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

Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data Hassan Aboudi Hassan Al-Sayegh, Reza Safaralizadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4916069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 INTRODUCTION 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. Materials and Methods 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. RESULTS 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 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 0.05 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.‌‌ DISCUSSION 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. CONCLUSION 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. Abbreviations 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. Declarations 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. References 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 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4916069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":352173969,"identity":"975b91ae-0d53-4a60-b1dc-1c71640a89b1","order_by":0,"name":"Hassan Aboudi Hassan Al-Sayegh","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"Aboudi Hassan","lastName":"Al-Sayegh","suffix":""},{"id":352173970,"identity":"265a5e83-1bdd-46f1-ab2c-a19885bca00d","order_by":1,"name":"Reza Safaralizadeh","email":"data:image/png;base64,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","orcid":"","institution":"University of Tabriz, University","correspondingAuthor":true,"prefix":"","firstName":"Reza","middleName":"","lastName":"Safaralizadeh","suffix":""}],"badges":[],"createdAt":"2024-08-14 22:01:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4916069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4916069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64903145,"identity":"b2cfbc6d-c569-4fc4-a3ff-be17fccf519e","added_by":"auto","created_at":"2024-09-20 08:27:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3383620,"visible":true,"origin":"","legend":"\u003cp\u003eThe heat map plot shows the expression of genes in GSE9476 (A), GSE48558 (B) and GSE63270 (C), which are designated by the \"heatmap\" package.\u003c/p\u003e","description":"","filename":"Finalheatmap.png","url":"https://assets-eu.researchsquare.com/files/rs-4916069/v1/b95296798f1c49f43324cce9.png"},{"id":64903147,"identity":"deb289ad-cf87-4888-abca-b65981430194","added_by":"auto","created_at":"2024-09-20 08:27:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1224999,"visible":true,"origin":"","legend":"\u003cp\u003eThe volcano plot chart shows the expression of genes in GSE9476 (A), GSE48558 (B), GSE63270 (C), which is marked by the \"gggplot2\" package, red inscriptions show the increase of expression and blue inscriptions show the decrease in expression. D) Venn diagram of genes with different expression that had overlap expression in three different datasets. Red entries are genes that have not been investigated in AML.\u003c/p\u003e","description":"","filename":"PLOTV.png","url":"https://assets-eu.researchsquare.com/files/rs-4916069/v1/3505d9d97e5e8b56ce72de8a.png"},{"id":64903148,"identity":"6b076894-c656-4aad-86a2-8fb8828aea3c","added_by":"auto","created_at":"2024-09-20 08:27:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2323371,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of protein with protein and other interactions drawn by cystoscope and with Gene Mania app.\u003c/p\u003e\n\u003cp\u003eA) GO biological processes.\u003c/p\u003e\n\u003cp\u003eB) GO cellular components\u003c/p\u003e\n\u003cp\u003eC) Automagical*\u003c/p\u003e\n\u003cp\u003eD) Molecular Function-based\u003c/p\u003e\n\u003cp\u003e*Automatically selected weighting method If your input gene list has 5 or more genes, GeneMANIA assigns weights based to maximize connectivity between all input genes using the ‘assigned based on query gene’ strategy.\u003c/p\u003e","description":"","filename":"Finalinteraction.png","url":"https://assets-eu.researchsquare.com/files/rs-4916069/v1/da89ed249fea0ab3b615f054.png"},{"id":67952986,"identity":"0e54f19b-c6b6-49b4-af63-758f14ddbcac","added_by":"auto","created_at":"2024-10-31 15:47:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7632481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4916069/v1/d3458deb-0e73-4330-95d8-448bdd24115f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Regarding the prevalence of the mentioned morbidity in various stages of age, adults are mostly affected with an onset in 18 to 85 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Meanwhile, regarding the wide spectrum of cancers, leukemia is not common and includes about one percent of the cancers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with other types of cancers, environmental factors and genetic predisposition are involved in the pathogenesis of AML [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The mentioned factors consequently emerge as changes in genome and intermittent-uncontrolled proliferation of blood stem cells and malignancy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy 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.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatasets and collection of data\u003c/h2\u003e \u003cp\u003eTo identify microarray-based gene expression profiles for acute myeloid leukemia (AML), a comprehensive search strategy was employed using the PubMed database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pubmed.com\u003c/span\u003e\u003cspan address=\"http://www.pubmed.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the ArrayExpress dataset from the European Molecular Biology Laboratory-European Bioinformatics Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/arrayexpress/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/arrayexpress/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Three datasets were selected from the GEO database for this study: GSE9476 (N\u0026thinsp;=\u0026thinsp;64, GPL96), GSE48558 (N\u0026thinsp;=\u0026thinsp;170, GPL6244), and GSE63270 (N\u0026thinsp;=\u0026thinsp;104, GPL17810). Samples from AML subgroups were selected from these datasets.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion and exclusion criteria\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Method\u003c/h2\u003e \u003cp\u003eThe analysis was conducted using R software (version 4.3.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [16\u0026thinsp;\u0026minus;\u0026thinsp;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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eScreening of DEGs\u003c/h2\u003e \u003cp\u003eThe \"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)\u0026thinsp;\u0026ge;\u0026thinsp;1 and an adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. A heatmap was generated using data from genes with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |logFC| \u0026ge; 1.5, with gene expression visualized using the \"pheatmap\" package (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, a volcano plot was created using the \"ggplot2\" package to further investigate expression changes based on logFC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePPI Network analysis\u003c/h2\u003e \u003cp\u003eThe analysis of the PPI network was conducted using Network Analyst [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In these networks, proteins are represented as nodes, while edges illustrate the known interactions between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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 \u0026lsquo;hairball effect\u0026rsquo; 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. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis for identification of overrepresented GO terms\u003c/h2\u003e \u003cp\u003eGene 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 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toppgene.cchmc.org\u003c/span\u003e\u003cspan address=\"https://toppgene.cchmc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e 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\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data processing\u003c/h2\u003e \u003cp\u003eThis 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (healthy\u0026thinsp;=\u0026thinsp;38 and AML\u0026thinsp;=\u0026thinsp;26, respectively), GSE63270 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (healthy\u0026thinsp;=\u0026thinsp;42 and AML\u0026thinsp;=\u0026thinsp;62, respectively), as well as subjects affected by AML with sub-groups and percentages of various blast cells and GSE48558 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (healthy\u0026thinsp;=\u0026thinsp;43 and AML\u0026thinsp;=\u0026thinsp;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.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eThe 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\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and absolute log2 fold change (|logFC|)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 are determined to be shared DEGs in at least two of the three datasets.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] To show the amount of expression of these genes, a volcano plot was used (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe up-regulated genes are documented as below:\u003c/p\u003e \u003cp\u003eLGALS1 (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\u0026rsquo;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.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModels for Genes with Different Expression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePubMed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCFD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplement Factor D Deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplement Activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[34,35]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTRIB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eacute myeloid leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAPK pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eABLIM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatocellular Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecell migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLGALS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrophoblastic Neoplasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eapoptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[46]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFLT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic Myelomonocytic Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecell proliferation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHOMER3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeta-Catenin activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[48]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLMNA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandibulofacial Dysplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRNA splicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSTAB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipopolysaccharides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eendocytose LPS-HDL complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and modular analysis\u003c/h2\u003e \u003cp\u003eData 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.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Hence p- value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown in the table (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene ontology related to genes with DGEs as well as pathways related to them\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO: Molecular Function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eq-value FDR B\u0026amp;H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHit in Query List\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHit Count in Genome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0030395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elactose binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evascular endothelial growth factor receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0048030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edisaccharide binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egalactose binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egalactoside binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow-density lipoprotein particle receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0031625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eubiquitin protein ligase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3,TRIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0044389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eubiquitin-like protein ligase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3,TRIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0031434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emitogen-activated protein kinase kinase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTRIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0030169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow-density lipoprotein particle binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0030228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elipoprotein particle receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0035259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enuclear glucocorticoid receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0070492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoligosaccharide binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0061629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA polymerase II-specific DNA-binding transcription factor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3,TRIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehyaluronic acid binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0071813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elipoprotein particle binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0071814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein-lipid complex binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0055106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eubiquitin-protein transferase regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTRIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0043236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elaminin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein phosphatase 1 binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0015035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein-disulfide reductase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0043548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephosphatidylinositol 3-kinase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0015036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edisulfide oxidoreductase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGO: Cellular Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:1990724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egalectin complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elamin filament\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0098635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein complex involved in cell-cell adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathways of DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq-value FDR B\u0026amp;H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHit in Query List\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHit Count in Genome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM42576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_ACQUIRED_PARTIAL_LIPODYSTROPHY_BARRAQUER_SIMONS_SYNDROME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.03E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA,CFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_ADIPOGENESIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA,CFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM15970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_ADIPOGENESIS_GENES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA,CFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM27024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_ALTERNATIVE_COMPLEMENT_ACTIVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM14674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_ALTERNATIVE_COMPLEMENT_ACTIVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_FLT3_SIGNALING_THROUGH_SRC_FAMILY_KINASES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_FLT3_SIGNALING_BY_CBL_MUTANTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM27953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_STAT5_ACTIVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_ALTERNATIVE_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM22072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_ALTERNATIVE_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_STAT5_ACTIVATION_DOWNSTREAM_OF_FLT3_ITD_MUTANTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM15105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_DEPOLYMERIZATION_OF_THE_NUCLEAR_LAMINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_VARIANT_DUPLICATION_OR_MUTATION_ACTIVATED_FLT3_TO_RAS_ERK_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_VARIANT_DUPLICATION_OR_MUTATION_ACTIVATED_FLT3_TO_RAS_PI3K_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_DCC_MEDIATED_ATTRACTIVE_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABLIM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_REFERENCE_FLT3LG_FLT3_RAS_ERK_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_REFERENCE_FLT3LG_FLT3_RAS_PI3K_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_NEGATIVE_REGULATION_OF_FLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM9367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_ERYTH_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM27360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_DEPOLYMERIZATION_OF_THE_NUCLEAR_LAMINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_SIGNALING_BY_FLT3_ITD_AND_TKD_MUTANTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_COMP_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM14920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_ERYTH_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM29617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_COMP_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_HEMATOPOIETIC_STEM_CELL_GENE_REGULATION_BY_GABP_ALPHA_BETA_COMPLEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM48100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_7Q11_23_DISTAL_COPY_NUMBER_VARIATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM42534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_PROGERIA_ASSOCIATED_LIPODYSTROPHY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_COMPLEMENT_ACTIVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM17902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_CASPASE_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_CASPASE_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_REFERENCE_REGULATION_OF_GF_RTK_RAS_ERK_SIGNALING_UBIQUITINATION_OF_RTK_BY_CBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM42527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_DEFECTIVE_INTRINSIC_PATHWAY_FOR_APOPTOSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_WNT_BETA_CATENIN_SIGNALING_PATHWAY_IN_LEUKEMIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM47438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_MEDICUS_VARIANT_MLL_AF4_FUSION_TO_TRANSCRIPTIONAL_ACTIVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG Medicus Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_TNFR1_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_FAS_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_FLT3_SIGNALING_IN_DISEASE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM14971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_DEATH_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_TNFR1_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM42537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_FAMILIAL_PARTIAL_LIPODYSTROPHY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePID_RAS_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePID Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM9503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_FAS_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM27547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_DISEASES_OF_SIGNAL_TRANSDUCTION_BY_GROWTH_FACTOR_RECEPTORS_AND_SECOND_MESSENGERS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA,FLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM1422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIOCARTA_DEATH_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioCarta Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM15967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_FAS_PATHWAY_AND_STRESS_INDUCTION_OF_HSP_REGULATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMM14488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_INFLUENCE_OF_LAMINOPATHIES_ON_WNT_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM29803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_FLT3_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactome Pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM46456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP_IMMUNE_INFILTRATION_IN_PANCREATIC_CANCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWikiPathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLGALS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePPI 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.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] 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\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e 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.\u0026zwnj;\u0026zwnj;\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo 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\u0026thinsp;\u0026minus;\u0026thinsp;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\u0026thinsp;\u0026minus;\u0026thinsp;8]. This catastrophic cycle is followed by uncontrolled proliferation of blood stem cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs 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\u0026zwnj;\u0026zwnj; [11\u0026ndash;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\u0026ndash;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].\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003eWith 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt 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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CFD gene revealed an average increase in expression log FC\u0026thinsp;=\u0026thinsp;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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].​\u0026zwnj;\u003c/p\u003e \u003cp\u003eLikewise, 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 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIt 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\u0026thinsp;=\u0026thinsp;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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This finding suggests that LGALS1 may induce EMT at AML by imposing on ABLIM1, however research in needed to confirm these preliminary findings.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eTaken 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABLIM1, 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.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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.\u0026nbsp;\u003cbr\u003e\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eThe authors\u0026rsquo; 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCancer Statistics Center. (n.d.). *Cancer Statistics Center*. American Cancer Society. https://cancerstatisticscenter.cancer.org\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eSamoylov, A. S., Bushmanov, A. Y., Udalov, Y. D., Galstyan, I. A., Nugis, V. Y., Kozlova, M. G., Nikitina, V. A., Khvostunov, I. K., \u0026amp; 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\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E., \u0026amp; Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 17-48. doi:10.3322/caac.21763\u003c/li\u003e\n\u003cli\u003eSavage, S. A., \u0026amp; Walsh, M. F. (2018). Myelodysplastic Syndrome, Acute Myeloid Leukemia, and Cancer Surveillance in Fanconi Anemia. Hematology/oncology clinics of North America, 32(4), 657\u0026ndash;668. https://doi.org/10.1016/j.hoc.2018.04.002\u003c/li\u003e\n\u003cli\u003eLindvall, C., Nordenskj\u0026ouml;ld, M., Porwit, A., Bj\u0026ouml;rkholm, M., \u0026amp; Blennow, E. (2001). Molecular cytogenetic characterization of acute myeloid leukemia and myelodysplastic syndromes with multiple chromosome rearrangements. Haematologica, 86(11), 1158\u0026ndash;1164.\u003c/li\u003e\n\u003cli\u003ePaperna, 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., \u0026amp; 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\u0026ndash;504. https://doi.org/10.1136/jmedgenet-2018-105824\u003c/li\u003e\n\u003cli\u003eImashuku, S., Teramura, T., Kuriyama, K., Kitazawa, J., Ito, E., Morimoto, A., \u0026amp; 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\u0026ndash;177. https://doi.org/10.1007/BF02982023\u003c/li\u003e\n\u003cli\u003eTashakori, 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., \u0026amp; Khoury, J. D. (2022). TP53 copy number and protein expression inform mutation status across risk categories in acute myeloid leukemia. Blood, 140(1), 58\u0026ndash;72. https://doi.org/10.1182/blood.2021013983 \u003c/li\u003e\n\u003cli\u003eWallwitz, J., Aigner, P., \u0026amp; Stoiber, D. (2021). Tumor suppressors in acute myeloid leukemia. Leukemia \u0026amp; lymphoma, 62(10), 2320\u0026ndash;2330. https://doi.org/10.1080/10428194.2021.1907372\u003c/li\u003e\n\u003cli\u003eYi, M., Li, A., Zhou, L., Chu, Q., Song, Y., \u0026amp; 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 \u0026amp; oncology, 13(1), 72. https://doi.org/10.1186/s13045-020-00908-z\u003c/li\u003e\n\u003cli\u003eFalini, B., \u0026amp; 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\u0026ndash;492. https://doi.org/10.1002/ajh.26812\u003c/li\u003e\n\u003cli\u003eHuber, S., Baer, C., Hutter, S. et al. AML classification in the year 2023: How to avoid a Babylonian confusion of languages. Leukemia 37, 1413\u0026ndash;1420 (2023). https://doi.org/10.1038/s41375-023-01909-w\u003c/li\u003e\n\u003cli\u003eKolur, V., Vastrad, B., Vastrad, C., Kotturshetti, S., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003ePollyea, 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., \u0026hellip; Tallman, M. S. (2021). NCCN Guidelines Insights: Acute Myeloid Leukemia, Version 2.2021. Journal of the National Comprehensive Cancer Network : JNCCN, 19(1), 16\u0026ndash;27. https://doi.org/10.6004/jnccn.2021.0002\u003c/li\u003e\n\u003cli\u003eLiu H. (2021). Emerging agents and regimens for AML. Journal of hematology \u0026amp; oncology, 14(1), 49. https://doi.org/10.1186/s13045-021-01062-w\u003c/li\u003e\n\u003cli\u003eKoenig, K. L., Sahasrabudhe, K. D., Sigmund, A. M., \u0026amp; Bhatnagar, B. (2020). AML with Myelodysplasia-Related Changes: Development, Challenges, and Treatment Advances. Genes, 11(8), 845. https://doi.org/10.3390/genes11080845\u003c/li\u003e\n\u003cli\u003eHansen, B. A., Wendelbo, \u0026Oslash;., Bruserud, \u0026Oslash;., Hemsing, A. L., Mosevoll, K. A., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eJentzsch, 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\u003c/li\u003e\n\u003cli\u003eZeng, H., Liu, X., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003ePrada-Arismendy, J., Arroyave, J. C., \u0026amp; R\u0026ouml;thlisberger, S. (2017). Molecular biomarkers in acute myeloid leukemia. Blood reviews, 31(1), 63\u0026ndash;76. https://doi.org/10.1016/j.blre.2016.08.005\u003c/li\u003e\n\u003cli\u003eFeng, H., Gu, Z. Y., Li, Q., Liu, Q. H., Yang, X. Y., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eZhang 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.\u003c/li\u003e\n\u003cli\u003eLuo, 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\u003c/li\u003e\n\u003cli\u003eStirewalt, 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., \u0026amp; Radich, J. P. (2008). Identification of genes with abnormal expression changes in acute myeloid leukemia. Genes, chromosomes \u0026amp; cancer, 47(1), 8\u0026ndash;20. https://doi.org/10.1002/gcc.20500\u003c/li\u003e\n\u003cli\u003eJung, N., Dai, B., Gentles, A. J., Majeti, R., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eCramer-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., M\u0026uuml;schen, M., Civin, C. I., \u0026amp; Skorski, T. (2013). Personalized synthetic lethality induced by targeting RAD52 in leukemias identified by gene mutation and expression profile. Blood, 122(7), 1293\u0026ndash;1304. https://doi.org/10.1182/blood-2013-05-501072\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Connor, C., Lohan, F., Campos, J., Ohlsson, E., Salom\u0026egrave;, M., Forde, C., Artschwager, R., Liskamp, R. M., Cahill, M. R., Kiely, P. A., Porse, B., \u0026amp; Keeshan, K. (2016). The presence of C/EBP\u0026alpha; and its degradation are both required for TRIB2-mediated leukaemia. Oncogene, 35(40), 5272\u0026ndash;5281. https://doi.org/10.1038/onc.2016.66\u003c/li\u003e\n\u003cli\u003eRuvolo, P. P., Ma, H., Ruvolo, V. R., Zhang, X., Post, S. M., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eLv, K., Ren, J. G., Han, X., Gui, J., Gong, C., \u0026amp; Tong, W. (2021). Depalmitoylation rewires FLT3-ITD signaling and exacerbates leukemia progression. Blood, 138(22), 2244\u0026ndash;2255. https://doi.org/10.1182/blood.2021011582\u003c/li\u003e\n\u003cli\u003eZhang, J., Liu, T., Wang, Y., Yan, X., Li, Y., Xu, F., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eAlshaalan 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\u003c/li\u003e\n\u003cli\u003eLin, S. Y., Hu, F. F., Miao, Y. R., Hu, H., Lei, Q., Zhang, Q., Li, Q., Wang, H., Chen, Z., \u0026amp; 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\u0026ndash;484. https://doi.org/10.1016/j.omtn.2019.09.014\u003c/li\u003e\n\u003cli\u003eEzure, T., Sugahara, M., \u0026amp; 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\u0026ndash;562. https://doi.org/10.1002/biof.1512\u003c/li\u003e\n\u003cli\u003eTripodo, 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., \u0026amp; Sangaletti, S. (2017). Persistent Immune Stimulation Exacerbates Genetically Driven Myeloproliferative Disorders via Stromal Remodeling. Cancer research, 77(13), 3685\u0026ndash;3699. https://doi.org/10.1158/0008-5472.CAN-17-1098\u003c/li\u003e\n\u003cli\u003eWang, C., Chen, Z., Li, Z., \u0026amp; 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\u0026ndash;1090. https://doi.org/10.1016/j.leukres.2010.01.016\u003c/li\u003e\n\u003cli\u003eZhang, H., Fei, R., Xue, B., Yu, S., Zhang, Z., Zhong, S., Gao, Y., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eVolanakis 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.\u003c/li\u003e\n\u003cli\u003eLiu, D., Wang, X., Liu, Y., Li, C., Zhang, Z., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eJin SH, Kim H, Gu DR, Park KH, Lee YR, Choi Y, Lee SH. Actin-binding LIM protein 1 regulates receptor activator of NF-\u0026kappa;B 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.\u003c/li\u003e\n\u003cli\u003eNojszewska, 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., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eCabral, 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., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eLi, X., Wang, H., Jia, A., Cao, Y., Yang, L., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eDong, X., Feng, M., Yang, H., Liu, H., Guo, H., Gao, X., Liu, Y., Liu, R., Zhang, N., Chen, R., \u0026amp; 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\u0026ndash;2852. https://doi.org/10.7150/ijbs.46285\u003c/li\u003e\n\u003cli\u003eFischer, I., Schulze, S., Kuhn, C., Friese, K., Walzel, H., Markert, U. R., \u0026amp; 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\u0026ndash;1082. https://doi.org/10.1016/j.placenta.2009.10.003\u003c/li\u003e\n\u003cli\u003eVu, H. A., Xinh, P. T., Kano, Y., Tokunaga, K., \u0026amp; 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\u0026ndash;313. https://doi.org/10.1016/j.bbrc.2009.03.157\u003c/li\u003e\n\u003cli\u003eHou, X., Guo, Q., Wei, W., Guo, L., Guo, D., \u0026amp; 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\u003c/li\u003e\n\u003cli\u003eIto, 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., \u0026amp; 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\u0026ndash;7694. https://doi.org/10.1073/pnas.1707741114\u003c/li\u003e\n\u003cli\u003eBiesma, D. H., Hannema, A. J., van Velzen-Blad, H., Mulder, L., van Zwieten, R., Kluijt, I., \u0026amp; Roos, D. (2001). A family with complement factor D deficiency. The Journal of clinical investigation, 108(2), 233\u0026ndash;240. https://doi.org/10.1172/JCI12023\u003c/li\u003e\n\u003cli\u003eGilby, D. C., Sung, H. Y., Winship, P. R., Goodeve, A. C., Reilly, J. T., \u0026amp; Kiss-Toth, E. (2010). Tribbles-1 and -2 are tumour suppressors, down-regulated in human acute myeloid leukaemia. Immunology letters, 130(1-2), 115\u0026ndash;124. https://doi.org/10.1016/j.imlet.2009.12.007\u003c/li\u003e\n\u003cli\u003eSprong, T., Roos, D., Weemaes, C., Neeleman, C., Geesing, C. L., Mollnes, T. E., \u0026amp; 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\u0026ndash;4870. https://doi.org/10.1182/blood-2005-07-2820\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute myeloid leukemia, differentially expressed genes, Bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-4916069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4916069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute 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\u0026ndash;protein interaction (PPI) to investigate the underpinned molecular and genetic mechanisms of AML.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur bioinformatics analysis revealed that among the explored genes, CFD and ABLIM1 were linked to AML.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIt is concluded that ABLIM1 and CFD genes are associated with the presence and progression of AML, even in different subtypes of the disease.\u003c/p\u003e","manuscriptTitle":"Identification of Novel Genes Implicated in Acute Myeloid Leukemia Progression using Bioinformatics Analysis of Microarray Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 08:27:52","doi":"10.21203/rs.3.rs-4916069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e69d9f60-8897-40d1-97f1-65a2527fadc1","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-31T15:39:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-20 08:27:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4916069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4916069","identity":"rs-4916069","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}