with DGEs as well as pathways related to them
GO: Molecular Function	Name	p-value	q-value FDR B&H	Hit in Query List	Hit Count in Genome
GO:0030395	lactose binding	0.001406	0.0427	LGALS1	4
GO:0005021	vascular endothelial growth factor receptor activity	0.002459	0.0427	FLT3	7
GO:0048030	disaccharide binding	0.002809	0.0427	LGALS1	8
GO:0005534	galactose binding	0.002809	0.0427	LGALS1	8
GO:0016936	galactoside binding	0.003861	0.0427	LGALS1	11
GO:0005041	low-density lipoprotein particle receptor activity	0.005262	0.0427	STAB1	15
GO:0031625	ubiquitin protein ligase binding	0.005801	0.0427	FLT3,TRIB2	341
GO:0044389	ubiquitin-like protein ligase binding	0.006516	0.0427	FLT3,TRIB2	362
GO:0031434	mitogen-activated protein kinase kinase binding	0.006661	0.0427	TRIB2	19
GO:0030169	low-density lipoprotein particle binding	0.006661	0.0427	STAB1	19
GO:0030228	lipoprotein particle receptor activity	0.007011	0.0427	STAB1	20
GO:0035259	nuclear glucocorticoid receptor binding	0.00736	0.0427	FLT3	21
GO:0070492	oligosaccharide binding	0.00771	0.0427	LGALS1	22
GO:0061629	RNA polymerase II-specific DNA-binding transcription factor binding	0.009134	0.04555	FLT3,TRIB2	431
GO:0005540	hyaluronic acid binding	0.01015	0.04555	STAB1	29
GO:0071813	lipoprotein particle binding	0.01224	0.04555	STAB1	35
GO:0071814	protein-lipid complex binding	0.01224	0.04555	STAB1	35
GO:0055106	ubiquitin-protein transferase regulator activity	0.01224	0.04555	TRIB2	35
GO:0043236	laminin binding	0.01259	0.04555	LGALS1	36
GO:0008157	protein phosphatase 1 binding	0.01294	0.04555	LMNA	37
GO:0015035	protein-disulfide reductase activity	0.01328	0.04555	STAB1	38
GO:0043548	phosphatidylinositol 3-kinase binding	0.01467	0.04802	FLT3	42
GO:0015036	disulfide oxidoreductase activity	0.01537	0.04811	STAB1	44
GO: Cellular Component
GO:1990724	galectin complex	0.000669	0.0388	LGALS1	2
GO:0005638	lamin filament	0.002007	0.0388	LMNA	6
GO:0098635	protein complex involved in cell-cell adhesion	0.002007	0.0388	LGALS1	6

Table 3

Pathways of DEGs
Pathway	Name	Source	p-value	q-value FDR B&H	Hit in Query List	Hit Count in Genome
M42576	WP_ACQUIRED_PARTIAL_LIPODYSTROPHY_BARRAQUER_SIMONS_SYNDROME	WikiPathways	7.03E-06	0.001061	LMNA,CFD	10
M39505	WP_ADIPOGENESIS	WikiPathways	0.001299	0.04437	LMNA,CFD	131
MM15970	WP_ADIPOGENESIS_GENES	WikiPathways	0.00144	0.04437	LMNA,CFD	138
M27024	REACTOME_ALTERNATIVE_COMPLEMENT_ACTIVATION	Reactome Pathways	0.002165	0.04437	CFD	5
MM14674	REACTOME_ALTERNATIVE_COMPLEMENT_ACTIVATION	Reactome Pathways	0.002165	0.04437	CFD	5
M41736	REACTOME_FLT3_SIGNALING_THROUGH_SRC_FAMILY_KINASES	Reactome Pathways	0.002597	0.04437	FLT3	6
M41737	REACTOME_FLT3_SIGNALING_BY_CBL_MUTANTS	Reactome Pathways	0.003029	0.04437	FLT3	7
M27953	REACTOME_STAT5_ACTIVATION	Reactome Pathways	0.003029	0.04437	FLT3	7
MM1582	BIOCARTA_ALTERNATIVE_PATHWAY	BioCarta Pathways	0.003461	0.04437	CFD	8
M22072	BIOCARTA_ALTERNATIVE_PATHWAY	BioCarta Pathways	0.003893	0.04437	CFD	9
M41731	REACTOME_STAT5_ACTIVATION_DOWNSTREAM_OF_FLT3_ITD_MUTANTS	Reactome Pathways	0.004325	0.04437	FLT3	10
MM15105	REACTOME_DEPOLYMERIZATION_OF_THE_NUCLEAR_LAMINA	Reactome Pathways	0.005188	0.04437	LMNA	12
M47365	KEGG_MEDICUS_VARIANT_DUPLICATION_OR_MUTATION_ACTIVATED_FLT3_TO_RAS_ERK_SIGNALING_PATHWAY	KEGG Medicus Pathways	0.006051	0.04437	FLT3	14
M47389	KEGG_MEDICUS_VARIANT_DUPLICATION_OR_MUTATION_ACTIVATED_FLT3_TO_RAS_PI3K_SIGNALING_PATHWAY	KEGG Medicus Pathways	0.006051	0.04437	FLT3	14
M889	REACTOME_DCC_MEDIATED_ATTRACTIVE_SIGNALING	Reactome Pathways	0.006051	0.04437	ABLIM1	14
M47473	KEGG_MEDICUS_REFERENCE_FLT3LG_FLT3_RAS_ERK_SIGNALING_PATHWAY	KEGG Medicus Pathways	0.006482	0.04437	FLT3	15
M47474	KEGG_MEDICUS_REFERENCE_FLT3LG_FLT3_RAS_PI3K_SIGNALING_PATHWAY	KEGG Medicus Pathways	0.006482	0.04437	FLT3	15
M41735	REACTOME_NEGATIVE_REGULATION_OF_FLT3	Reactome Pathways	0.006482	0.04437	FLT3	15
M9367	BIOCARTA_ERYTH_PATHWAY	BioCarta Pathways	0.006482	0.04437	FLT3	15
M27360	REACTOME_DEPOLYMERIZATION_OF_THE_NUCLEAR_LAMINA	Reactome Pathways	0.006482	0.04437	LMNA	15
M41733	REACTOME_SIGNALING_BY_FLT3_ITD_AND_TKD_MUTANTS	Reactome Pathways	0.006913	0.04437	FLT3	16
MM1369	BIOCARTA_COMP_PATHWAY	BioCarta Pathways	0.007343	0.04437	CFD	17
MM14920	REACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION	Reactome Pathways	0.007343	0.04437	LMNA	17
MM1392	BIOCARTA_ERYTH_PATHWAY	BioCarta Pathways	0.007774	0.04437	FLT3	18
M29617	REACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION	Reactome Pathways	0.008204	0.04437	LMNA	19
M917	BIOCARTA_COMP_PATHWAY	BioCarta Pathways	0.008635	0.04437	CFD	20
M39593	WP_HEMATOPOIETIC_STEM_CELL_GENE_REGULATION_BY_GABP_ALPHA_BETA_COMPLEX	WikiPathways	0.009065	0.04437	FLT3	21
M48100	WP_7Q11_23_DISTAL_COPY_NUMBER_VARIATION	WikiPathways	0.009495	0.04437	LMNA	22
M42534	WP_PROGERIA_ASSOCIATED_LIPODYSTROPHY	WikiPathways	0.009495	0.04437	LMNA	22
M39502	WP_COMPLEMENT_ACTIVATION	WikiPathways	0.009495	0.04437	CFD	22
M17902	BIOCARTA_CASPASE_PATHWAY	BioCarta Pathways	0.009495	0.04437	LMNA	22
MM1359	BIOCARTA_CASPASE_PATHWAY	BioCarta Pathways	0.01078	0.04437	LMNA	25
M47933	KEGG_MEDICUS_REFERENCE_REGULATION_OF_GF_RTK_RAS_ERK_SIGNALING_UBIQUITINATION_OF_RTK_BY_CBL	KEGG Medicus Pathways	0.01078	0.04437	FLT3	25
M42527	REACTOME_DEFECTIVE_INTRINSIC_PATHWAY_FOR_APOPTOSIS	Reactome Pathways	0.01078	0.04437	LMNA	25
M39407	WP_WNT_BETA_CATENIN_SIGNALING_PATHWAY_IN_LEUKEMIA	WikiPathways	0.01121	0.04437	FLT3	26
M47438	KEGG_MEDICUS_VARIANT_MLL_AF4_FUSION_TO_TRANSCRIPTIONAL_ACTIVATION	KEGG Medicus Pathways	0.01121	0.04437	FLT3	26
MM1516	BIOCARTA_TNFR1_PATHWAY	BioCarta Pathways	0.01207	0.04437	LMNA	28
MM1396	BIOCARTA_FAS_PATHWAY	BioCarta Pathways	0.01207	0.04437	LMNA	28
M41724	REACTOME_FLT3_SIGNALING_IN_DISEASE	Reactome Pathways	0.01207	0.04437	FLT3	28
M14971	BIOCARTA_DEATH_PATHWAY	BioCarta Pathways	0.0125	0.04437	LMNA	29
M3618	BIOCARTA_TNFR1_PATHWAY	BioCarta Pathways	0.0125	0.04437	LMNA	29
M42537	WP_FAMILIAL_PARTIAL_LIPODYSTROPHY	WikiPathways	0.01293	0.04437	LMNA	30
M269	PID_RAS_PATHWAY	PID Pathways	0.01293	0.04437	LGALS1	30
M9503	BIOCARTA_FAS_PATHWAY	BioCarta Pathways	0.01293	0.04437	LMNA	30
M27547	REACTOME_DISEASES_OF_SIGNAL_TRANSDUCTION_BY_GROWTH_FACTOR_RECEPTORS_AND_SECOND_MESSENGERS	Reactome Pathways	0.0134	0.04495	LMNA,FLT3	432
MM1422	BIOCARTA_DEATH_PATHWAY	BioCarta Pathways	0.01464	0.04806	LMNA	34
MM15967	WP_FAS_PATHWAY_AND_STRESS_INDUCTION_OF_HSP_REGULATION	WikiPathways	0.0155	0.04842	LMNA	36
MM14488	REACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS	Reactome Pathways	0.01593	0.04842	LMNA	37
M39817	WP_INFLUENCE_OF_LAMINOPATHIES_ON_WNT_SIGNALING	WikiPathways	0.01593	0.04842	LMNA	37
M29803	REACTOME_FLT3_SIGNALING	Reactome Pathways	0.01635	0.04842	FLT3	38
M495	REACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS	Reactome Pathways	0.01635	0.04842	LMNA	38
M46456	WP_IMMUNE_INFILTRATION_IN_PANCREATIC_CANCER	WikiPathways	0.01678	0.04873	LGALS1	39
P value < 0.01 and FDR > 0.05

PPI revealed the connections between CFD and the CFB, CFH, CFP gene family, and additionally CR1, C3, SERPIN2F, CD55, all of which were involved in immunological pathways, in order to reveal the communication network between them.[40–41] Among them, nodes of CFD and ABLIM1 genes were not previously investigated in AML. It was established how ABLIM1 correlated with LDOC1, DOCK1, BCO1, IMPA2, and NTN1. However, IMPA2 indirectly interacted with LGAL3, revealing the point that not only by protein-protein interactions but also IMPA2 interacts with LGALS3. Figure 3 represents co-localization, shared protein domains, genetic connection, and co-expression (all of which are not mentioned in the text). In the present study, cell-map, reactom, humancyc, IMID, NCINATURE were used in Cytoscape to analyze the network and for PPI from BioGRID and PathwayCommons, data collection was revealed in cellular and molecular functions and processes.‌‌

To our knowledge, AML as an important hematopoietic malignancy and burdensome bone marrow disease is properly overlooked by the viewpoint of clinicians, oncologists, as well as genetic specialists due to its rapidly increased prevalence, worldwide (18). Current evidence has suggested the role of infections, radioactive radiation, ionizing rays, and carcinogens in exerting variations in the sequence of DNA, DNA- breakage, and DNA fragment displacement, namely termed as "environmental factors" [9 − 8]. On top of the aforementioned factors "genetic predisposition" refers to the suppression in mutations of tumor suppressors, and stimulations in oncogenes which finally emerges as inherited and acquired disorders and thereby sharpens the risk of AML [9 − 8]. This catastrophic cycle is followed by uncontrolled proliferation of blood stem cells [10]. In fact, AML is ceased through blood stem cell differentiation, and bone marrow cell clonal proliferation could be linked to the progression of malignancies. Furthermore, these changes result in interruptions in the body's normal process of producing blood. The clonal expansion of myeloid blasts then manifests in bone marrow, peripheral blood, and other tissues [11–19].

As mentioned, according to WHO classification, different mutations that contribute to the pathogenesis of AML are classified into different subgroups. This definition also helps clinicains in order to make a better decision on the dose, duration of prescribed medications as well as the reaction of patients to the therapy in various subgroups‌‌ [11–19]. Hence, AML treatment is facilitated by an overall awareness on the disease's extent and alterations in bone marrow cells in addition to underlying genetic mediators. A classified perspective could be obtained through exploring and identifying genetic modifications and pathways that cause AML [12–13]. It may be possible to determine the stages of AML severity by analyzing the variations and interferences that accumulate in myeloblasts and blood cells [14]. Utilizing gene expression profiles to closely investigate genetic expression modifications and identify differences and general genetic similarities between healthy individuals and different AML groups is a helpful and efficient approach to exploring genetic mutations in these cells. By considering the role of complex genetic mediators in the development and progression of AML to severe stages, investigating the exact pathological mechanisms is urgent and necessary [18].

In the present study, three GPLs of different GSE objects were applied to assess gene differences among the groups of patients with AML and healthy individuals. Nevertheless, the AML group in these datasets consisted of various subgroups, suggesting that DEGs showed manifestations of common genes in the creation and progression of the disease, which subsequently may expand the current perspective on the disease. Moreover, the interference between proteins and pathways serves as an excellent illustration of how genes utilized by statistical methods are essential to the cellular and biological foundation for processes that affects the formation of malignancies in myeloid stem cells in both direct and indirect ways. Eight DEGs were considered in the present investigation and there were no overlapping genes as well as no same GPLs. Facing the findings a previous study has reported the facilitation of the genes TRIB2L, GALS1, FLT3, HOMER3, LMNA, and STAB1genes in AML through bioinformatics assessments.

With this regard, bioinformatics analysis could be more effective because statistical calculations can be used to determine the risk and predict the involved protein, gene, and molecular-cellular pathways, however, the observed genes participate across all types of the cancers. Identified genes activate oncogenic pathways, which exert modifications in the cellular biogenesis system to exert epigenetic modifications, which are essential to understand the nation of disease and proper interventions [20–21].

It was observed that CFD and ABLIM1 genes may be associated with AML. Despite the uncertainty in the function of CFD in AML in adults, by considering its significant role in other cancers it should perform similar roles in leukemia as well. Evidence has demonstrated that CFDs produce esthetic activity that destroys the skin matrix, aging the skin and activating the protein kinase-B (AKT) signaling pathway. AKT activation in turn, elevates the expression of matrix metalloproteinase 1 (MMP1), which decreases collagen type- I alpha-1 [33]. Subsequent to the reductions in collagen-I and SPARC in bone marrow, inflammation as the focal core of cancer pathogenesis becomes evident. In addition, extracellular matrix (ECM) decay, which gets resulted from the disruptions in AML hematopoietics [23]. It should be noted that MMPs accelerate the capability of ECM macromolecules including type I, II, III, laminins-1 and laminins-5 as well as collagens to be destroyed, in line with activating aggression and metastasis. M4 and M5, are classified by FAB [24].

The CFD gene revealed an average increase in expression log FC = 1.89 in AML using bioinformatic analysis. Given its specific functions, this protein is expected to be involved in AML. Furthermore, due to the increased expression of CFD across all subgroups, it creates this perception that may play a role in the early stages of malignancy. Moreover, using SERPIN2F, CFD was demonstrated in PPIs. A particular aspect of the investigation that was left to escape the text was GSE35008, a three-fold reduction in the expression of SERPIN2, and a member of the SERPIN cluster. It should be mentioned that the intrinsic function of CFDs is serine peptidase, whereas the intrinsic role of SERPIN2s is serine protease inhibitor [25–26].‌

Likewise, considering the functions of Actin binding LIM protein 1 (ABLIM1), gene in other cancers and the lack of studies on this gene in AML, it is possible that ABLIM1 which reduces cell proliferation, cell migration, and multiplication by decreasing polymerization through actin phosphorylation, could also reduce glioblastoma aggression [27–28]. The pathogenic process and aggression are termed as epithelial-to-mesenchymal transition (EMT), which is the cause of F- actin dynamics. In this context, Danping Liu et al. documented that AML cells in the cell class display EMT-like characteristics. However, extracellular signals and oral signaling pathways related to this phenotype are not available [29]

It is noteworthy that blood and bone brain samples were examined together and different subgroups were presented in GSE48558, which relates to primary AML samples. This gene decreases expression by reducing expression compared to previous data that suggests its role in the progression of the disease. Moreover, the mean expression of log FC = 1.55 was eliminated in the current study. Furthermore, the gene mania network analysis reveals that ABLIM1 has interactions with FLT3, TRIB2, and LGALS1 genes and LGALS1 modulates matrix-cell and cell-cell communication in bioinformatics analysis [30–31]. In addition, the LGALS1 gene has been demonstrated to rise to two-fold in AML. Compared with the other two GSEs with a more severe level of disease (2.44 and 1.83), the expression of LGALS1 in GSE48558 (consisting of primary AML samples) is the lowest, with a 1.42-fold increase [32]. This finding suggests that LGALS1 may induce EMT at AML by imposing on ABLIM1, however research in needed to confirm these preliminary findings.[42–43]

In summary, among the addressed genes, ABLIM1 and CFD were linked to AML and this association was firstly mentioned by this study. This finding suggests the presence of novel pathways and interactions related to AML that have yet to be fully understood. The identification of these genes opens up new avenues for research, potentially offering deeper insights into the molecular mechanisms underlying AML. However, the mentioned results are preliminary and additional experimental and clinical studies are required to validate these findings. Further research will be crucial in determining the precise roles of these genes in AML and how they might contribute to the development, progression, or treatment of the disease.

Taken together, this bioinformatics study identified new mechanisms and interactions associated with AML and demonstrated the association of ABLIM1 and CFD genes with AML for the first time. Moreover, ABLIM1 and CFD are expressed in different subtypes of AML, suggesting that they may play a role in the initial malignancy of the disease and induce significant modifications compared with healthy subject. However, further well-designed explorations are recommended.

ABLIM1, Actin binding LIM protein 1; AKT, Protein kinase-B; AML, Acute myeloid leukemia; CFD, Complement factor-D; DEGs, Differentially expressed genes; ECM, Extracellular matrix; EMT, Epithelial-to-mesenchymal transition; FAB, French-American-British; FDR, False discovery rate; GEO, Gene expression omnibus; GO, Gene ontology; MMP1, Matrix metalloproteinase 1; PPI, Protein-protein interaction network; WHO, World health organization.

Ethics approval and consent to participate

This study was conducted in accordance with the ethical standards of the Ethics Committee of the University of Tabriz. All human participants involved in this study provided their informed consent prior to their participation.
This study did not involve human participants, and therefore, informed consent was not applicable. The research was conducted using publicly available datasets, and no direct interaction with human subjects occurred.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on a reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Authors' contributions: The authors’ responsibilities were as follows: H.A.H.A and RS helped in data collection; H.A.H.A wrote the original paper; H.A.H.A, SR did statistical analysis; RS contributed to the conception of the article as well as to the final revision of the manuscript. All authors read and approved the final version of the manuscript.

Cancer Statistics Center. (n.d.). *Cancer Statistics Center*. American Cancer Society. https://cancerstatisticscenter.cancer.org
Samoylov, A. S., Bushmanov, A. Y., Udalov, Y. D., Galstyan, I. A., Nugis, V. Y., Kozlova, M. G., Nikitina, V. A., Khvostunov, I. K., & Golub, E. V. (2018). ACUTE MYELOID LEUKEMIA, PROSTATE AND SKIN CANCER IN ACUTE RADIATION SYNDROME SURVIVOR AFTER THE 1986 CHERNOBYL NUCLEAR ACCIDENT: CASE REPORT. Radiation protection dosimetry, 182(1), 85–89.
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 17-48. doi:10.3322/caac.21763
Savage, S. A., & Walsh, M. F. (2018). Myelodysplastic Syndrome, Acute Myeloid Leukemia, and Cancer Surveillance in Fanconi Anemia. Hematology/oncology clinics of North America, 32(4), 657–668. https://doi.org/10.1016/j.hoc.2018.04.002
Lindvall, C., Nordenskjöld, M., Porwit, A., Björkholm, M., & Blennow, E. (2001). Molecular cytogenetic characterization of acute myeloid leukemia and myelodysplastic syndromes with multiple chromosome rearrangements. Haematologica, 86(11), 1158–1164.
Paperna, T., Sharon-Shwartzman, N., Kurolap, A., Goldberg, Y., Moustafa, N., Carasso, Y., Feinstien, M., Mory, A., Reznick-Levi, G., Gonzaga-Jauregui, C., Shuldiner, A. R., Basel-Salmon, L., Ofran, Y., Half, E. E., & Baris Feldman, H. (2020). Homozygosity for CHEK2 p.Gly167Arg leads to a unique cancer syndrome with multiple complex chromosomal translocations in peripheral blood karyotype. Journal of medical genetics, 57(7), 500–504. https://doi.org/10.1136/jmedgenet-2018-105824
Imashuku, S., Teramura, T., Kuriyama, K., Kitazawa, J., Ito, E., Morimoto, A., & Hibi, S. (2002). Risk of etoposide-related acute myeloid leukemia in the treatment of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis. International journal of hematology, 75(2), 174–177. https://doi.org/10.1007/BF02982023
Tashakori, M., Kadia, T., Loghavi, S., Daver, N., Kanagal-Shamanna, R., Pierce, S., Sui, D., Wei, P., Khodakarami, F., Tang, Z., Routbort, M., Bivins, C. A., Jabbour, E. J., Medeiros, L. J., Bhalla, K., Kantarjian, H. M., Ravandi, F., & Khoury, J. D. (2022). TP53 copy number and protein expression inform mutation status across risk categories in acute myeloid leukemia. Blood, 140(1), 58–72. https://doi.org/10.1182/blood.2021013983
Wallwitz, J., Aigner, P., & Stoiber, D. (2021). Tumor suppressors in acute myeloid leukemia. Leukemia & lymphoma, 62(10), 2320–2330. https://doi.org/10.1080/10428194.2021.1907372
Yi, M., Li, A., Zhou, L., Chu, Q., Song, Y., & Wu, K. (2020). The global burden and attributable risk factor analysis of acute myeloid leukemia in 195 countries and territories from 1990 to 2017: estimates based on the global burden of disease study 2017. Journal of hematology & oncology, 13(1), 72. https://doi.org/10.1186/s13045-020-00908-z
Falini, B., & Martelli, M. P. (2023). Comparison of the International Consensus and 5th WHO edition classifications of adult myelodysplastic syndromes and acute myeloid leukemia. American journal of hematology, 98(3), 481–492. https://doi.org/10.1002/ajh.26812
Huber, S., Baer, C., Hutter, S. et al. AML classification in the year 2023: How to avoid a Babylonian confusion of languages. Leukemia 37, 1413–1420 (2023). https://doi.org/10.1038/s41375-023-01909-w
Kolur, V., Vastrad, B., Vastrad, C., Kotturshetti, S., & Tengli, A. (2021). Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis. BMC cardiovascular disorders, 21(1), 329. https://doi.org/10.1186/s12872-021-02146-8
Pollyea, D. A., Bixby, D., Perl, A., Bhatt, V. R., Altman, J. K., Appelbaum, F. R., de Lima, M., Fathi, A. T., Foran, J. M., Gojo, I., Hall, A. C., Jacoby, M., Lancet, J., Mannis, G., Marcucci, G., Martin, M. G., Mims, A., Neff, J., Nejati, R., Olin, R., … Tallman, M. S. (2021). NCCN Guidelines Insights: Acute Myeloid Leukemia, Version 2.2021. Journal of the National Comprehensive Cancer Network : JNCCN, 19(1), 16–27. https://doi.org/10.6004/jnccn.2021.0002
Liu H. (2021). Emerging agents and regimens for AML. Journal of hematology & oncology, 14(1), 49. https://doi.org/10.1186/s13045-021-01062-w
Koenig, K. L., Sahasrabudhe, K. D., Sigmund, A. M., & Bhatnagar, B. (2020). AML with Myelodysplasia-Related Changes: Development, Challenges, and Treatment Advances. Genes, 11(8), 845. https://doi.org/10.3390/genes11080845
Hansen, B. A., Wendelbo, Ø., Bruserud, Ø., Hemsing, A. L., Mosevoll, K. A., & Reikvam, H. (2020). Febrile Neutropenia in Acute Leukemia. Epidemiology, Etiology, Pathophysiology and Treatment. Mediterranean journal of hematology and infectious diseases, 12(1), e2020009. https://doi.org/10.4084/MJHID.2020.009
Jentzsch, M., Bischof, L., Ussmann, J. et al. Prognostic impact of the AML ELN2022 risk classification in patients undergoing allogeneic stem cell transplantation. Blood Cancer J. 12, 170 (2022). https://doi.org/10.1038/s41408-022-00764-9
Zeng, H., Liu, X., & Zhang, Y. (2021). Identification of Potential Biomarkers and Immune Infiltration Characteristics in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Analysis. Frontiers in cardiovascular medicine, 8, 624714. https://doi.org/10.3389/fcvm.2021.624714
Prada-Arismendy, J., Arroyave, J. C., & Röthlisberger, S. (2017). Molecular biomarkers in acute myeloid leukemia. Blood reviews, 31(1), 63–76. https://doi.org/10.1016/j.blre.2016.08.005
Feng, H., Gu, Z. Y., Li, Q., Liu, Q. H., Yang, X. Y., & Zhang, J. J. (2019). Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. Journal of ovarian research, 12(1), 35. https://doi.org/10.1186/s13048-019-0508-2
Zhang Z, Zhao L, Wei X, Guo Q, Zhu X, Wei R, Yin X, Zhang Y, Wang B, Li X. Integrated bioinformatic analysis of microarray data reveals shared gene signature between MDS and AML. Oncol Lett. 2018 Oct;16(4):5147-5159. doi: 10.3892/ol.2018.9237. Epub 2018 Jul 31. PMID: 30214614; PMCID: PMC6126153.
Luo, Y., Zhou, Y. Identification of novel biomarkers and immune infiltration features of recurrent pregnancy loss by machine learning. Sci Rep 13, 10751 (2023). https://doi.org/10.1038/s41598-023-38046-4
Stirewalt, D. L., Meshinchi, S., Kopecky, K. J., Fan, W., Pogosova-Agadjanyan, E. L., Engel, J. H., Cronk, M. R., Dorcy, K. S., McQuary, A. R., Hockenbery, D., Wood, B., Heimfeld, S., & Radich, J. P. (2008). Identification of genes with abnormal expression changes in acute myeloid leukemia. Genes, chromosomes & cancer, 47(1), 8–20. https://doi.org/10.1002/gcc.20500
Jung, N., Dai, B., Gentles, A. J., Majeti, R., & Feinberg, A. P. (2015). An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nature communications, 6, 8489. https://doi.org/10.1038/ncomms9489
Cramer-Morales, K., Nieborowska-Skorska, M., Scheibner, K., Padget, M., Irvine, D. A., Sliwinski, T., Haas, K., Lee, J., Geng, H., Roy, D., Slupianek, A., Rassool, F. V., Wasik, M. A., Childers, W., Copland, M., Müschen, M., Civin, C. I., & Skorski, T. (2013). Personalized synthetic lethality induced by targeting RAD52 in leukemias identified by gene mutation and expression profile. Blood, 122(7), 1293–1304. https://doi.org/10.1182/blood-2013-05-501072
O'Connor, C., Lohan, F., Campos, J., Ohlsson, E., Salomè, M., Forde, C., Artschwager, R., Liskamp, R. M., Cahill, M. R., Kiely, P. A., Porse, B., & Keeshan, K. (2016). The presence of C/EBPα and its degradation are both required for TRIB2-mediated leukaemia. Oncogene, 35(40), 5272–5281. https://doi.org/10.1038/onc.2016.66
Ruvolo, P. P., Ma, H., Ruvolo, V. R., Zhang, X., Post, S. M., & Andreeff, M. (2020). LGALS1 acts as a pro-survival molecule in AML. Biochimica et biophysica acta. Molecular cell research, 1867(10), 118785. https://doi.org/10.1016/j.bbamcr.2020.118785
Lv, K., Ren, J. G., Han, X., Gui, J., Gong, C., & Tong, W. (2021). Depalmitoylation rewires FLT3-ITD signaling and exacerbates leukemia progression. Blood, 138(22), 2244–2255. https://doi.org/10.1182/blood.2021011582
Zhang, J., Liu, T., Wang, Y., Yan, X., Li, Y., Xu, F., & Zhang, R. (2023). Dynamic alterations of the transcriptome-wide m6A methylome in cytogenetically normal acute myeloid leukaemia during initial diagnosis and relapse. Genomics, 115(6), 110725. https://doi.org/10.1016/j.ygeno.2023.110725
Alshaalan KS, Albawardi TK, Zhra M, Bin Sulaiman N, Jnied OY, Saleem RA, Aljada A. Differential Expression of LMNA/C and Insulin Receptor Transcript Variants in Peripheral Blood Mononuclear Cells of Leukemia Patients. Journal of Clinical Medicine. 2024; 13(9):2568. https://doi.org/10.3390/jcm13092568
Lin, S. Y., Hu, F. F., Miao, Y. R., Hu, H., Lei, Q., Zhang, Q., Li, Q., Wang, H., Chen, Z., & Guo, A. Y. (2019). Identification of STAB1 in Multiple Datasets as a Prognostic Factor for Cytogenetically Normal AML: Mechanism and Drug Indications. Molecular therapy. Nucleic acids, 18, 476–484. https://doi.org/10.1016/j.omtn.2019.09.014
Ezure, T., Sugahara, M., & Amano, S. (2019). Senescent dermal fibroblasts negatively influence fibroblast extracellular matrix-related gene expression partly via secretion of complement factor D. BioFactors (Oxford, England), 45(4), 556–562. https://doi.org/10.1002/biof.1512
Tripodo, C., Burocchi, A., Piccaluga, P. P., Chiodoni, C., Portararo, P., Cappetti, B., Botti, L., Gulino, A., Isidori, A., Liso, A., Visani, G., Martelli, M. P., Falini, B., Pandolfi, P. P., Colombo, M. P., & Sangaletti, S. (2017). Persistent Immune Stimulation Exacerbates Genetically Driven Myeloproliferative Disorders via Stromal Remodeling. Cancer research, 77(13), 3685–3699. https://doi.org/10.1158/0008-5472.CAN-17-1098
Wang, C., Chen, Z., Li, Z., & Cen, J. (2010). The essential roles of matrix metalloproteinase-2, membrane type 1 metalloproteinase and tissue inhibitor of metalloproteinase-2 in the invasive capacity of acute monocytic leukemia SHI-1 cells. Leukemia research, 34(8), 1083–1090. https://doi.org/10.1016/j.leukres.2010.01.016
Zhang, H., Fei, R., Xue, B., Yu, S., Zhang, Z., Zhong, S., Gao, Y., & Zhou, X. (2017). Pnserpin: A Novel Serine Protease Inhibitor from Extremophile Pyrobaculum neutrophilum. International journal of molecular sciences, 18(1), 113. https://doi.org/10.3390/ijms18010113
Volanakis JE, Narayana SV. Complement factor D, a novel serine protease. Protein Sci. 1996 Apr;5(4):553-64. doi: 10.1002/pro.5560050401. PMID: 8845746; PMCID: PMC2143395.
Liu, D., Wang, X., Liu, Y., Li, C., Zhang, Z., & Lv, P. (2022). Actin-Binding LIM 1 (ABLIM1) Inhibits Glioblastoma Progression and Serves as a Novel Prognostic Biomarker. Disease markers, 2022, 9516808. https://doi.org/10.1155/2022/9516808
Jin SH, Kim H, Gu DR, Park KH, Lee YR, Choi Y, Lee SH. Actin-binding LIM protein 1 regulates receptor activator of NF-κB ligand-mediated osteoclast differentiation and motility. BMB Rep. 2018 Jul;51(7):356-361. doi: 10.5483/bmbrep.2018.51.7.106. PMID: 29921413; PMCID: PMC6089868.
Nojszewska, N., Idilli, O., Sarkar, D., Ahouiyek, Z., Arroyo-Berdugo, Y., Sandoval, C., Amin-Anjum, M. S., Bowers, S., Greaves, D., Saeed, L., Khan, M., Salti, S., Al-Shami, S., Topoglu, H., Punzalan, J. K., Farias, J. G., & Calle, Y. (2023). Bone marrow mesenchymal/fibroblastic stromal cells induce a distinctive EMT-like phenotype in AML cells. European journal of cell biology, 102(3), 151334. https://doi.org/10.1016/j.ejcb.2023.151334
Cabral, F., Al-Rahem, M., Skaggs, J., Thomas, T. A., Kumar, N., Wu, Q., Fadda, P., Yu, L., Robinson, J. M., Kim, J., Pandey, E., Sun, X., Jarjour, W. N., Rajaram, M. V. S., Harris, E. N., & Ganesan, L. P. (2021). Stabilin receptors clear LPS and control systemic inflammation. iScience, 24(11), 103337. https://doi.org/10.1016/j.isci.2021.103337
Li, X., Wang, H., Jia, A., Cao, Y., Yang, L., & Jia, Z. (2023). LGALS1 regulates cell adhesion to promote the progression of ovarian cancer. Oncology letters, 26(2), 326. https://doi.org/10.3892/ol.2023.13912
Dong, X., Feng, M., Yang, H., Liu, H., Guo, H., Gao, X., Liu, Y., Liu, R., Zhang, N., Chen, R., & Kong, R. (2020). Rictor promotes cell migration and actin polymerization through regulating ABLIM1 phosphorylation in Hepatocellular Carcinoma. International journal of biological sciences, 16(15), 2835–2852. https://doi.org/10.7150/ijbs.46285
Fischer, I., Schulze, S., Kuhn, C., Friese, K., Walzel, H., Markert, U. R., & Jeschke, U. (2009). Inhibiton of RET and JAK2 signals and upregulation of VEGFR3 phosphorylation in vitro by galectin-1 in trophoblast tumor cells BeWo. Placenta, 30(12), 1078–1082. https://doi.org/10.1016/j.placenta.2009.10.003
Vu, H. A., Xinh, P. T., Kano, Y., Tokunaga, K., & Sato, Y. (2009). The juxtamembrane domain in ETV6/FLT3 is critical for PIM-1 up-regulation and cell proliferation. Biochemical and biophysical research communications, 383(3), 308–313. https://doi.org/10.1016/j.bbrc.2009.03.157
Hou, X., Guo, Q., Wei, W., Guo, L., Guo, D., & Zhang, L. (2018). Screening of Genes Related to Early and Late Flowering in Tree Peony Based on Bulked Segregant RNA Sequencing and Verification by Quantitative Real-Time PCR. Molecules (Basel, Switzerland), 23(3), 689. https://doi.org/10.3390/molecules23030689
Ito, K., Patel, P. N., Gorham, J. M., McDonough, B., DePalma, S. R., Adler, E. E., Lam, L., MacRae, C. A., Mohiuddin, S. M., Fatkin, D., Seidman, C. E., & Seidman, J. G. (2017). Identification of pathogenic gene mutations in LMNA and MYBPC3 that alter RNA splicing. Proceedings of the National Academy of Sciences of the United States of America, 114(29), 7689–7694. https://doi.org/10.1073/pnas.1707741114
Biesma, D. H., Hannema, A. J., van Velzen-Blad, H., Mulder, L., van Zwieten, R., Kluijt, I., & Roos, D. (2001). A family with complement factor D deficiency. The Journal of clinical investigation, 108(2), 233–240. https://doi.org/10.1172/JCI12023
Gilby, D. C., Sung, H. Y., Winship, P. R., Goodeve, A. C., Reilly, J. T., & Kiss-Toth, E. (2010). Tribbles-1 and -2 are tumour suppressors, down-regulated in human acute myeloid leukaemia. Immunology letters, 130(1-2), 115–124. https://doi.org/10.1016/j.imlet.2009.12.007
Sprong, T., Roos, D., Weemaes, C., Neeleman, C., Geesing, C. L., Mollnes, T. E., & van Deuren, M. (2006). Deficient alternative complement pathway activation due to factor D deficiency by 2 novel mutations in the complement factor D gene in a family with meningococcal infections. Blood, 107(12), 4865–4870. https://doi.org/10.1182/blood-2005-07-2820

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

INTRODUCTION

Materials and Methods

Datasets and collection of data

Analysis Method

Screening of DEGs

PPI Network analysis

Gene set enrichment analysis for identification of overrepresented GO terms

RESULTS

Microarray data processing

Identification of DEGs

PPI network and modular analysis

DISCUSSION

CONCLUSION

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1