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

Acute myeloid leukemia (AML) is a type of blood cancers that begins from progenitor and hematopoietic stem cell. Chromosomal abnormalities include balanced translocations between two chromosome like t[8;21] and t[15;17]) in malignancies cells. The present study aimed to explore the AML presenting with leukopenia and gene expression changes induced in High-white count B-cell and Low-white count B-cell the total number of samples is ten. The raw gene expression proles (ID: GSE20482) of bone marrow achieve from AML patient ve High-white count B-cell and ve Low-white count B-cell were expressed genes used to recognize differentially. These genes that correspond to ocial gene symbols were select for protein-protein interaction (PPI) and sub-network construction (score > 0.4). The functional annotation of Gene Ontology (GO) and pathways analysis were performed for those genes involve in networking.


Introduction
Acute myeloid leukemia (AML) is a heterogeneous malignant disorder of the hematopoietic stem cells these are characterized by abnormal proliferation of immature cell, blast cells and disable the production of normal blood cells 1  Cytogenetics provide a powerful diagnostic information and also provide the framework for risk strati cation in AML patient and it has a numbers of limitations 3 . Some from of technical failures, like cytogenetics cannot relate the gene fusions, for some example NUP98-NSD1, CBFA2T3-GLIS2, and MNX1-ETV6, which forecast the poor outcome in pediatric patient of AML 4,5 .AML was initially subdivided on the basis of morphology (French-American-British system), those are helpful for the categorization of The raw data procure from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) datasets (http://www. ncbi.nlm.nih.gov/geo/) and gene expression pro le datasets obtained from (GENE ID: GSE20482). These samples for this data were hematological stem cells bone marrow and peripheral blood from AML obtained presenting with leukopenia. The datasets derived from a study utilize the GPL6848 Agilent-012391 Whole Human Genome Oligo Microarray G4112A platform.
These studies are only genes of high and low blood count B-cell samples analyzed through bioinformatic techniques.

Preprocessing of data and screening of DEGs in AML
Gene expression datasets obtained from NCBI and further these data were used for pre-processed. The expression values of probes communicate to a particular gene has mean calculated to nd the advantage of gene expression and additional including up and down-regulated genes were distinguishing by using BiGGEsTs software analysis. The probe-level ideogram is transformed into gene-level ideogram by using GEO2R (https://www. ncbi.nlm.nih.gov/geo/geo2r/). These DEGs pick out, calibrate p values < 0.5 and threshold logFC values are > 0.1 for up and < −0.1 for down regulated genes.

Principal component analysis and heat map generation in AML
Principal component analysis (PCA) was execute and construct the heat map by using the ClustVis software which is available in online and plotted the PCA graph on DEGs data. ClustVis support maximum le size is larger so it is not feasible to assemble the PCA plot for all gene expression study.

PPI network creation and generation of sub-network in AML
DEGs data were upload to the STRING v 10.5 (http://www. string-db.org/). It is an online software dataset for anticipate the functional exchange between the proteins and predict a collaborated outcome for protein-protein interaction among gene position and combined outcome N 0.4 was set as the standard.
Prepare for modelling networking and sub-network we used cytoscape v 3.2.1. Cytoscape (http://www.cytoscape.org/) is a bioinformatics software for investigation and visualization of biological networks with high throughput data. The assemble and edge coe cient were taken as standard for network construction.

Analysis of differentially functional expressed genes in AML
The database for annotation, visualization, and integrated discovery (DAVID, http://david.abcc.ncifcrf.gov/) draft of an action is a database combine a comprising set of functional interpretation and huge genes list. Gene ontology (GO) ampli cation performed as well as biological, molecular and cellular module were implemented by using DAVID v 6.8 and STRING v 10.5. The basis of hyper-geometric distribution, DAVID clutch the genes with exactly similar function or related as a whole set. DEGs related pathways analysis and completed with the Panther Classi cation System (http://pantherdb.org/)

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 data base as like as protein-protein interactions, genetical or chemical interactions of acute myeloid leukemia.

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.  Tables 1 and 2

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 g.8.

Discussion
Present study is a little bit effort to understand the molecular mechanism and its complexity involved in the AML patient. In this study we used different bioinformatic tools to analyzed high throughput gene expression datasets which expose to 846 differentially expressed genes (DEGs) were identi ed. The total number of up-regulated gene were 406 while 440 genes were down-regulated. On the basis of GO cluster analysis major biological processes related to DEGs were homeostasis process, apoptosis and modi cation of protein. 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 speci c 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 tra c, EGFR degradation and cellular proliferation 12 . These action are determining for early embryogenesis and throughout tissue homeostasis as released by speci c deletion of p14 gene in epidermis 13 . ACTN4 gene is actin cross-linking protein which is encoded by human alphaactin-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 prior to its hydrolysis by alpha-N-acetyl glucosaminidase 16 . HGSNAT is located in the lysosomal membrane and they catalyses a trans membrane acetylation in which the terminal glucosamine residue of heparan sulphate acquires an acetyl group so it converts N-acetylglucosamine. TMED10 genes are trans-membrane protein, that is able to alteration of distinct proteins of different in segment. These protein shows the E3 ligase activity regarding the cyclin-dependent kinase inhibitor which is also familiar as p27 or KIP1.

Conclusion
Present study it is a miniature attempt to understand the molecular mechanisms and its complexity involved in the AML patient sample. In this study we analyzed high throughput gene expression datasets.
Although additional study and experimental authentication are still required to certify the results. The crucial biological processes associated to DEGs, found on GO cluster exploration, were metabolic process, signal transduction, apoptosis and protein purifying. DEGs which are extensively initiate the hub nodes are LAMTOR2, KLHL21 and UBR4 respectively. For hub genes like LAMTOR2, KLHL21 and UBR4 overall survival was found to be low for their increased expression. The hallmark of cancer is an irregular cellular metabolism. Besides it promotes the glycolysis, lipid biosynthesis and besides play a crucial role in growth of tumor in AML. Most of the carbon sources synthesize fatty acid it is a form of glucose in mammalian cells and it conduct de novo lipid synthesis and building blocks for 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 signi cantly affected by AML.

Declarations
Competing interests: The authors declare no competing interests.     These ve (A, B, C, D, E) sub-networks of differentially expressed genes (DEGs). Pink circle represents the up-regulated genes and blue circle represent the down-regulated genes and lines shows the correlation between these genes.

Figure 6
A. Analysis of gene ontology for differentially expressed up regulated genes in AML. B. Analysis of gene ontology for differentially expressed down regulated genes in AML.