In silico analysis of genes and pathways related to acute myeloid leukemia presenting leukopenia

In silico analysis of genes and pathways related to acute myeloid leukemia presenting leukopenia | 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 Research Article In silico analysis of genes and pathways related to acute myeloid leukemia presenting leukopenia Ajeet Kumar, Ravi Bhushan, Pawan K. Dubey, Vijai Tilak, Vineeta Gupta, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1019863/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Acute myeloid leukemia (AML) is the progenitor and hematopoietic stem cell blood cancer. Chromosomal abnormalities include balanced translocations between two chromosomes like t[8;21] and t[15;17]) in malignant cells. The current study aimed to investigate AML with leukopenia and gene expression changes in high-white and low-white counts B-cell. The total number of samples used was ten. The raw gene expression profiles (ID: GSE20482) of bone marrow obtained from AML patients showed five high-white counts B-cell and five low-white counts B-cell expressed genes differentially expressed. Genes that corresponded to official gene symbols were selected for protein-protein interaction (PPI) and sub-network construction (score > 0.4). In addition, functional annotation of gene ontology (GO) and pathway analysis were performed for the genes involved in networking. Results A total of 846 genes were identified as differentially expressed, and 406 genes were upregulated; and another 440 genes were downregulated. The remaining 14 genes that interacted with each other were significantly identified. The hub genes GNB4, LAMTOR2, ACTN4, HGSNAT, and TMED1 were upregulated while the downregulated DEGs forming hub nodes were UBR4, FBXO30, KLHL21, DCTN6, RNF123, RNF114. AML significantly affects the expression of genes involved in cell differentiation, apoptosis, cell signaling, and protein modification. AML cells enter the blood quickly and spread to the liver, spleen, and central nervous system. These are a total of thirteen pathways. They were enriched, and AML was found to significantly impact genes involved in oxidative phosphorylation, actin cytoskeleton regulation, endocytosis, phagocytosis, shigellosis, and epithelial cell signaling in helicobacter, adherent junction, pertussis, bile secretion, malaria, and African trypanosomiasis. Conclusions Hub genes like GNB4 and UBR4 provide a novel biomarker in AML. Acute myeloid leukemia Leukopenia Biomarker gene ontology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Acute myeloid leukemia (AML) is a heterogeneous malignant disorder of the hematopoietic stem cells these are characterized by abnormal proliferation of the immature cell, blast cells, and disabling of the production of normal blood cells [ 1 ]. AML is a genetic disease first established by Janet Rowley, who discovered the somatic chromosomal abnormalities in the leukemic cell of the patient sample, and they show the balanced translocations of chromosome (i.e., t[8;21] and t[15;17]) [ 2 ]. AML affects people worldwide, such as leukemia, lymphoma, and multiple myelomas. It is the most common type of leukemia in adults and has the lowest survival rate of all different kinds of leukemia. AML is a rare disease. They are highly malignant neoplasms and supervise a large number of cancer-related deaths. Approximately 25% of all leukemia in adults in the West constitutes the most frequent form of leukemia. In AML the chromosomal abnormalities are found in t(8;21), t(15;17), inv (16), and 11q23. These aberrations produce PML-RARA, RUNX1-RUNX1T1, CBF-MYH11, and MLL-fusion genes. Mutations generally occur in transcription factors, signaling of molecules, tumor suppressor genes, epigenetic regulators, RNA splicing factors, and cohesion complexes, with FLT3, NPM1, and DNMT3A the most frequently mutated genes in AML. Cytogenetics provides robust diagnostic information and provides the framework for risk stratification in AML patients, and it has several limitations[ 3 ]. Some technical failures, like cytogenetics, cannot relate the gene fusions, for example, NUP98-NSD1, CBFA2T3-GLIS2, and MNX1-ETV6, which forecast the poor outcome in pediatric patients of AML[ 4 ] , [ 5 ].AML was initially subdivided based on morphology (French-American-British system). Those are helpful for the categorization of pathology. Later, some genomics and clinical factors are initiated to coordinate with chemotherapy's reaction and overall survival. In 2017 the European Leukemia Net (ELN) separated into three prognostic groups based on genetic mutation and abnormalities in acute myeloid leukemia. These are categorized as favorable, intermediate, and adverse[ 6 ]. Material And Methodology 2.1. Differentially expressed genes and Microarray data Screening in AML The raw data procured from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) datasets ( http://www.ncbi.nlm.nih.gov/geo/ ) and gene expression profile datasets obtained from (GENE ID: GSE20482). Hematological stem cells, bone marrow, and peripheral blood from AML patients with leukopenia were used in this study. The datasets derived from a study utilize the GPL6848 Agilent-012391 Whole Human Genome Oligo Microarray G4112A platform. These studies are only on genes of high and low blood count B-cell samples analyzed through bioinformatics techniques. 2.2. Preprocessing of data and screening of DEGs in AML Gene expression datasets were obtained from NCBI and further these data were used for pre-processed. The expression values of probes communicate to a particular gene have to mean calculated to find the advantage of gene expression. Additionally, up and down-regulated genes were distinguished by using BiGGEsTs software analysis. The probe-level ideogram is transformed into a gene-level ideogram by using GEO2R ( https://www.ncbi.nlm.nih.gov/geo/geo2r/ ). These DEGs pick out, calibrate p values 0.1 for up and < − 0.1 for down-regulated genes. 2.3 Principal component analysis and heat map generation in AML Principal component analysis (PCA) was executed, and constructed the heat map using the ClustVis software available online and plotted the PCA graph on DEGs data. ClustVis support maximum file size is larger, so it is not feasible to assemble the PCA plot for all gene expression studies. 2.4 PPI network creation and generation of sub-network in AML DEGs data were uploaded to the STRING v 10.5 ( http://www . string-db.org/). It is an online software dataset for anticipating the functional exchange between the proteins and predicting a collaborated outcome for protein-protein interaction among gene positions, and the combined outcome N 0.4 was set as the standard. Prepare for modeling networking and sub-network; we used Cytoscape v 3.2.1. Cytoscape ( http://www.cytoscape.org/ ) is a bioinformatics software for the investigation and visualization of biological networks with high throughput data. The assemble and edge coefficient were taken as standard for network construction. 2.5 Analysis of differentially functional expressed genes in AML The database for annotation, visualization, and integrated discovery (DAVID, http://david.abcc.ncifcrf.gov/ ) draft of action is a database combined with a comprising set of functional interpretations and a huge genes list. Gene ontology (GO) amplification performed as well as biological, molecular, and cellular modules were implemented by using DAVID v 6.8 and STRING v 10.5. Based on hypergeometric distribution, DAVID clutches the genes with exactly similar functions or related as a whole set. DEGs related pathways analysis and completed with the Panther Classification System ( http://pantherdb.org/ ) 2.6 Analysis of Interaction network Biological General Repository for Interaction Datasets (BioGRID) tools are used to identify ( https://wiki.thebiogrid.org/doku.php/ORCS:tools ) the possible interconnection for acute myeloid leukemia. BioGRID is curated for biological databases like protein-protein interactions and acute myeloid leukemia's genetic or chemical interactions. 2.7 Survival analysis Overall survival analysis for selected hub genes was performed using GEPIA V2.0. The association between mRNA prognosis and expression of acute myeloid leukemia. Results 3.1. Differentially expressed genes (DEGs) in AML 846 DEGs were allowed with their formal gene ideogram and up and down-regulated DEGs 406 & 440. Their p-value is < .05. (Supplementary file 1). Fourteen genes only were recognized to remarkably interconnect with each other. Mean of AML B cell high and low B cell shown in Fig. 1. DEGs were affected based on their common gene expression value. 3.2 Principal component and hierarchical clustering analysis of DEGs in AML PCA plot shows a distributed plot with axes communicated to two independent principal components, 1 and 2, shown in Fig. 2A. Another shows a Heat-map data matrix in which shade summarizes the arithmetic divergence. A heat map was fabricated for differentially expressed genes, shown in Fig. 2B . Conceive of screened DEGs along with assembling of Volcano plot shows in Fig. 3A. MD plot releases the up and down-regulated genes shown in Fig. 3B . Venn diagram showing the overhang between GO terms down-regulated and up-regulated in the AML shows in Fig. 3C . Total 198 genes were set up, the novel up-regulated and 235 genes were set up the novel down regulated. DEGs were determined based upon standard gene expression value shows in Fig. 3D . 3.3 Protein-protein interaction meshwork in AML Protein-protein interaction (PPI) of DEGs shown in Fig. 4 . Pink and blue circle represents up and down-regulated genes. Based on the combined score estimated through STRING. Seventy-six gene pairs (integrated score N 0.4) were elevated to link together and constitute one prime network with 69 nodes and 381 edges. Total five sub-networks will eliminate independently. The up-regulated DEGs and down-regulated DEGs forming hub nodes were GNB4, LAMTOR2, ACTN4, HGSNAT, TMED1, and UBR4 FBXO30, KLHL21, DCTN6, RNF123, RNF114. Clustering coefficient and edge were appropriated as the basis of the selection criteria of hub nodes. 3.4 Construction of Sub-network in AML Five sub-networks (3 nodes and three edges in network1; 2 nodes and one boundary in networks 2 and 3 both) were pulled out from the primary network beside using Cytoscape shown in Fig. 5. Each gene in network A was up-regulated, and network B was down-regulated. Another network, C, D , and E lines, shows the correlation between up and downregulated genes. 3.5 Analysis of functional enrichment in AML Analysis of functional enrichment was carried out and remarkably improved their processes, functions, and components of DEGs (FDR b 0.05) were recorded in Tables 1 and 2 results of GO shown in Fig. 6. Table 1 Gene Ontology (GO) analysis of up regulated DEGs. Category Term PValue FDR GOTERM_BP_FAT GO:0006796 ~ phosphate-containing compound metabolic process 2.98E-06 0.007334 GOTERM_BP_FAT GO:0006793 ~ phosphorus metabolic process 3.25E-06 0.007334 GOTERM_BP_FAT GO:0032268 ~ regulation of cellular protein metabolic process 5.35E-05 0.063377 GOTERM_BP_FAT GO:0051246 ~ regulation of protein metabolic process 5.62E-05 0.063377 GOTERM_BP_FAT GO:0030029 ~ actin filament-based process 1.48E-04 0.096366 GOTERM_BP_FAT GO:0044403 ~ symbiosis, encompassing mutualism through parasitism 1.93E-04 0.096366 GOTERM_BP_FAT GO:0044419 ~ interspecies interaction between organisms 1.93E-04 0.096366 GOTERM_BP_FAT GO:0030198 ~ extracellular matrix organization 2.07E-04 0.096366 GOTERM_BP_FAT GO:0016032 ~ viral process 2.07E-04 0.096366 GOTERM_BP_FAT GO:0043062 ~ extracellular structure organization 2.14E-04 0.096366 GOTERM_BP_FAT GO:0044764 ~ multi-organism cellular process 2.37E-04 0.097261 GOTERM_BP_FAT GO:0080134 ~ regulation of response to stress 3.18E-04 0.118647 GOTERM_BP_FAT GO:0035336 ~ long-chain fatty-acyl-CoA metabolic process 3.42E-04 0.118647 GOTERM_BP_FAT GO:0007010 ~ cytoskeleton organization 3.75E-04 0.120822 GOTERM_BP_FAT GO:0035337 ~ fatty-acyl-CoA metabolic process 4.85E-04 0.142612 GOTERM_BP_FAT GO:0030036 ~ actin cytoskeleton organization 5.06E-04 0.142612 GOTERM_BP_FAT GO:0007015 ~ actin filament organization 6.11E-04 0.162057 GOTERM_BP_FAT GO:1901135 ~ carbohydrate derivative metabolic process 0.001101 0.274455 GOTERM_BP_FAT GO:0032956 ~ regulation of actin cytoskeleton organization 0.001196 0.274455 GOTERM_BP_FAT GO:0008610 ~ lipid biosynthetic process 0.001217 0.274455 GOTERM_BP_FAT GO:0033036 ~ macromolecule localization 0.001309 0.281035 GOTERM_CC_FAT GO:0070062 ~ extracellular exosome 4.89E-12 1.23E-09 GOTERM_CC_FAT GO:1903561 ~ extracellular vesicle 6.82E-12 1.23E-09 GOTERM_CC_FAT GO:0043230 ~ extracellular organelle 6.98E-12 1.23E-09 GOTERM_CC_FAT GO:0031988 ~ membrane-bounded vesicle 1.59E-10 2.10E-08 GOTERM_CC_FAT GO:0044421 ~ extracellular region part 1.61E-06 1.71E-04 GOTERM_CC_FAT GO:0005925 ~ focal adhesion 6.53E-06 5.63E-04 GOTERM_CC_FAT GO:0005924 ~ cell-substrate adherens junction 7.45E-06 5.63E-04 GOTERM_CC_FAT GO:0030055 ~ cell-substrate junction 9.21E-06 6.09E-04 GOTERM_CC_FAT GO:0005912 ~ adherens junction 2.85E-05 0.001435 GOTERM_CC_FAT GO:0000323 ~ lytic vacuole 2.98E-05 0.001435 GOTERM_CC_FAT GO:0005764 ~ lysosome 2.98E-05 0.001435 GOTERM_CC_FAT GO:0070161 ~ anchoring junction 4.61E-05 0.002033 GOTERM_CC_FAT GO:0005576 ~ extracellular region 1.56E-04 0.006356 GOTERM_CC_FAT GO:0005773 ~ vacuole 2.02E-04 0.007627 GOTERM_MF_FAT GO:0008092 ~ cytoskeletal protein binding 8.72E-05 0.076204 GOTERM_MF_FAT GO:0019899 ~ enzyme binding 7.15E-04 0.196724 GOTERM_MF_FAT GO:0016818 ~ hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides 0.00115 0.196724 GOTERM_MF_FAT GO:0016817 ~ hydrolase activity, acting on acid anhydrides 0.001195 0.196724 GOTERM_MF_FAT GO:0003924 ~ GTPase activity 0.001305 0.196724 GOTERM_MF_FAT GO:0019901 ~ protein kinase binding 0.001807 0.196724 GOTERM_MF_FAT GO:0017111 ~ nucleoside-triphosphatase activity 0.001865 0.196724 GOTERM_MF_FAT GO:0016779 ~ nucleotidyltransferase activity 0.002001 0.196724 GOTERM_MF_FAT GO:0016462 ~ pyrophosphatase activity 0.002194 0.196724 GOTERM_MF_FAT GO:0019001 ~ guanyl nucleotide binding 0.002251 0.196724 GOTERM_MF_FAT GO:0044325 ~ ion channel binding 0.002533 0.201247 GOTERM_MF_FAT GO:0005525 ~ GTP binding 0.002974 0.216623 GOTERM_MF_FAT GO:0003779 ~ actin binding 0.004198 0.282254 GOTERM_MF_FAT GO:0032561 ~ guanyl ribonucleotide binding 0.00513 0.305882 GOTERM_MF_FAT GO:0044769 ~ ATPase activity, coupled to transmembrane movement of ions, rotational mechanism 0.00527 0.305882 GOTERM_MF_FAT GO:0032403 ~ protein complex binding 0.0056 0.305882 GOTERM_MF_FAT GO:0051015 ~ actin filament binding 0.006499 0.328758 GOTERM_MF_FAT GO:0019900 ~ kinase binding 0.006975 0.328758 GOTERM_MF_FAT GO:0045182 ~ translation regulator activity 0.007147 0.328758 GOTERM_MF_FAT GO:0005200 ~ structural constituent of cytoskeleton 0.008236 0.359914 GOTERM_MF_FAT GO:0030371 ~ translation repressor activity 0.009062 0.374378 GOTERM_MF_FAT GO:0015078 ~ hydrogen ion transmembrane transporter activity 0.009485 0.374378 GOTERM_MF_FAT GO:0003684 ~ damaged DNA binding 0.010045 0.374378 Table 2 Gene Ontology (GO) analysis of down regulated DEGs. Category Term PValue FDR GOTERM_BP_FAT GO:0046501 ~ protoporphyrinogen IX metabolic process 3.44E-08 6.86E-05 GOTERM_BP_FAT GO:0006779 ~ porphyrin-containing compound biosynthetic process 3.87E-08 6.86E-05 GOTERM_BP_FAT GO:0006778 ~ porphyrin-containing compound metabolic process 6.72E-08 6.86E-05 GOTERM_BP_FAT GO:0015669 ~ gas transport 6.79E-08 6.86E-05 GOTERM_BP_FAT GO:0033014 ~ tetrapyrrole biosynthetic process 1.01E-07 8.12E-05 GOTERM_BP_FAT GO:0033013 ~ tetrapyrrole metabolic process 3.52E-06 0.002366 GOTERM_BP_FAT GO:0006783 ~ heme biosynthetic process 4.56E-06 0.002633 GOTERM_BP_FAT GO:0015671 ~ oxygen transport 9.93E-06 0.005014 GOTERM_BP_FAT GO:0006782 ~ protoporphyrinogen IX biosynthetic process 2.20E-05 0.009871 GOTERM_BP_FAT GO:0042168 ~ heme metabolic process 3.84E-05 0.015509 GOTERM_BP_FAT GO:0051188 ~ cofactor biosynthetic process 1.32E-04 0.048324 GOTERM_BP_FAT GO:0030218 ~ erythrocyte differentiation 1.84E-04 0.061892 GOTERM_BP_FAT GO:0048534 ~ hematopoietic or lymphoid organ development 2.22E-04 0.068828 GOTERM_BP_FAT GO:0021953 ~ central nervous system neuron differentiation 2.96E-04 0.085258 GOTERM_BP_FAT GO:0034101 ~ erythrocyte homeostasis 3.62E-04 0.09735 GOTERM_BP_FAT GO:0030097 ~ hemopoiesis 4.36E-04 0.110089 GOTERM_BP_FAT GO:0002520 ~ immune system development 5.65E-04 0.13421 GOTERM_BP_FAT GO:0055072 ~ iron ion homeostasis 7.66E-04 0.171931 GOTERM_BP_FAT GO:0021515 ~ cell differentiation in spinal cord 8.13E-04 0.172802 GOTERM_BP_FAT GO:0046148 ~ pigment biosynthetic process 8.99E-04 0.181474 GOTERM_BP_FAT GO:0002262 ~ myeloid cell homeostasis 0.001208 0.232225 GOTERM_BP_FAT GO:0018130 ~ heterocycle biosynthetic process 0.001338 0.235364 GOTERM_BP_FAT GO:0051186 ~ cofactor metabolic process 0.001341 0.235364 GOTERM_CC_FAT GO:0005833 ~ hemoglobin complex 1.30E-09 6.64E-07 GOTERM_CC_FAT GO:0014731 ~ spectrin-associated cytoskeleton 4.88E-04 0.124537 GOTERM_CC_FAT GO:0030863 ~ cortical cytoskeleton 7.42E-04 0.126063 GOTERM_CC_FAT GO:0044448 ~ cell cortex part 0.001803 0.229872 GOTERM_CC_FAT GO:0044445 ~ cytosolic part 0.005077 0.517882 GOTERM_CC_FAT GO:0008091 ~ spectrin 0.014618 1 GOTERM_CC_FAT GO:0005829 ~ cytosol 0.035669 1 GOTERM_CC_FAT GO:0005938 ~ cell cortex 0.038142 1 GOTERM_CC_FAT GO:0072562 ~ blood microparticle 0.045891 1 GOTERM_CC_FAT GO:0031672 ~ A band 0.04615 1 GOTERM_CC_FAT GO:0030864 ~ cortical actin cytoskeleton 0.053691 1 GOTERM_CC_FAT GO:0099568 ~ cytoplasmic region 0.054347 1 GOTERM_MF_FAT GO:0005344 ~ oxygen transporter activity 7.41E-06 0.006183 GOTERM_MF_FAT GO:0019825 ~ oxygen binding 6.57E-04 0.27416 GOTERM_MF_FAT GO:0003700 ~ transcription factor activity, sequence-specific DNA binding 0.00173 0.366809 GOTERM_MF_FAT GO:0001071 ~ nucleic acid binding transcription factor activity 0.001759 0.366809 GOTERM_MF_FAT GO:0008047 ~ enzyme activator activity 0.002244 0.374321 GOTERM_MF_FAT GO:0020037 ~ heme binding 0.002743 0.381242 GOTERM_MF_FAT GO:0046906 ~ tetrapyrrole binding 0.004189 0.47989 GOTERM_MF_FAT GO:0015399 ~ primary active transmembrane transporter activity 0.005179 0.47989 GOTERM_MF_FAT GO:0015405 ~ P-P-bond-hydrolysis-driven transmembrane transporter activity 0.005179 0.47989 GOTERM_MF_FAT GO:0042910 ~ xenobiotic transporter activity 0.006461 0.538849 GOTERM_MF_FAT GO:0030506 ~ ankyrin binding 0.008435 0.639508 GOTERM_MF_FAT GO:0042626 ~ ATPase activity, coupled to transmembrane movement of substances 0.011255 0.663746 GOTERM_MF_FAT GO:0016820 ~ hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substances 0.012306 0.663746 GOTERM_MF_FAT GO:0022804 ~ active transmembrane transporter activity 0.012817 0.663746 GOTERM_MF_FAT GO:0003810 ~ protein-glutamine gamma-glutamyltransferase activity 0.014862 0.663746 GOTERM_MF_FAT GO:0097159 ~ organic cyclic compound binding 0.015401 0.663746 GOTERM_MF_FAT GO:0051537 ~ 2 iron, 2 sulfur cluster binding 0.015733 0.663746 GOTERM_MF_FAT GO:0044212 ~ transcription regulatory region DNA binding 0.01618 0.663746 GOTERM_MF_FAT GO:0000975 ~ regulatory region DNA binding 0.016386 0.663746 GOTERM_MF_FAT GO:0019209 ~ kinase activator activity 0.016927 0.663746 GOTERM_MF_FAT GO:0001067 ~ regulatory region nucleic acid binding 0.017304 0.663746 3.6 Enrichment of KEGG pathway for DEGs in AML KEGG pathway enrichment examination was carried out, and remarkably up-regulated KEGG pathway enrichment for DEGs are African trypanosomiasis, malaria, bile secretion, porphyrin, and chlorophyll. Down regulated KEGG pathway enrichment for DEGs is Pertussis, adherent junction, epithelial cell signaling, shigellosis, γ R mediated phagocytosis, endocytosis, regulation of actin cytoskeleton, oxidative phosphorylation, infection of Escherichia coli shown in Fig. 7. 3.7 Survival analysis in AML For hub genes like LAMTOR2, KLHL21 and UBR4 overall survival was found to be low for their increased expression shown in Fig. 8. and Fig. 9. shown the flowchart illustrating the process of our research. Discussion The present study is a little effort to understand the molecular mechanism and its complexity involved in the AML patient. In this study, we used different bioinformatic tools to analyze high throughput gene expression datasets exposed to 846 differentially expressed genes (DEGs). The total number of the up-regulated gene was 406, while 440 genes were down-regulated. Based on GO cluster analysis, major biological processes related to DEGs were the homeostasis process, apoptosis, and protein modification. LAMTOR2, ACTN4, HGSNAT, TMED10 are up-regulated, and UBR4, FBXO30, KLHL21, DCTN6, RNF123, RNF114 were down-regulated respectively. GNB4gene is located on chromosome 3q26.33. Guanine nucleotide-binding proteins β-4, which produce signals to communicate receptors and effector molecules. Which is made up three subunits α, β and γ. Subunits of G protein are encoded by mammalian cells [ 7 ]. GNB4 gene mainly encodes through beta subunit. The β subunits are dominant regulators and as well as it also regulates signal transduction in various signaling systems. The LAMTOR2 was normally acknowledged in a yeast two-hybrid on a specific binding partner of MEK1 [ 8 ] which assemble in late endosomes beside the adaptor protein LAMTOR2 (p14)[ 9 ]. MP1 and p14 are nearly identical on structurally and very stable heterodimeric complex. Which is essential for ERK stimulation on endosomes[ 10 ],[ 11 ]. Which depends on gene disruption of p14 and p14/MP1-MEK1 signaling complex modulates the endosomal traffic, EGFR degradation and cellular proliferation [ 12 ]. These action are determining for early embryogenesis and throughout tissue homeostasis as released by specific deletion of p14 gene in epidermis [ 13 ]. ACTN4 gene is actin cross-linking protein which is encoded by human alpha-actin-4 protein. These are correlated with cell motility, invasion and metastasis in cancer[ 14 ]. Excessive expression and massive copy number extension of ACTN4 in different cancer tissue has been also reported and they are associated with the imperfect prognosis in diverse type of cancer [ 15 ]. The spectrin genes are superfamily which belongs to the alpha actin and it represents a various group of cytoskeletal proteins. Diverse roles of alpha actin are an actin-binding protein in various types of cell. Gene scramble an isoform of actinin non-muscle, which is condensed in the cytoplasm, and elaborate in metastatic processes. HGSNAT gene encodes acetyl-CoA:alpha-glucosaminide N-acetyltransferase is an enzyme that catalyzes acetylation of the terminal glucosamine residues of sulfate before its hydrolysis by alpha-N-acetyl glucosaminidase [ 16 ]. HGSNAT is located in the lysosomal membrane, and it catalyzes transmembrane acetylation in which the terminal glucosamine residue of heparan sulfate acquires an acetyl group, so it converts N-acetylglucosamine. TMED10 genes are trans-membrane proteins that can alter distinct proteins of different segments. TMED10 can form more advance oligomeric assemble in cross linking and depend upon the assemble of mIL-1β in THP-1 cells. These cells are containing a unique transmembrane domain, a luminal signal peptide (SS), a Golgi dynamics (GOLD) domain, and a C-terminal tail (CT) facing the cytoplasm. Downregulation gene The UBR4 gene encodes Ubiquitin-protein ligase UBR4 enzyme (UBR4) in humans, found in chromosome number one. It encodes the protein that appears to be a component of cytoplasm in cytoskeletal [ 17 ]. UBR4 gene mark to human papillomavirus. These related pathways are the PI3K-Akt signaling pathway and the Innate Immune System[ 18 ]. The function of UBR4 gene calmodulin, ubiquitinase ligase activity, transferase activity, binding of zinc ion, and monitoring the integrin-mediated signaling ( https://www.genecards.org/cgi-bin/carddisp.pl?gene=UBR4 ). The therapeutically consistent protein interconnection organized by N-terminal acetylation involves assembling an E2–E3 ubiquitin-like protein–ligation complex, nucleosome binding by an epigenetic regulator, cytoskeletal organization, and integrity of the anaphase-promoting complex [ 18 ]. F-Box Protein 30 (FBXO30) is a Protein Coding gene, and it is paralogous of the FBXO40. It associated with FBXO30 include Nasopharyngeal Carcinoma disease. The related pathways are MHC Class I mediated antigen presentation and Immune System. FBX030 also regulates the chromosome segregation of oocyte meiosis. ( https://www.genecards.org/cgi-bin/carddisp.pl?gene=FBXO30 ). The gene family of Kelch-like protein 21 (KLHL) is an interchangeable signal transduction mechanism and protein degeneration along with ligase and cell-cycle regulation[ 19 ]. KLHLs attached and stain as a particular membrane for degradation and significance of sequence dissimilarity in the BTB (Broad Complex, Tramtrack, and BricàBrac) domain20. They transpire through the E3 ligase complex, which KLHLs connect to the Cullin-3 (CUL3)[ 20 ]. BACK domains and namesake Kelch motifs distinguish KLHLs from other BTB proteins[ 21 ]. The Kelch motif connects with actin, which recognizes organelles, plasma membrane, and cytoskeleton [ 22 ]. These genes are adapters for BCR (BTB-CUL3-RBX1). Ubiquitin-protein ligase complexes are essential for well-organized chromosome arrangement or cytokinesis. The ligase complex control and restrain the chromosomal passenger complex (CPC) from chromosomes and moderate the ubiquitinated AURKB[ 23 ]. DCTN6 Dynactin (DCTN) is a diverse subunit protein that drives retrograde transport in cells [ 24 ]. DCTN6 (Dynactin Subunit 6) is a Protein Coding gene which is RGD (Arg-Gly-Asp) motif in the N-terminal region encoded by DCTN6. Adherent effect of macromolecular proteins such as fibronectin and identical biological function is still not recognized in some pathways relevant to the cell cycle, chromosome separation and transport to the Golgi, and consecutive modification. Total six subunits of DCTN specify to as dynactin 1 to 6 (DCTN1–6), and these subunits of DCTN are severe to the structural and function of DCTN[ 25 ],[ 26 ]. RNF123, the protein encoded by the RNF gene, contains a C-terminal ring finger domain, a motif present in various functionally distinct proteins involved in protein-protein or protein-DNA interactions. This protein shows the E3 ligase activity regarding the cyclin-dependent kinase inhibitor, also known as p27 or KIP1. Conclusion The present study is a miniature attempt to understand the molecular mechanisms and complexity of the AML patient sample. In this study, we analyzed high throughput gene expression datasets. However, additional research and experimental authentication are still required to certify the results. The crucial biological processes associated with DEGs, found on GO cluster exploration, were metabolic process, signal transduction, apoptosis, and protein purifying. DEGs that extensively initiate the hub nodes are LAMTOR2, KLHL21, and UBR4. For hub genes like LAMTOR2, KLHL21, and UBR4 overall survival was low for their increased expression. The hallmark of cancer is an irregular cellular metabolism. Besides, it promotes glycolysis and lipid biosynthesis and plays a crucial role in the growth of tumors in AML. Most of the carbon sources synthesize fatty acids. It is a form of glucose in mammalian cells, and it conducts de novo lipid synthesis and building blocks for a tumor cell. Thirteen pathways were enriched, and genes related to oxidative phosphorylation, regulation of actin cytoskeleton, endocytosis, phagocytosis, shigellosis, epithelial cell signaling, adherent junction, pertussis, bile secretion, malaria, African trypanosomiasis were found significantly affected by AML. Declarations Conflict of interest- Nil Acknowledgements- The author are grateful to IoE grant BHU and ISLS Institute of science, Banaras Hindu University. Ethical Approval - Ethical approval was obtained from the institutional ethical committee (Ref. No: I.Sc./ECM-XII/2021-22) Institute of Science, Banaras Hindu University. Competing interests - can be non-financial in nature. Authors' contributions Ajeet Kumar, M.Sc.: Ph.D. Scholar, Centre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India Contributions in the manuscript: Data analysis, manuscript writing. Ravi Bhushan, M.Sc.: Ph.D. Scholar, Centre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India Contributions in the manuscript: Data analysis, manuscript writing. Pawan K. Dubey, Ph.D.: Professor and head, Department of pathology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, U.P., India. Contributions in the manuscript: Data analysis, reviewing the literatures, manuscript writing. Vijai Tilak, M.D.: Professor and head, Department of pathology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, U.P., India. Contributions in the manuscript: data analysis, manuscript writing. Vineeta Gupta, Professor, Department of Pediatrics, Institute of Medical Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Contributions in the manuscript: manuscript writing. Nilesh Kumar, Assistant Professor Department of Pediatrics, Institute of Medical Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Contributions in the manuscript: manuscript writing. S.V.S Raju Professor, Department of Entomology and Agricultural Zoology, Institute of Agriculture Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Contributions in the manuscript: data analysis, manuscript writing. Akhtar Ali, Ph.D.: Assistant Professor, Centre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India Contributions in the manuscript: Supervised the study, conceived and designed the experiments; data analysis, manuscript writing. Funding - not applicable. Availability of data and materials Data derived from public domain resources. The data that support the findings of this study are available in NCBI GEO dataset at SE20482. These data were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20482 References Tamamyan G, Kadia T, Ravandi F, Borthakur G, Cortes J, Jabbour E, Daver N, Ohanian M, Kantarjian H, Konopleva M. Frontline treatment of acute myeloid leukemia in adults. Critical Reviews in Oncology/Hematology, 2017 110: 20–34. Pescia G, Jotterand M. POSSIBLE EVIDENCE OF X-Y INTERCHANGE IN AN XX MALE. The Lancet, 1977 309: 550. Grimwade D, Mrózek K. Diagnostic and Prognostic Value of Cytogenetics in Acute Myeloid Leukemia. Hematology/Oncology Clinics of North America, 2011 25: 1135–1161. 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Association of the human papillomavirus type 16 E7 oncoprotein with the 600-kDa retinoblastoma protein-associated factor, p600. Proceedings of the National Academy of Sciences, 2005 102: 11492–11497. Scott DC, Hammill JT, Min J, Rhee DY, Connelly M, Sviderskiy VO, Bhasin D, Chen Y, Ong S-S, Chai SC, Goktug AN, Huang G, Monda JK, Low J, Kim HS, Paulo JA, Cannon JR, Shelat AA, Chen T, Kelsall IR, Alpi AF, Pagala V, Wang X, Peng J, Singh B, Harper JW, Schulman BA, Guy RK. Blocking an N-terminal acetylation–dependent protein interaction inhibits an E3 ligase. Nat Chem Biol, 2017 13: 850–857. Stogios P, Prive G. The BACK domain in BTB-kelch proteins. Trends in Biochemical Sciences, 2004 29: 634–637. Dhanoa BS, Cogliati T, Satish AG, Bruford EA, Friedman JS. Update on the Kelch-like (KLHL) gene family. Hum Genomics, 2013 7: 13. Schleifer RJ, Li S, Nechtman W, Miller E, Bai S, Sharma A, She J-X. KLHL5 knockdown increases cellular sensitivity to anticancer drugs. Oncotarget, 2018 9: 37429–37438. Schroer TA. DYNACTIN. Annu Rev Cell Dev Biol, 2004 20: 759–779. Eckley DM, Gill SR, Melkonian KA, Bingham JB, Goodson HV, Heuser JE, Schroer TA. Analysis of Dynactin Subcomplexes Reveals a Novel Actin-Related Protein Associated with the Arp1 Minifilament Pointed End. Journal of Cell Biology, 1999 147: 307–320. Karki S, Tokito MK, Holzbaur ELF. A Dynactin Subunit with a Highly Conserved Cysteine-rich Motif Interacts Directly with Arp1. Journal of Biological Chemistry, 2000 275: 4834–4839. Garces JA, Clark IB, Meyer DI, Vallee RB. Interaction of the p62 subunit of dynactin with Arp1 and the cortical actin cytoskeleton. Current Biology, 1999 9: 1497–1502. Additional Declarations No competing interests reported. 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18:16:12","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":106422,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementryfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1019863/v2/32aa5331b3f0c4336bdaf70f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico analysis of genes and pathways related to acute myeloid leukemia presenting leukopenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a heterogeneous malignant disorder of the hematopoietic stem cells these are characterized by abnormal proliferation of the immature cell, blast cells, and disabling of the production of normal blood cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. AML is a genetic disease first established by Janet Rowley, who discovered the somatic chromosomal abnormalities in the leukemic cell of the patient sample, and they show the balanced translocations of chromosome (i.e., t[8;21] and t[15;17]) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AML affects people worldwide, such as leukemia, lymphoma, and multiple myelomas. It is the most common type of leukemia in adults and has the lowest survival rate of all different kinds of leukemia. AML is a rare disease. They are highly malignant neoplasms and supervise a large number of cancer-related deaths. Approximately 25% of all leukemia in adults in the West constitutes the most frequent form of leukemia. In AML the chromosomal abnormalities are found in t(8;21), t(15;17), inv (16), and 11q23. These aberrations produce PML-RARA, RUNX1-RUNX1T1, CBF-MYH11, and MLL-fusion genes. Mutations generally occur in transcription factors, signaling of molecules, tumor suppressor genes, epigenetic regulators, RNA splicing factors, and cohesion complexes, with FLT3, NPM1, and DNMT3A the most frequently mutated genes in AML.\u003c/p\u003e \u003cp\u003eCytogenetics provides robust diagnostic information and provides the framework for risk stratification in AML patients, and it has several limitations[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some technical failures, like cytogenetics, cannot relate the gene fusions, for example, NUP98-NSD1, CBFA2T3-GLIS2, and MNX1-ETV6, which forecast the poor outcome in pediatric patients of AML[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].AML was initially subdivided based on morphology (French-American-British system). Those are helpful for the categorization of pathology. Later, some genomics and clinical factors are initiated to coordinate with chemotherapy's reaction and overall survival. In 2017 the European Leukemia Net (ELN) separated into three prognostic groups based on genetic mutation and abnormalities in acute myeloid leukemia. These are categorized as favorable, intermediate, and adverse[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e"},{"header":"Material And Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Differentially expressed genes and Microarray data Screening in AML\u003c/h2\u003e \u003cp\u003eThe raw data procured from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) datasets (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and gene expression profile datasets obtained from (GENE ID: GSE20482). Hematological stem cells, bone marrow, and peripheral blood from AML patients with leukopenia were used in this study. The datasets derived from a study utilize the GPL6848 Agilent-012391 Whole Human Genome Oligo Microarray G4112A platform. These studies are only on genes of high and low blood count B-cell samples analyzed through bioinformatics techniques.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Preprocessing of data and screening of DEGs in AML\u003c/h2\u003e \u003cp\u003eGene expression datasets were obtained from NCBI and further these data were used for pre-processed. The expression values of probes communicate to a particular gene have to mean calculated to find the advantage of gene expression. Additionally, up and down-regulated genes were distinguished by using BiGGEsTs software analysis. The probe-level ideogram is transformed into a gene-level ideogram by using GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These DEGs pick out, calibrate p values\u0026thinsp;\u0026lt;\u0026thinsp;0.5 and threshold logFC values are \u0026gt;\u0026thinsp;0.1 for up and \u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.1 for down-regulated genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Principal component analysis and heat map generation in AML\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was executed, and constructed the heat map using the ClustVis software available online and plotted the PCA graph on DEGs data. ClustVis support maximum file size is larger, so it is not feasible to assemble the PCA plot for all gene expression studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 PPI network creation and generation of sub-network in AML\u003c/h2\u003e \u003cp\u003eDEGs data were uploaded to the STRING v 10.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www\u003c/span\u003e\u003cspan address=\"http://www\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. string-db.org/). It is an online software dataset for anticipating the functional exchange between the proteins and predicting a collaborated outcome for protein-protein interaction among gene positions, and the combined outcome N 0.4 was set as the standard. Prepare for modeling networking and sub-network; we used Cytoscape v 3.2.1. Cytoscape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a bioinformatics software for the investigation and visualization of biological networks with high throughput data. The assemble and edge coefficient were taken as standard for network construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of differentially functional expressed genes in AML\u003c/h2\u003e \u003cp\u003eThe database for annotation, visualization, and integrated discovery (DAVID, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.abcc.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"http://david.abcc.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) draft of action is a database combined with a comprising set of functional interpretations and a huge genes list. Gene ontology (GO) amplification performed as well as biological, molecular, and cellular modules were implemented by using DAVID v 6.8 and STRING v 10.5. Based on hypergeometric distribution, DAVID clutches the genes with exactly similar functions or related as a whole set. DEGs related pathways analysis and completed with the Panther Classification System (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pantherdb.org/\u003c/span\u003e\u003cspan address=\"http://pantherdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of Interaction network\u003c/h2\u003e \u003cp\u003eBiological General Repository for Interaction Datasets (BioGRID) tools are used to identify (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wiki.thebiogrid.org/doku.php/ORCS:tools\u003c/span\u003e\u003cspan address=\"https://wiki.thebiogrid.org/doku.php/ORCS:tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) the possible interconnection for acute myeloid leukemia. BioGRID is curated for biological databases like protein-protein interactions and acute myeloid leukemia's genetic or chemical interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Survival analysis\u003c/h2\u003e \u003cp\u003eOverall survival analysis for selected hub genes was performed using GEPIA V2.0. The association between mRNA prognosis and expression of acute myeloid leukemia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Differentially expressed genes (DEGs) in AML\u003c/h2\u003e \u003cp\u003e846 DEGs were allowed with their formal gene ideogram and up and down-regulated DEGs 406 \u0026amp; 440. Their p-value is \u0026lt;\u0026thinsp;.05. \u003cb\u003e(Supplementary file 1).\u003c/b\u003e Fourteen genes only were recognized to remarkably interconnect with each other. Mean of AML B cell high and low B cell shown in \u003cb\u003eFig.\u0026nbsp;1.\u003c/b\u003e DEGs were affected based on their common gene expression value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Principal component and hierarchical clustering analysis of DEGs in AML\u003c/h2\u003e \u003cp\u003ePCA plot shows a distributed plot with axes communicated to two independent principal components, 1 and 2, shown in \u003cb\u003eFig.\u0026nbsp;2A.\u003c/b\u003e Another shows a Heat-map data matrix in which shade summarizes the arithmetic divergence. A heat map was fabricated for differentially expressed genes, shown in \u003cb\u003eFig.\u0026nbsp;2B\u003c/b\u003e. Conceive of screened DEGs along with assembling of Volcano plot shows in \u003cb\u003eFig.\u0026nbsp;3A.\u003c/b\u003e MD plot releases the up and down-regulated genes shown in \u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e. Venn diagram showing the overhang between GO terms down-regulated and up-regulated in the AML shows in \u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e. Total 198 genes were set up, the novel up-regulated and 235 genes were set up the novel down regulated. DEGs were determined based upon standard gene expression value shows in \u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Protein-protein interaction meshwork in AML\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) of DEGs shown in \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e. Pink and blue circle represents up and down-regulated genes. Based on the combined score estimated through STRING. Seventy-six gene pairs (integrated score N 0.4) were elevated to link together and constitute one prime network with 69 nodes and 381 edges. Total five sub-networks will eliminate independently. The up-regulated DEGs and down-regulated DEGs forming hub nodes were GNB4, LAMTOR2, ACTN4, HGSNAT, TMED1, and UBR4 FBXO30, KLHL21, DCTN6, RNF123, RNF114. Clustering coefficient and edge were appropriated as the basis of the selection criteria of hub nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction of Sub-network in AML\u003c/h2\u003e \u003cp\u003eFive sub-networks (3 nodes and three edges in network1; 2 nodes and one boundary in networks 2 and 3 both) were pulled out from the primary network beside using Cytoscape shown in \u003cb\u003eFig.\u0026nbsp;5.\u003c/b\u003e Each gene in network \u003cb\u003eA\u003c/b\u003e was up-regulated, and network \u003cb\u003eB\u003c/b\u003e was down-regulated. Another network, \u003cb\u003eC, D\u003c/b\u003e, and \u003cb\u003eE\u003c/b\u003e lines, shows the correlation between up and downregulated genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of functional enrichment in AML\u003c/h2\u003e \u003cp\u003eAnalysis of functional enrichment was carried out and remarkably improved their processes, functions, and components of DEGs (FDR b 0.05) were recorded in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e results of GO shown in \u003cb\u003eFig.\u0026nbsp;6.\u003c/b\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\u003eGene Ontology (GO) analysis of up regulated DEGs.\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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006796\u0026thinsp;~\u0026thinsp;phosphate-containing compound metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006793\u0026thinsp;~\u0026thinsp;phosphorus metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0032268\u0026thinsp;~\u0026thinsp;regulation of cellular protein metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051246\u0026thinsp;~\u0026thinsp;regulation of protein metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.62E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030029\u0026thinsp;~\u0026thinsp;actin filament-based process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044403\u0026thinsp;~\u0026thinsp;symbiosis, encompassing mutualism through parasitism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044419\u0026thinsp;~\u0026thinsp;interspecies interaction between organisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030198\u0026thinsp;~\u0026thinsp;extracellular matrix organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016032\u0026thinsp;~\u0026thinsp;viral process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043062\u0026thinsp;~\u0026thinsp;extracellular structure organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044764\u0026thinsp;~\u0026thinsp;multi-organism cellular process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0080134\u0026thinsp;~\u0026thinsp;regulation of response to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035336\u0026thinsp;~\u0026thinsp;long-chain fatty-acyl-CoA metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.42E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0007010\u0026thinsp;~\u0026thinsp;cytoskeleton organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035337\u0026thinsp;~\u0026thinsp;fatty-acyl-CoA metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030036\u0026thinsp;~\u0026thinsp;actin cytoskeleton organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.06E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0007015\u0026thinsp;~\u0026thinsp;actin filament organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.11E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:1901135\u0026thinsp;~\u0026thinsp;carbohydrate derivative metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0032956\u0026thinsp;~\u0026thinsp;regulation of actin cytoskeleton organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0008610\u0026thinsp;~\u0026thinsp;lipid biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0033036\u0026thinsp;~\u0026thinsp;macromolecule localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.281035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0070062\u0026thinsp;~\u0026thinsp;extracellular exosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:1903561\u0026thinsp;~\u0026thinsp;extracellular vesicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.82E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043230\u0026thinsp;~\u0026thinsp;extracellular organelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.98E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031988\u0026thinsp;~\u0026thinsp;membrane-bounded vesicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044421\u0026thinsp;~\u0026thinsp;extracellular region part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005925\u0026thinsp;~\u0026thinsp;focal adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.53E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.63E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005924\u0026thinsp;~\u0026thinsp;cell-substrate adherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.45E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.63E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030055\u0026thinsp;~\u0026thinsp;cell-substrate junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.21E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.09E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005912\u0026thinsp;~\u0026thinsp;adherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0000323\u0026thinsp;~\u0026thinsp;lytic vacuole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005764\u0026thinsp;~\u0026thinsp;lysosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0070161\u0026thinsp;~\u0026thinsp;anchoring junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.61E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005576\u0026thinsp;~\u0026thinsp;extracellular region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005773\u0026thinsp;~\u0026thinsp;vacuole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0008092\u0026thinsp;~\u0026thinsp;cytoskeletal protein binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.72E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019899\u0026thinsp;~\u0026thinsp;enzyme binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.15E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016818\u0026thinsp;~\u0026thinsp;hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016817\u0026thinsp;~\u0026thinsp;hydrolase activity, acting on acid anhydrides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003924\u0026thinsp;~\u0026thinsp;GTPase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019901\u0026thinsp;~\u0026thinsp;protein kinase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0017111\u0026thinsp;~\u0026thinsp;nucleoside-triphosphatase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016779\u0026thinsp;~\u0026thinsp;nucleotidyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016462\u0026thinsp;~\u0026thinsp;pyrophosphatase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019001\u0026thinsp;~\u0026thinsp;guanyl nucleotide binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044325\u0026thinsp;~\u0026thinsp;ion channel binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005525\u0026thinsp;~\u0026thinsp;GTP binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.216623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003779\u0026thinsp;~\u0026thinsp;actin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0032561\u0026thinsp;~\u0026thinsp;guanyl ribonucleotide binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044769\u0026thinsp;~\u0026thinsp;ATPase activity, coupled to transmembrane movement of ions, rotational mechanism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0032403\u0026thinsp;~\u0026thinsp;protein complex binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051015\u0026thinsp;~\u0026thinsp;actin filament binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019900\u0026thinsp;~\u0026thinsp;kinase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0045182\u0026thinsp;~\u0026thinsp;translation regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005200\u0026thinsp;~\u0026thinsp;structural constituent of cytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.359914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030371\u0026thinsp;~\u0026thinsp;translation repressor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0015078\u0026thinsp;~\u0026thinsp;hydrogen ion transmembrane transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003684\u0026thinsp;~\u0026thinsp;damaged DNA binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374378\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene Ontology (GO) analysis of down regulated DEGs.\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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0046501\u0026thinsp;~\u0026thinsp;protoporphyrinogen IX metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006779\u0026thinsp;~\u0026thinsp;porphyrin-containing compound biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.87E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006778\u0026thinsp;~\u0026thinsp;porphyrin-containing compound metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.72E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0015669\u0026thinsp;~\u0026thinsp;gas transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.79E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0033014\u0026thinsp;~\u0026thinsp;tetrapyrrole biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.12E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0033013\u0026thinsp;~\u0026thinsp;tetrapyrrole metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006783\u0026thinsp;~\u0026thinsp;heme biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0015671\u0026thinsp;~\u0026thinsp;oxygen transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.93E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006782\u0026thinsp;~\u0026thinsp;protoporphyrinogen IX biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0042168\u0026thinsp;~\u0026thinsp;heme metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051188\u0026thinsp;~\u0026thinsp;cofactor biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030218\u0026thinsp;~\u0026thinsp;erythrocyte differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0048534\u0026thinsp;~\u0026thinsp;hematopoietic or lymphoid organ development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0021953\u0026thinsp;~\u0026thinsp;central nervous system neuron differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0034101\u0026thinsp;~\u0026thinsp;erythrocyte homeostasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030097\u0026thinsp;~\u0026thinsp;hemopoiesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002520\u0026thinsp;~\u0026thinsp;immune system development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.65E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0055072\u0026thinsp;~\u0026thinsp;iron ion homeostasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.171931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0021515\u0026thinsp;~\u0026thinsp;cell differentiation in spinal cord\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.13E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.172802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0046148\u0026thinsp;~\u0026thinsp;pigment biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.99E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002262\u0026thinsp;~\u0026thinsp;myeloid cell homeostasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0018130\u0026thinsp;~\u0026thinsp;heterocycle biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051186\u0026thinsp;~\u0026thinsp;cofactor metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005833\u0026thinsp;~\u0026thinsp;hemoglobin complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0014731\u0026thinsp;~\u0026thinsp;spectrin-associated cytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.88E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030863\u0026thinsp;~\u0026thinsp;cortical cytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.42E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.126063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044448\u0026thinsp;~\u0026thinsp;cell cortex part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044445\u0026thinsp;~\u0026thinsp;cytosolic part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.517882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0008091\u0026thinsp;~\u0026thinsp;spectrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005829\u0026thinsp;~\u0026thinsp;cytosol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005938\u0026thinsp;~\u0026thinsp;cell cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0072562\u0026thinsp;~\u0026thinsp;blood microparticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031672\u0026thinsp;~\u0026thinsp;A band\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030864\u0026thinsp;~\u0026thinsp;cortical actin cytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0099568\u0026thinsp;~\u0026thinsp;cytoplasmic region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005344\u0026thinsp;~\u0026thinsp;oxygen transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.41E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019825\u0026thinsp;~\u0026thinsp;oxygen binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.57E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003700\u0026thinsp;~\u0026thinsp;transcription factor activity, sequence-specific DNA binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.366809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0001071\u0026thinsp;~\u0026thinsp;nucleic acid binding transcription factor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.366809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0008047\u0026thinsp;~\u0026thinsp;enzyme activator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0020037\u0026thinsp;~\u0026thinsp;heme binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.381242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0046906\u0026thinsp;~\u0026thinsp;tetrapyrrole binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0015399\u0026thinsp;~\u0026thinsp;primary active transmembrane transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0015405\u0026thinsp;~\u0026thinsp;P-P-bond-hydrolysis-driven transmembrane transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0042910\u0026thinsp;~\u0026thinsp;xenobiotic transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.538849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030506\u0026thinsp;~\u0026thinsp;ankyrin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.639508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0042626\u0026thinsp;~\u0026thinsp;ATPase activity, coupled to transmembrane movement of substances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016820\u0026thinsp;~\u0026thinsp;hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0022804\u0026thinsp;~\u0026thinsp;active transmembrane transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003810\u0026thinsp;~\u0026thinsp;protein-glutamine gamma-glutamyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0097159\u0026thinsp;~\u0026thinsp;organic cyclic compound binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051537\u0026thinsp;~\u0026thinsp;2 iron, 2 sulfur cluster binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044212\u0026thinsp;~\u0026thinsp;transcription regulatory region DNA binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0000975\u0026thinsp;~\u0026thinsp;regulatory region DNA binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019209\u0026thinsp;~\u0026thinsp;kinase activator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0001067\u0026thinsp;~\u0026thinsp;regulatory region nucleic acid binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663746\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Enrichment of KEGG pathway for DEGs in AML\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment examination was carried out, and remarkably up-regulated KEGG pathway enrichment for DEGs are African trypanosomiasis, malaria, bile secretion, porphyrin, and chlorophyll. Down regulated KEGG pathway enrichment for DEGs is Pertussis, adherent junction, epithelial cell signaling, shigellosis, γ R mediated phagocytosis, endocytosis, regulation of actin cytoskeleton, oxidative phosphorylation, infection of Escherichia coli shown in \u003cb\u003eFig.\u0026nbsp;7.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Survival analysis in AML\u003c/h2\u003e \u003cp\u003eFor hub genes like LAMTOR2, KLHL21 and UBR4 overall survival was found to be low for their increased expression shown in \u003cb\u003eFig.\u0026nbsp;8.\u003c/b\u003e and \u003cb\u003eFig.\u0026nbsp;9.\u003c/b\u003e shown the flowchart illustrating the process of our research.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eThe present study is a little effort to understand the molecular mechanism and its complexity involved in the AML patient. In this study, we used different bioinformatic tools to analyze high throughput gene expression datasets exposed to 846 differentially expressed genes (DEGs). The total number of the up-regulated gene was 406, while 440 genes were down-regulated. Based on GO cluster analysis, major biological processes related to DEGs were the homeostasis process, apoptosis, and protein modification. LAMTOR2, ACTN4, HGSNAT, TMED10 are up-regulated, and UBR4, FBXO30, KLHL21, DCTN6, RNF123, RNF114 were down-regulated respectively. GNB4gene is located on chromosome 3q26.33. Guanine nucleotide-binding proteins β-4, which produce signals to communicate receptors and effector molecules. Which is made up three subunits α, β and γ. Subunits of G protein are encoded by mammalian cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGNB4 gene mainly encodes through beta subunit. The β subunits are dominant regulators and as well as it also regulates signal transduction in various signaling systems. The LAMTOR2 was normally acknowledged in a yeast two-hybrid on a specific binding partner of MEK1 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] which assemble in late endosomes beside the adaptor protein LAMTOR2 (p14)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. MP1 and p14 are nearly identical on structurally and very stable heterodimeric complex. Which is essential for ERK stimulation on endosomes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Which depends on gene disruption of p14 and p14/MP1-MEK1 signaling complex modulates the endosomal traffic, EGFR degradation and cellular proliferation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These action are determining for early embryogenesis and throughout tissue homeostasis as released by specific deletion of p14 gene in epidermis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ACTN4 gene is actin cross-linking protein which is encoded by human alpha-actin-4 protein. These are correlated with cell motility, invasion and metastasis in cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Excessive expression and massive copy number extension of ACTN4 in different cancer tissue has been also reported and they are associated with the imperfect prognosis in diverse type of cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The spectrin genes are superfamily which belongs to the alpha actin and it represents a various group of cytoskeletal proteins. Diverse roles of alpha actin are an actin-binding protein in various types of cell. Gene scramble an isoform of actinin non-muscle, which is condensed in the cytoplasm, and elaborate in metastatic processes. HGSNAT gene encodes acetyl-CoA:alpha-glucosaminide N-acetyltransferase is an enzyme that catalyzes acetylation of the terminal glucosamine residues of sulfate before its hydrolysis by alpha-N-acetyl glucosaminidase [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. HGSNAT is located in the lysosomal membrane, and it catalyzes transmembrane acetylation in which the terminal glucosamine residue of heparan sulfate acquires an acetyl group, so it converts N-acetylglucosamine. TMED10 genes are trans-membrane proteins that can alter distinct proteins of different segments. TMED10 can form more advance oligomeric assemble in cross linking and depend upon the assemble of mIL-1β in THP-1 cells. These cells are containing a unique transmembrane domain, a luminal signal peptide (SS), a Golgi dynamics (GOLD) domain, and a C-terminal tail (CT) facing the cytoplasm.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDownregulation gene\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe UBR4 gene encodes Ubiquitin-protein ligase UBR4 enzyme (UBR4) in humans, found in chromosome number one. It encodes the protein that appears to be a component of cytoplasm in cytoskeletal [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. UBR4 gene mark to human papillomavirus. These related pathways are the PI3K-Akt signaling pathway and the Innate Immune System[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The function of UBR4 gene calmodulin, ubiquitinase ligase activity, transferase activity, binding of zinc ion, and monitoring the integrin-mediated signaling (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/cgi-bin/carddisp.pl?gene=UBR4\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/cgi-bin/carddisp.pl?gene=UBR4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The therapeutically consistent protein interconnection organized by N-terminal acetylation involves assembling an E2\u0026ndash;E3 ubiquitin-like protein\u0026ndash;ligation complex, nucleosome binding by an epigenetic regulator, cytoskeletal organization, and integrity of the anaphase-promoting complex [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. F-Box Protein 30 (FBXO30) is a Protein Coding gene, and it is paralogous of the FBXO40. It associated with FBXO30 include Nasopharyngeal Carcinoma disease. The related pathways are MHC Class I mediated antigen presentation and Immune System. FBX030 also regulates the chromosome segregation of oocyte meiosis. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/cgi-bin/carddisp.pl?gene=FBXO30\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/cgi-bin/carddisp.pl?gene=FBXO30\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gene family of Kelch-like protein 21 (KLHL) is an interchangeable signal transduction mechanism and protein degeneration along with ligase and cell-cycle regulation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. KLHLs attached and stain as a particular membrane for degradation and significance of sequence dissimilarity in the BTB (Broad Complex, Tramtrack, and Bric\u0026agrave;Brac) domain20. They transpire through the E3 ligase complex, which KLHLs connect to the Cullin-3 (CUL3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. BACK domains and namesake Kelch motifs distinguish KLHLs from other BTB proteins[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Kelch motif connects with actin, which recognizes organelles, plasma membrane, and cytoskeleton [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These genes are adapters for BCR (BTB-CUL3-RBX1). Ubiquitin-protein ligase complexes are essential for well-organized chromosome arrangement or cytokinesis. The ligase complex control and restrain the chromosomal passenger complex (CPC) from chromosomes and moderate the ubiquitinated AURKB[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. DCTN6 Dynactin (DCTN) is a diverse subunit protein that drives retrograde transport in cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. DCTN6 (Dynactin Subunit 6) is a Protein Coding gene which is RGD (Arg-Gly-Asp) motif in the N-terminal region encoded by DCTN6. Adherent effect of macromolecular proteins such as fibronectin and identical biological function is still not recognized in some pathways relevant to the cell cycle, chromosome separation and transport to the Golgi, and consecutive modification. Total six subunits of DCTN specify to as dynactin 1 to 6 (DCTN1\u0026ndash;6), and these subunits of DCTN are severe to the structural and function of DCTN[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. RNF123, the protein encoded by the RNF gene, contains a C-terminal ring finger domain, a motif present in various functionally distinct proteins involved in protein-protein or protein-DNA interactions. This protein shows the E3 ligase activity regarding the cyclin-dependent kinase inhibitor, also known as p27 or KIP1.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThe present study is a miniature attempt to understand the molecular mechanisms and complexity of the AML patient sample. In this study, we analyzed high throughput gene expression datasets. However, additional research and experimental authentication are still required to certify the results. The crucial biological processes associated with DEGs, found on GO cluster exploration, were metabolic process, signal transduction, apoptosis, and protein purifying. DEGs that extensively initiate the hub nodes are LAMTOR2, KLHL21, and UBR4. For hub genes like LAMTOR2, KLHL21, and UBR4 overall survival was low for their increased expression. The hallmark of cancer is an irregular cellular metabolism. Besides, it promotes glycolysis and lipid biosynthesis and plays a crucial role in the growth of tumors in AML. Most of the carbon sources synthesize fatty acids. It is a form of glucose in mammalian cells, and it conducts de novo lipid synthesis and building blocks for a tumor cell. Thirteen pathways were enriched, and genes related to oxidative phosphorylation, regulation of actin cytoskeleton, endocytosis, phagocytosis, shigellosis, epithelial cell signaling, adherent junction, pertussis, bile secretion, malaria, African trypanosomiasis were found significantly affected by AML.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest-\u0026nbsp;\u003c/strong\u003eNil\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements-\u0026nbsp;\u003c/strong\u003eThe author are grateful to IoE grant BHU and ISLS Institute of science, Banaras Hindu University.\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e-\u0026nbsp;Ethical approval was obtained from the institutional ethical committee (Ref. No: I.Sc./ECM-XII/2021-22) Institute of Science, Banaras Hindu University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e - can be non-financial in nature.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAjeet Kumar, M.Sc.:\u0026nbsp;\u003c/strong\u003ePh.D. Scholar,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCentre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData analysis, manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRavi Bhushan, M.Sc.:\u0026nbsp;\u003c/strong\u003ePh.D. Scholar,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCentre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData analysis, manuscript writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePawan K. Dubey, Ph.D.:\u003c/strong\u003e\u0026nbsp; Professor and head, Department of pathology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, U.P., India.\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData analysis, reviewing the literatures, manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVijai Tilak, M.D.:\u003c/strong\u003e Professor and head, Department of pathology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, U.P., India.\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edata analysis, manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVineeta Gupta,\u0026nbsp;\u003c/strong\u003eProfessor,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eDepartment of Pediatrics, Institute of Medical Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emanuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNilesh Kumar,\u0026nbsp;\u003c/strong\u003eAssistant\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eProfessor Department of Pediatrics, Institute of Medical Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emanuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.V.S Raju\u003c/strong\u003e Professor, Department of Entomology and Agricultural Zoology, Institute of Agriculture Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edata analysis, manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkhtar Ali, Ph.D.:\u003c/strong\u003e\u0026nbsp; Assistant Professor, Centre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, U.P., India\u003c/p\u003e\n\u003cp\u003eContributions in the manuscript:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupervised the study, conceived and designed the experiments; data analysis, manuscript writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e-\u0026nbsp;not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData derived from public domain resources. The data that support the findings of this study are available in NCBI GEO dataset at SE20482. These data were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20482 \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTamamyan G, Kadia T, Ravandi F, Borthakur G, Cortes J, Jabbour E, Daver N, Ohanian M, Kantarjian H, Konopleva M. Frontline treatment of acute myeloid leukemia in adults. Critical Reviews in Oncology/Hematology, 2017 110: 20\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePescia G, Jotterand M. POSSIBLE EVIDENCE OF X-Y INTERCHANGE IN AN XX MALE. The Lancet, 1977 309: 550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimwade D, Mr\u0026oacute;zek K. Diagnostic and Prognostic Value of Cytogenetics in Acute Myeloid Leukemia. 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Journal of Cell Biology, 1999 147: 307\u0026ndash;320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarki S, Tokito MK, Holzbaur ELF. A Dynactin Subunit with a Highly Conserved Cysteine-rich Motif Interacts Directly with Arp1. Journal of Biological Chemistry, 2000 275: 4834\u0026ndash;4839.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarces JA, Clark IB, Meyer DI, Vallee RB. Interaction of the p62 subunit of dynactin with Arp1 and the cortical actin cytoskeleton. Current Biology, 1999 9: 1497\u0026ndash;1502.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Banaras Hindu University","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, Leukopenia, Biomarker, gene ontology","lastPublishedDoi":"10.21203/rs.3.rs-1019863/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1019863/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute myeloid leukemia (AML) is the progenitor and hematopoietic stem cell blood cancer. Chromosomal abnormalities include balanced translocations between two chromosomes like t[8;21] and t[15;17]) in malignant cells. The current study aimed to investigate AML with leukopenia and gene expression changes in high-white and low-white counts B-cell. The total number of samples used was ten. The raw gene expression profiles (ID: GSE20482) of bone marrow obtained from AML patients showed five high-white counts B-cell and five low-white counts B-cell expressed genes differentially expressed. Genes that corresponded to official gene symbols were selected for protein-protein interaction (PPI) and sub-network construction (score\u0026thinsp;\u0026gt;\u0026thinsp;0.4). In addition, functional annotation of gene ontology (GO) and pathway analysis were performed for the genes involved in networking.\u003c/p\u003e \u003cp\u003eResults\u003c/p\u003e \u003cp\u003eA total of 846 genes were identified as differentially expressed, and 406 genes were upregulated; and another 440 genes were downregulated. The remaining 14 genes that interacted with each other were significantly identified. The hub genes GNB4, LAMTOR2, ACTN4, HGSNAT, and TMED1 were upregulated while the downregulated DEGs forming hub nodes were UBR4, FBXO30, KLHL21, DCTN6, RNF123, RNF114. AML significantly affects the expression of genes involved in cell differentiation, apoptosis, cell signaling, and protein modification. AML cells enter the blood quickly and spread to the liver, spleen, and central nervous system. These are a total of thirteen pathways. They were enriched, and AML was found to significantly impact genes involved in oxidative phosphorylation, actin cytoskeleton regulation, endocytosis, phagocytosis, shigellosis, and epithelial cell signaling in helicobacter, adherent junction, pertussis, bile secretion, malaria, and African trypanosomiasis.\u003c/p\u003e \u003cp\u003eConclusions\u003c/p\u003e \u003cp\u003eHub genes like GNB4 and UBR4 provide a novel biomarker in AML.\u003c/p\u003e","manuscriptTitle":"In silico analysis of genes and pathways related to acute myeloid leukemia presenting leukopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-07-25 18:16:10","doi":"10.21203/rs.3.rs-1019863/v2","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}},{"code":1,"date":"2021-11-17 18:59:56","doi":"10.21203/rs.3.rs-1019863/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":"74fbf1d9-3ff5-461c-b3cd-c9dad2457ce0","owner":[],"postedDate":"July 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-25T18:16:11+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-25 18:16:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1019863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1019863","identity":"rs-1019863","version":["v2"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}