Identification of Candidate Biomarkers for Idiopathic Thrombocytopenic Purpura by Bioinformatics Analysis of Microarray Data


avatar Samira Gilanchi a , avatar Hakimeh Zali b , c , * , avatar Mohammad Faranoush d , * , avatar Mostafa Rezaei Tavirani b , avatar Keyvan Shahriary e , avatar Mahyar Daskareh f

Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Science, Tehran, Iran.
School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Pediatric Growth and Development Research Center, Institute of Endocrinology, Iran University of Medical Sciences, Tehran, Iran.
West Coast University PharmD campus, Los Angeles, Ca, USA.
Department of Radiology, Ziyaian Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Corresponding Authors:

how to cite: Gilanchi S, Zali H, Faranoush M, Rezaei Tavirani M, Shahriary K, et al. Identification of Candidate Biomarkers for Idiopathic Thrombocytopenic Purpura by Bioinformatics Analysis of Microarray Data. Iran J Pharm Res. 2020;19(4):e124369.


Idiopathic Thrombocytopenic Purpura (ITP) is a multifactorial disease with decreased count of platelet that can lead to bruising and bleeding manifestations. This study was intended to identify critical genes associated with chronic ITP. The gene expression profile GSE46922 was downloaded from the Gene Expression Omnibus database to recognize Differentially Expressed Genes (DEGs) by R software. Gene ontology and pathway analyses were performed by DAVID. The biological network was constructed using the Cytoscape. Molecular Complex Detection (MCODE) was applied for detecting module analysis. Transcription factors were identified by the PANTHER classification system database and the gene regulatory network was constructed by Cytoscape. One hundred thirty-two DEGs were screened from comparison newly diagnosed ITP than chronic ITP. Biological process analysis revealed that the DEGs were enriched in terms of positive regulation of autophagy and prohibiting apoptosis in the chronic phase. KEGG pathway analysis showed that the DEGs were enriched in the ErbB signaling pathway, mRNA surveillance pathway, Estrogen signaling pathway, and Notch signaling pathway. Additionally, the biological network was established, and five modules were extracted from the network. ARRB1, VIM, SF1, BUB3, GRK5, and RHOG were detected as hub genes that also belonged to the modules. SF1 also was identified as a hub-TF gene. To sum up, microarray data analysis could perform a panel of genes that provides new clues for diagnosing chronic ITP.


Immune thrombocytopenic purpura (ITP) known as Idiopathic thrombocytopenic purpura is a multifactorial autoimmune bleeding disease associated with platelet destruction and discriminated by isolated thrombocytopenia (platelet count < 150,000 u/L) that was reported in almost 2 per 100,000 adults with a mean age of diagnosis of 50 years (1, 2). However, the vague pathogenesis, the abnormalities in the number and the function of different immune cells can play a crucial role in this disease. ITP phenotype, characterized by dysfunctional T-lymphocyte immunity, dysregulation in pre-B-cell, and T cell immunophenotypic markers, was recognized in bone marrow lymphocytes of pediatric ITP (3, 4). Besides, it is believed that membrane glycoproteins IIb-IIIa of platelet was targeted by immunoglobulin G autoantibody which is confirmed significantly by elevated CRP levels in ITP patients (5,6). These autoantibodies are recognized in 40–60% of patients and provide condition to Kupffer cells and splenic macrophages in the liver phagocytosis platelets (7). Other mechanisms include impaired production of platelet stimulatory hormone, thrombopoietin, reduced expression of human leukocyte antigen-G and immunoglobulin-like transcripts or secondary contributors such as childhood exposure to viruses, helicobacter pylori infection, and pregnancy (8-10). Zhang, et al., determined six marker proteins that separate primary ITP from secondary ITP, including NPS, EDN1, CORT, CLEC7A, CCL18, and NPPB. Most of the detected proteins related to the immune system act as up/down-regulator in macrophages and platelet (11). Platelets can be recognized with the expression of CD38 as a prognostic marker for ITP (2).

As mentioned before, ITP classified as acute and chronic types and sub-categorized by primary and secondary etiology (9, 10). Besides, the alternative classification by international consensus guidelines organized 3 phases as newly diagnosed (up to 3 months), persistent (3-12 months’ duration), and chronic (over 12 months’ duration) (2, 6 and 11).

ITP patients were characterized by a decrease in platelet count of peripheral blood and variable bleeding symptoms. In severe cases, it may lead to fatal intracranial hemorrhage. Thus, prompt diagnosis and early therapeutic intervention are essential (12, 13).

There are no specific criteria for diagnosing ITP, and diagnosis is based on the exclusion criteria of the other diseases, such as lupus erythematosus, Von Willebrand disease type IIb, hemolytic uremic syndrome, Evans syndrome, disseminated intravascular coagulation, Posttransfusion purpura, paroxysmal nocturnal hemoglobinuria, myelodysplastic syndrome, lymphoproliferative disorders, Infections (viral, bacterial, parasitic), and drug-induced thrombocytopenia. Furthermore, antiplatelet antibody testing is not recommended because of high inter-laboratory variability and reduced sensitivity (14-16).

Microarray technology is a prevalent technique for studying the pattern of expression of a large number of genes to analyze a genome. Microarray data are important in many aspects of disease research, including primary research, target discovery, biomarker identification, and prognostic test determination. The methods used to analyze the data can have a profound effect on the interpretation of the results (17, 18). Network analysis of high-throughput data can be useful in breaking the gap between data production and drug targeting and helps to uncover biological complexity (19, 20). Therefore, to explore the molecular mechanism and discover specific biomarkers for chronic ITP compared with newly diagnosed in pediatrics, we applied bioinformatics techniques to analyze gene expression profiles of pediatric chronic ITP versus newly diagnosed and identify DEGs. For this aim, in the beginning, pediatric chronic ITP patients’ gene expression profiles were compared with pediatric newly diagnosed downloaded from GEO dataset. DEGs were identified using limma packages of the R software. The involvement of DEGs in the biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were assessed with DAVID online tool. DEGs visualized using Cytoscape software. We applied the network analysis using Cytoscape to predict probable biomarkers. The panther database was used for transcriptional regulatory network construction. These studies could help find crucial genes that might be applied for appropriate diagnostics and treatment strategies in ITP.


Microarrays data

The Gene Expression Omnibus (GEO, is a public dataset for storage microarray, and next-generation sequencing data is freely available for users. In this study, ITP genomic data were obtained from GEO with the series accession number GSE46922 and the platform GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array). This dataset included data from thirteen blood samples: seven newly diagnosed and six chronic samples described by Margareta Jernas et al. (21).

Data processing

Data retrieved from GEO were analyzed with R software to discover significantly expressed genes by employing various statistical tests, mainly, the t-statistics and P-value. R software is a fascinating tool to discriminate two or more groups of samples to classify genes, differentially regulated following the same experimental condition. This software can estimate the P-value for significant outcomes by utilizing Limma R packages from the Bioconductor project. Benjamini false discovery rate was concerned in this outline. Here the genes were chosen for more evaluation with P-value < 0.05, and-0.5 >M > 0.5 (M is log2 fold change).

Functional and pathway enrichment analysis

Up-regulated and down-regulated genes were analyzed separately by the DAVID enrichment database (version 6.8) ( The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-accessible program that provides a comprehensive set of functional annotation tools to disclose the biological meaning behind gene sets. DAVID contains numerous public sources of protein and gene annotation from more than 65,000 species (22). Gene Ontology and KEGG pathway analysis were performed using the DAVID database for functional analysis of the gene lists. We used the functional annotation clustering; to reveal the clusters enriched in gene ontology and KEGG pathway terms with the enrichment score number. Gene Ontology (GO; and the Kyoto Encyclopedia of Genes and Genomes (KEGG; enrichment analysis were performed to identify DEGs. GO was used for categorization, including biological process, molecular function, and cellular component, which is widely used in bioinformatics and increases the possibility of indentifying the most correlative mechanisms. KEGG was used for understanding the most relevant pathway of informative genes. 

Network construction and modules selection

DEGs interaction network can clarify the molecular mechanism of cellular processing. Functional interaction between DEGs was constructed with Cytoscape (version 3.5.1). In this study, the network was extended with the Cytoscape public database. Highly connected nodes were selected as hubs. Some nodes with the highest betweenness centrality were nominated as bottleneck nodes. Then, Molecular Complex Detection (MCODE) was used for screening modules. The functional enrichment analysis of DEGs in each module was performed by DAVID.

Transcriptional regulatory network construction

In order to identify the transcription factor (TF) nodes in the network, the PANTHER Classification System database ( was used (23). Then the transcriptional regulatory network was visualized by Cytoscape. 


Data screening

Based on P-value < 0.05 in comparison to newly diagnosed ITP/chronic ITP, 132 DEGs were identified, consisting of 78 up-regulated (Supplementary Table S1) and 54 down-regulated genes (Supplementary Table S2). As shown in (Figure 1), the medians located at the same level after performing data normalization with R software, indicating a perfect effect. 

Gene ontology and pathway enrichment analysis

Gene Ontology and Pathway functional enrichment analysis were performed according to the P-values < 0.05 on the identified DEG. The enriched term of BP for up-regulated genes was reported in (Table 1 and Supplementary Table S3). They were significantly involved in 10 significant clusters of biological processes, associated with regulation of autophagy, cell cycle checkpoint, regulation of gene expression, cellular component organization or biogenesis, positive regulation of cell projection organization, macromolecule metabolic process, sister chromatid segregation, the establishment of protein localization to the membrane, and regulation of protein tyrosine kinase activity. The up-regulated genes were located in 5 clusters associated with the intracellular part, organelle, nucleus, intracellular non-membrane-bounded organelle, chromosome, centromeric region, and ciliary membrane (Supplementary Table S4). Significant Molecular function represented in (Supplementary Table S5) involved 2 cluster link to nucleic acid binding and protein kinase activity.  

Moreover, the gene ontology related to biological process terms were over-represented in down-regulated DEGs with significant P-value which mainly involved in the modification of morphology or physiology of other organism involved in symbiotic interaction, cellular response to monosaccharide stimulus, programmed cell death, histone methylation, negative regulation of sequence-specific DNA binding transcription factor activity, and positive regulation of binding (Table 2 and Supplementary Table S6). 

The significant cellular components, related to down-regulated genes contained four clusters that were mainly involved in intracellular, organelle part, membrane-bounded organelle, lysosomal membrane, lytic vacuole membrane, and chromosomal part. (Supplementary Table S7). Two significant molecular function clusters for down-regulated genes, represented in (Supplementary Table S8), were related to lipase activity, hydrolase activity, acting on ester bonds, and structure-specific DNA binding.

The significant pathway represented in (Table 3) for up-regulated and down-regulated DEGs .the pathway enrichment analysis for up-regulated DEGs indicated these genes involved in the ErbB signaling pathway, mRNA surveillance pathway, and the Estrogen signaling pathway. Whereas, only the Notch signaling pathway is related to down-regulated genes.

Network construction and modules selection

Based on public databases existing on Cytoscape, the PPI network of DEGs was established. Network analysis was shown consisting of 1137 nodes and 2647 edges (Figure.2A). The cut-off criterion of hub gene selection was set at ≥ 40 degrees. Based on this cut-off, twenty hubs are recognized in the network. Therefore, regarding the cut-off criteria, seventeen genes of DEGs were selected as hub nodes. They consisted of eleven up-regulated (ATF2, VIM, PAK2, SF1, BUB3, PCF11, PCF12, FBXW7, GSPT1, CLIP1, ABL2) and six down-regulated (ARRB1, KPNA2, GRK5, TUFM, RHOG, TEX264) genes (Table 4). 

 Twenty of the highest betweenness centrality including fifteen genes of DEGs were selected as bottleneck containing nine up-regulated (BBS2, RPRD1A, FNBP4, TUBE1, ABCA5, EIF4E3, TNRC6A, TBC1D5, VIM) and six down-regulated (TMEM214, COPRS, MAU2, MRPL45, TBC1D9B, ARRB1) genes (Table 5). Two of the mentioned genes,including VIM and ARRB1, appeared among hub nodes. These two genes were dentified as hub-bottleneck genes. Which confirms the important role of these two genes.

The functional modules were assessed using the MCODE plugin. Five modules were identified including thirty nodes and 36 edges that comprised Module 1 (ZNF324, ZNF224, ZNF382, TRIM28), module 2 (BUB3, CDC42, GRK5, PSMC2, SF1, HDAC6, SRPK1, CLIP1), module 3 (VIM, MEN1, GFAP), module 4 (APP, EIF4E2, ARRB1, ARRB2, ADRB2, YWHAE, USP33), module 5 (ATG7, GSPT1, RALBP1, MIZF, RPD3L1, ARHGAP25, RHOG) (Figure 2B).

We found six DEGs existing in both hub genes and modules, which have significant P-value (P-value < 0.05) for enriched BP. These genes included three up-regulated genes and three down-regulated genes (Table 6). 

Transcriptional regulatory network construction:

One hundred twenty-one nodes with TF function have been identified from 1137 network nodes using the panther database. By using Cytoscape, these 121 nodes have been visualized in a regulatory network.

Five genes of thirty genes existing in modules are TF, including ZNF224, ZNF382, TRIM28, MIZF, and SF1. ZNF382, TRIM28, ZNF224 belong to module one, SF1 belongs to module two, and MIZF belongs to module 5.

Further analysis of potentially remarkable modules was performed by detecting TFs with a high degree of connections with other nodes, the so-called hub-TFs.

It should be noted that SF1 is the hub node in the transcriptional regulatory network. SF1, as a TF encoding gene is a hub-TF (Figure.3). 


Diminished platelet production and enhanced platelet destruction are the familiar characters of ITP (24). However, the first hit for dysregulation of the immune system in ITP remains unknown (25). Understanding the molecular and physiopathological mechanisms of ITP requires many efforts to design new preventive and therapeutic strategies. Due to the interaction of genes and environmental factors in common human diseases, a more integrated biological approach is needed to solve these complexities (26). DNA microarrays are used as a powerful technique in biomedical research. This method has attracted much attention from scientists because of its ability to identify thousands of genes and even the entire genome simultaneously (26). Systemic network analysis of high-throughput data is the most useful technique to explain the important implications of life science. Network features, such as composition and topology are highly relevant to vital cellular functions, so they are critical in biological science research (27). This study tries to find essential genes and mechanisms by bioinformatics analysis of GSE46922 microarray data, which are different between the newly diagnosed and the chronic ITP. This study identifies, 131 DEGs, consisting of 78 up-regulated genes and 53 down-regulated genes, which are differentially expressed between, the newly diagnosed ITP and chronic ITP-.

Our enrichment analysis of the up-regulated DEGs showed that autophagy played a significant role in ITP. There is evidence that the positive regulation of autophagy is the most relevant biological process in ITP associated with the expressed genes in the chronic phase. Autophagy induces to the maintenance of platelet life and physiological functions (28). Improper expression of molecules in the autophagy pathway has been also determined in ITP patients lymphocytes (29). Elevating platelet autophagy has been also shown to diminish platelet destruction by prohibiting apoptosis and amending platelet viability (28). Besides, particular evidence implied that megakaryocytes undergo autophagy in ITP patients (30). The apoptotic process was diminished in accordance with activate autophagy process in chronic ITP.

Our study has shown that down-regulated genes in the chronic phase were mainly enriched in the Notch signaling, closely related to hematopoiesis, which involves the evolving hematopoietic system to generate hematopoietic stem cells and the development of immune cells like in T-cells or progress several autoimmune diseases like ITP (32). Rania Mohsen Gawdat et al. found the correlation of Notch1/Hes1 gene expression levels in Egyptian paediatric patients with newly diagnosed and persistent primary ITP (31, 32). We detected this pathway in newly diagnosed ITP while down-regulated in the chronic phase, and this data has shown that the Notch pathway is replaced by the ErbB signaling pathway, mRNA surveillance pathway, and Estrogen signaling pathway over time to display the chronic phase symptom. Also molecular crosstalk among Notch signaling pthway with ErbB and Estrogen signaling pathways was acknowledged in breast cancer (33). This study also confirms the crosstalk between emerging ErbB and Estrogen pathway and inhibition of the Notch signaling pathway in ITP. The mRNA surveillance pathway was enriched by the up-regulated genes related to the quality control mechanism that targets aberrant mRNAs for degradation (34). This pathway was not reported for ITP but confirm this mechanism in autoimmune disease and cellular defense against virus invasion. Mutations affecting the mRNA surveillance machinery cause chronic activation of defense programs, resulting in autoimmune phenotypes. The Systemic lupus erythematosus (SLE) as a human autoinflammatory and autoimmune disorders are notably linked to this system deviation (34). ITP manifests several symptoms of mimicking diseases like SLE; therefore, one might be aware of this similarity emphasizing with several investigations. Besides, this pathway enriched from down-regulated genes in the chronic phase; it implies that the chronic phase of ITP can be due to perturbations in the pathways.

The network analysis also demonstrated that there are interactions among the DEGs.

Our network analysis revealed a set of candidate genes (three up-regulated and three down-regulated) for the investigation of biomarkers or molecular mechanisms of ITP, which was significantly correlated with chronic ITP, including BUB3, GRK5, SF1, VIM, ARRB1, and RHOG

Our network analysis also verifies the Notch signaling pathway in ITP. In this study, ARRB1 was considered a hub-bottleneck protein with a high degree and high betweenness centrality value. This protein is strongly related to the Notch signaling pathway. Due to its unique features, it has an attractive advantage for drug targeting.

One of the essential genes that play an indispensable role in the maturation of hematopoietic precursors is Vimentin (VIM) that belongs to hub-bottleneck protein. Alteration in expression of VIM has been recognized in the maturation process of the megakaryocytic, granulomonocytic, erythroid, and lymphoid lineages (35). Up-regulated VIM has been also shown in the formation of fully active macrophage-like cells and macrophage polykaryons (36). Rho GTPases (RhoG) is one of the crucial members of our analysis, which has a central regulatory role in platelet production and megakaryocyte maturation (37).

One of the most important genes in this research was SF1. In addition to being a hub, integrating TF’s expression data into Cytoscape indicated that SF1 is also a TF. Kenichi Yoshida et al. reported that there is a mutation in SF1 in hematologic malignancies, but its frequency was not at confidence level for presentation to clinical associations (38).

The G-protein-coupled receptor kinase 5 (GRK5) is a critical member of the threonine/serine kinase family that phosphorylates and regulates the G-protein-coupled receptor (GPCR) signaling pathway. GRK5 has a key role in several diseases; for example, GRK5 is a decisive pathogenic factor in early Alzheimer’s disease, hepatic steatosis and metabolic disorders such as type II diabetes and obesity, injured and failing heart and cancer (39-44). GRK5 also has multiple roles in TLR (Toll-Like Receptor) signaling, which were described as a family of receptors involved in recognizing pathogen-associated molecular patterns (PAMPs) derived from microbes. Moreover, the importance of TLRs has been identified in several inflammatory diseases, including non-infectious diseases (45, 46). In addition, detection of GRK5 expression provides a target for determining the effectiveness of drugs and determining patient prognosis in cancer (47).

The BUB3 is one of the mitotic checkpoint proteins specified by a group of evolutionarily conserved genes. It is believed that the failure of the BUB gene family as a surveillance system is a critical components of the regulatory process which causes genomic instability. This gene family encodes proteins that are a part of a large multi-protein kinetochore complex (48, 49). The BUB3’s importance was found in colorectal cancer at a young age and in low-grade breast cancers (50, 51).

The use of omics technology to identify the mechanism of disease and the discovery of biomarkers has received much attention in recent years. Microarray and proteomics approaches can help to solve biological complexities by creating an extensive list of expressed transcripts that are simultaneously (52). As mentioned in the introduction, Zheng and his colleagues were able to introduce six important markers for the diagnosis of ITP by using Proteomics technology in 2016 (11). However, they have not yet been used in the clinic. Our study using microarray data analysis introduces six new markers that can clarify the pathogenesis of the ITP and need many examinations for clinic application.

Box plot of expression data by analyzing GSE46922 that contain seven newly diagnosed ITP and six chronic ITP samples
(A) network with 1137 nodes and 2647 edges. Unregulated hub genes were shown with red triangle nodes while down-regulated represented with green color (B) Significant modules selected from the network. Pink modules illustrated up-regulated genes, while green nodes illustrated down-regulated genes. Seed nodes are shown in rectangular shape
Visualization in Cytoscape of interactions between TFs, modules and hub-TFs. TFs are shown as triangle. Hubs are displayed in red ellipses. Modules showed by number with different color that contains the nodes are hub (red node), seed (green node) and TF (yellow and red triangle). There is just one red rectangle in module No.2 related to the node that is hub-seed gene. Red triangle related to the nodes are hub-TFs and green triangle is a node related seed-TFs. SF1 and ATF2 are hub-TFs and ZNF382 is a seed-TF. ZNF382 and SF1 are the members of modules No.1 and No.2 respectively
Table 1

Gene ontology enrichment analysis based on biological analysis of up-regulated DEGs. They were selected with significant value P < 0.05. Enrichment Score is related the type of analysis in the DAVID database selecting the"functional annotation clustering" for analysis of gene lists

Annotation Cluster 1Enrichment Score: 1.6089173346492356
GO:1902589single-organism organelle organization0.005313
GO:0000226microtubule cytoskeleton organization0.020728
GO:0007017microtubule-based process0.026109
Annotation Cluster 2Enrichment Score: 1.5454786370206883
GO:0010506regulation of autophagy0.003125
GO:0010508positive regulation of autophagy0.03688
Annotation Cluster 3Enrichment Score: 1.1276637957883262
GO:0000075cell cycle checkpoint0.010137
GO:0022402cell cycle process0.010174
GO:0000077DNA damage checkpoint0.017372
GO:0007049cell cycle0.017944
GO:0031570DNA integrity checkpoint0.020624
GO:0007093mitotic cell cycle checkpoint0.020967
GO:0045930negative regulation of mitotic cell cycle0.047204
Annotation Cluster 4Enrichment Score: 1.0524732568167308
GO:0016043cellular component organization0.03475
GO:0006996organelle organization0.046543
GO:0071840cellular component organization or biogenesis0.04844
Annotation Cluster 5Enrichment Score: 0.9254147029064898
GO:0031344regulation of cell projection organization0.004985
GO:0010975regulation of neuron projection development0.018065
GO:0030030cell projection organization0.020064
GO:0031346positive regulation of cell projection organization0.02586
GO:0031175neuron projection development0.030331
GO:0030182neuron differentiation0.035135
Annotation Cluster 6Enrichment Score: 0.9097209940740182
GO:0043170macromolecule metabolic process0.001037
GO:0010468regulation of gene expression0.008941
GO:0060255regulation of macromolecule metabolic process0.008953
GO:0019222regulation of metabolic process0.011749
GO:0044260cellular macromolecule metabolic process0.014544
GO:0010467gene expression0.017106
GO:0010558negative regulation of macromolecule biosynthetic process0.033922
GO:0009892negative regulation of metabolic process0.038268
GO:0010605negative regulation of macromolecule metabolic process0.041577
GO:0031327negative regulation of cellular biosynthetic process0.043543
GO:0031324negative regulation of cellular metabolic process0.044105
GO:0009890negative regulation of biosynthetic process0.047731
GO:0051172negative regulation of nitrogen compound metabolic process0.048092
Annotation Cluster 8Enrichment Score: 0.7138986023041735
GO:0007062sister chromatid cohesion0.011798
GO:0000819sister chromatid segregation0.049886
Annotation Cluster 11Enrichment Score: 0.5802981069084664
GO:0090150establishment of protein localization to membrane0.046903
Annotation Cluster 17Enrichment Score: 0.42916203800540753
GO:0018108peptidyl-tyrosine phosphorylation0.009263
GO:0018212peptidyl-tyrosine modification0.00948
Annotation Cluster 18Enrichment Score: 0.40498640863411367
GO:0018108peptidyl-tyrosine phosphorylation0.009263
GO:0018212peptidyl-tyrosine modification0.00948
GO:0044260cellular macromolecule metabolic process0.014544
GO:0061097regulation of protein tyrosine kinase activity0.023351
Table 2

Gene ontology enrichment analysis based on biological analysis of down-regulated DEGs. They were selected with a significant value P < 0.05. Enrichment Score is related the type of analysis in the DAVID database, selecting the “functional annotation clustering” for analysis of gene lists

Annotation Cluster 2Enrichment Score: 0.7613112520752194
GO:0044267cellular protein metabolic process0.011896
GO:0043412macromolecule modification0.01846
GO:0019538protein metabolic process0.018791
GO:0006807nitrogen compound metabolic process0.019514
GO:0009059macromolecule biosynthetic process0.021932
GO:0043170macromolecule metabolic process0.028871
GO:0044249cellular biosynthetic process0.033903
GO:0034641cellular nitrogen compound metabolic process0.034934
GO:0044237cellular metabolic process0.043685
GO:0008152metabolic process0.044952
GO:0044238primary metabolic process0.04675
GO:0006464cellular protein modification process0.047376
GO:0036211protein modification process0.047376
GO:0009058biosynthetic process0.048575
GO:0071704organic substance metabolic process0.04992
Annotation Cluster 3Enrichment Score: 0.7085147802867421
GO:0051817modification of morphology or physiology of other organism involved in symbiotic interaction0.023652
GO:0035821modification of morphology or physiology of other organism0.033336
Annotation Cluster 5Enrichment Score: 0.5438770693729118
GO:0016570histone modification0.005122
GO:0016569covalent chromatin modification0.012772
GO:0006325chromatin organization0.012912
GO:0090630activation of GTPase activity0.017421
GO:0032092positive regulation of protein binding0.017839
GO:0006996organelle organization0.019368
GO:0018205peptidyl-lysine modification0.01994
GO:0051817modification of morphology or physiology of other organism involved in symbiotic interaction0.023652
GO:0071333cellular response to glucose stimulus0.026068
GO:0071331cellular response to hexose stimulus0.027564
GO:0071326cellular response to monosaccharide stimulus0.027564
GO:0051276chromosome organization0.029444
GO:0006915apoptotic process0.030391
GO:0071322cellular response to carbohydrate stimulus0.032253
GO:0035821modification of morphology or physiology of other organism0.033336
GO:0001678cellular glucose homeostasis0.034432
GO:0012501programmed cell death0.042073
GO:0016571histone methylation0.0443
GO:0006464cellular protein modification process0.047376
GO:0036211protein modification process0.047376
GO:0043433negative regulation of sequence-specific DNA binding transcription factor activity0.048006
GO:0051099positive regulation of binding0.0499
Table 3

KEGG Pathway enrichment analysis of up-regulated and down-regulated DEGs. They were selected with a significant value P < 0.05 in the DAVID database

Up-regulated DEGs
1hsa04012ErbB signaling pathway0.033682PAK2, ABL2, AKT3
2hsa03015mRNA surveillance pathway0.036574PCF11, GSPT1, MSI2
3hsa04915Estrogen signaling pathway0.042636FKBP5, AKT3, ATF2
Down-regulated DEGs
1hsa04330Notch signaling pathway0.009201HDAC1;MFNG;DTX1
Table 4

Hub gene with the cut-off criterion degrees ≥ 40. Three genes did not excist in DEGs and were added by Cytoscape software. Bottleneck genes showed by star in the betweenness centrality column

UniProtKB IDGene nameDegreeBetweeness centrality
Added by network
2Q71U36Tubulin B-alpha-11340.135022*
Table 5

The genes with the highest betweenness centrality were selected as the bottleneck. Five genes did not excisted among the DEGs and were added by cytoscape software. Stars in degree column show the importance of these gnes as hub genes

UniProtKB IDGene nameBetweenness centralityDegree
Added by network
5Q71U36Tubulin B-alpha-10.13502234134*
Table 6

Key genes related to chronic ITP that selected based on multiple criteria of data analysis. Hub gene with the cut-off criterion degrees ≥ 40 which are also existed in modules selected as potential biomarkers for chronic ITP. The fold change in expressed genes in microarray selected based on M index that is log2 fold change

Gene nameGene IDDegreeBetweeness centralityMBiological process
1VIMP086701110.123853461.077912156positive regulation of protein ubiquitination involved in ubiquitin-dependent protein catabolic process (GO:2000060)
2SF1Q15637900.084551832.419055031mRNA splice site selection (GO:0006376),spliceosomal complex assembly (GO:0000245),mRNA 3'-splice site recognition (GO:0000389)
3BUB3O43684850.058293740.846438817regulation of translation (GO:0006417)
1ARRB1P494071250.12107173-1.855926621regulation of Notch signaling pathway (GO:0008593), negative regulation of sequence-specific DNA binding transcription factor activity (GO:0043433), negative regulation of NF-kappaB transcription factor activity (GO:0032088), positive regulation of histone H4 acetylation (GO:0090240), desensitization of G-protein coupled receptor protein signaling pathway (GO:0002029), regulation of histone H4 acetylation (GO:0090239), positive regulation of cellular metabolic process (GO:0031325), contractile actin filament bundle assembly (GO:0030038), stress fiber assembly (GO:0043149), negative regulation of cytokine production (GO:0001818), positive regulation of peptidyl-lysine acetylation (GO:2000758), negative regulation of interleukin-8 production (GO:0032717), modification-dependent protein catabolic process (GO:0019941)
2GRK5P34947590.03635575-0.896145434tachykinin receptor signaling pathway (GO:0007217), regulation of signal transduction (GO:0009966), positive regulation of cell proliferation (GO:0008284), regulation of cell proliferation (GO:0042127)
3RHOGP84095400.02847864-1.37279767Rac protein signal transduction (GO:0016601), activation of GTPase activity (GO:0090630), positive regulation of GTPase activity (GO:0043547), positive regulation of cell proliferation (GO:0008284), engulfment of apoptotic cell (GO:0043652), phagocytosis, engulfment (GO:0006911), neutrophil degranulation (GO:0043312),neutrophil activation involved in immune response (GO:0002283), neutrophil mediated immunity (GO:0002446), regulation of cell proliferation (GO:0042127)


The current study has obtained DEGs using comprehensive bioinformatics analysis of high-throughput data released from microarray analysis to find the possible biomarkers. In summary, a total of 132 DEGs were screened, and six genes, including BUB3, GRK5, SF1, VIM, ARRB1, and RHOG, previously have not been reported as signature genes in ITP; here we found that they might play critical roles in chronic ITP. This research contributes new insights into the molecular mechanisms of newly diagnosed ITP and chronic ITP. These six genes together could be considered as a panel of biomarkers to differentiate newly from chronic ITP. Thus, additional investigations are needed to focus on the clinical application of these genes.



  • 1.

    Michel M. Immune thrombocytopenic purpura: epidemiology and implications for patients. Eur. J. Haematol. 2009;82:3-7.

  • 2.

    Behzad MM, Asnafi AA, Jalalifar MA, Moghtadaei M, Jaseb K, Saki N. Cellular expression of CD markers in immune thrombocytopenic purpura: implications for prognosis. Apmis. 2018;126:523-32. [PubMed ID: 29924452].

  • 3.

    Radwan ER, Goda RL. Lack of impact of cytotoxic T-lymphocyte antigen 4 gene exon 1 polymorphism on susceptibility to or clinical course of Egyptian childhood immune thrombocytopenic purpura. CATH. 2015;21:378-82.

  • 4.

    Alavi S, Aryan Z, Ghazizadeh F, Arabi N, Nikougoftar M, Ebadi M. The immunophenotype of bone marrow lymphocytes in children with immune thrombocytopenic purpura. Pediatr. Hematol. Oncol. 2014;31:548-54. [PubMed ID: 25007136].

  • 5.

    Stasi R, Newland AC. ITP: a historical perspective. Br. J. Haematol. 2011;153:437-50. [PubMed ID: 21466538].

  • 6.

    Pan JQ, Wang W, Wang ML, Li X-Y, Wang JH, Zhao WW, Tian YY, Dong XS, Jiang YF. Recognition of the human antibody-mediated platelet destruction in adult ITP patients by C-reactive protein. Int. J. Clin. Exp. Pathol. 2017;10:10176-85. [PubMed ID: 31966351].

  • 7.

    Nazy I, Kelton JG, Moore JC, Clare R, Horsewood P, Smith JW, Ivetic N, D’Souza V, Li N, Arnold DM. Autoantibodies to thrombopoietin and the thrombopoietin receptor in patients with immune thrombocytopenia. Br. J. Haematol. 2018;181:234-41. [PubMed ID: 29532903].

  • 8.

    Imbach P, Crowther M. Thrombopoietin-receptor agonists for primary immune thrombocytopenia. N. Engl. J. Med. 2011;365:734-41. [PubMed ID: 21864167].

  • 9.

    Frydman GH, Davis N, Beck PL, Fox JG. Helicobacter pylori eradication in patients with immune thrombocytopenic purpura: a review and the role of biogeography. Helicobacter. 2015;20:239-51. [PubMed ID: 25728540].

  • 10.

    Li X, Sheng Z, Sun Y, Wang Y, Xu M, Zhang Z, Li H, Shao L, Zhang Y, Yu J. Human leukocyte antigen-G upregulates immunoglobulin-like transcripts and corrects dysfunction of immune cells in immune thrombocytopenia. Haematologica. 2020;105:1-47.

  • 11.

    Zhang HW, Zhou P, Wang K-Z, Liu J-B, Huang Y-S, Tu YT, Deng ZH, Zhu XD, Hang YL. Platelet proteomics in diagnostic differentiation of primary immune thrombocytopenia using SELDI-TOF-MS. Clinica. Chimica. Acta. 2016;455:75-79.

  • 12.

    Provan D, Stasi R, Newland AC, Blanchette VS, Bolton-Maggs P, Bussel JB, Chong BH, Cines DB, Gernsheimer TB, Godeau B. International consensus report on the investigation and management of primary immune thrombocytopenia. Blood. Am. J. Hematol. 2010;115:168-86.

  • 13.

    Samson M, Fraser W, Lebowitz D. Treatments for Primary Immune Thrombocytopenia: A Review. Cureus. 2019;11:e5849. [PubMed ID: 31754584].

  • 14.

    Zainal A, Salama A, Alweis R. Immune thrombocytopenic purpura. J. Community Hosp. Intern. Med. Perspect. 2019;9:59-61. [PubMed ID: 30788080].

  • 15.

    Matzdorff A, Meyer O, Ostermann H, Kiefel V, Eberl W, Kühne T, Pabinger I, Rummel M. Immune thrombocytopenia-current diagnostics and therapy: recommendations of a Joint Working Group of DGHO, ÖGHO, SGH, GPOH, and DGTI. Oncol. Res. Treat. 2018;41:1-30.

  • 16.

    Neunert CE. Current management of immune thrombocytopenia. Hematology. 2013;2013:276-82. [PubMed ID: 24319191].

  • 17.

    Quackenbush J. Computational analysis of microarray data. Nat. Rev. Genet. 2001;2:418-27. [PubMed ID: 11389458].

  • 18.

    Butte A. The use and analysis of microarray data. Nat. Rev. Drug Discov. 2002;1:951-60. [PubMed ID: 12461517].

  • 19.

    Arrell D, Terzic A. Network systems biology for drug discovery. Clin. Pharmacol. Ther. 2010;88:120-5. [PubMed ID: 20520604].

  • 20.

    Penrod NM, Cowper Sal lari R, Moore JH. Systems genetics for drug target discovery. Trends Pharmacol. Sci. 2011;32:623-30. [PubMed ID: 21862141].

  • 21.

    Jernås M, Hou Y, Strömberg Célind F, Shao L, Nookaew I, Wang Q, Ju X, Mellgren K, Wadenvik H, Hou M. Differences in gene expression and cytokine levels between newly diagnosed and chronic pediatric ITP. Blood Am. J. Hematol. 2013;122:1789-92.

  • 22.

    Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009;4:44-57. [PubMed ID: 19131956].

  • 23.

    Thomas PD, Kejariwal A, Campbell MJ, Mi H, Diemer K, Guo N, Ladunga I, Ulitsky Lazareva B, Muruganujan A, Rabkin S. Muruganujan and S. Rabkin, PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 2003;31:334-41. [PubMed ID: 12520017].

  • 24.

    Sun RJ, Shan NN. Megakaryocytic dysfunction in immune thrombocytopenia is linked to autophagy. Cancer Cell Int. 2019;19:59-69. [PubMed ID: 30923461].

  • 25.

    Gernsheimer T. Chronic idiopathic thrombocytopenic purpura: mechanisms of pathogenesis. Oncologist. 2009;14:12-21. [PubMed ID: 19144680].

  • 26.

    Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S. Genetics of gene expression and its effect on disease. Nature. 2008;452:423-8. [PubMed ID: 18344981].

  • 27.

    Liang M, Cowley Jr AW, Greene AS. High throughput gene expression profiling: a molecular approach to integrative physiology. J. Physiol. 2004;554:22-30. [PubMed ID: 14678487].

  • 28.

    Wang CY, Ma S, Bi SJ, Su L, Huang SY, Miao JY, Ma CH, Gao CJ, Hou M, Peng J. Enhancing autophagy protects platelets in immune thrombocytopenia patients. Ann. Transl. Med. 2019;7:8-18. [PubMed ID: 30788355].

  • 29.

    Shan N-n, Dong Ll, Zhang Xm, Liu X, Li Y. Targeting autophagy as a potential therapeutic approach for immune thrombocytopenia therapy. Crit. Rev. Oncol. Hematol. 2016;100:11-5. [PubMed ID: 26830007].

  • 30.

    Liu Z, Mei T. Immune thrombocytopenia induces autophagy and suppresses apoptosis in megakaryocytes. Mol. Med. Rep. 2018;18:4016-22. [PubMed ID: 30106156].

  • 31.

    Schwanbeck R, Just U. The Notch signaling pathway in hematopoiesis and hematologic malignancies. haematologica. 2011;96:1735-7. [PubMed ID: 22147769].

  • 32.

    Gawdat RM, Hammam AA, Ezzat DA. Correlation of Notch1/Hes1 Genes Expression Levels in Egyptian Paediatric Patients with Newly Diagnosed and Persistent Primary Immune (Idiopathic) Thrombocytopenic Purpura. Indian J. Hematol. Blood Transfus. 2016;32:362-7. [PubMed ID: 27429531].

  • 33.

    Ma D, Zhu Y, Ji C, Hou M, Hou. Targeting the Notch signaling pathway in autoimmune diseases. Expert Opin. Ther. Targets. 2010;14:553-65. [PubMed ID: 20334488].

  • 34.

    Rigby RE, Rehwinkel J. RNA degradation in antiviral immunity and autoimmunity. Trends. Immunol. 2015;36:179-88. [PubMed ID: 25709093].

  • 35.

    Dellagi K, Vainchenker W, Vinci G, Paulin D, Brouet JC. Alteration of vimentin intermediate filament expression during differentiation of human hemopoietic cells. EMBO. J. 1983;2:1509-14. [PubMed ID: 11892803].

  • 36.

    Beneš P, Macečková V, Zdráhal Z, Konečná H, Zahradníčková E, Mužík J, Šmarda J. Role of vimentin in regulation of monocyte/macrophage differentiation. Differentiation. 2006;74:265-76. [PubMed ID: 16831196].

  • 37.

    Pleines I, Cherpokova D, Bender M. Rho GTPases and their downstream effectors in megakaryocyte biology. Platelets. 2019;30:9-16. [PubMed ID: 29913074].

  • 38.

    Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, Sato Y, Sato Otsubo A, Kon A, Nagasaki M. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011;478:64-69. [PubMed ID: 21909114].

  • 39.

    Suo Z, Cox AA, Bartelli N, Rasul I, Festoff BW, Premont RT, Arendash GW. GRK5 deficiency leads to early Alzheimer-like pathology and working memory impairment. Neurobiol. Aging. 2007;28:1873-88. [PubMed ID: 17011668].

  • 40.

    Wang L, Shen M, Wang F, Ma L. GRK5 ablation contributes to insulin resistance. Biochem. Biophys. Res. Commun. 2012;429:99-104. [PubMed ID: 23111327].

  • 41.

    Islam KN, Bae JW, Gao E, Koch WJ. Regulation of nuclear factor κB (NF-κB) in the nucleus of cardiomyocytes by G protein-coupled receptor kinase 5 (GRK5). J. Biol. Chem. 2013;288:35683-9. [PubMed ID: 24174526].

  • 42.

    Chakraborty PK, Zhang Y, Coomes AS, Kim WJ, Stupay R, Lynch LD, Atkinson T, Kim JI, Nie Z, Daaka Y. G Protein–Coupled Receptor Kinase GRK5 Phosphorylates Moesin and Regulates Metastasis in Prostate Cancer. Cancer Res. 2014;74:3489-500. [PubMed ID: 24755472].

  • 43.

    Kaur G, Kim J, Kaur R, Tan I, Bloch O, Sun MZ, Safaee M, Oh MC, Sughrue M, Phillips J. G-protein coupled receptor kinase (GRK)-5 regulates proliferation of glioblastoma-derived stem cells. J. Clin. Neurosci. 2013;20:1014-8. [PubMed ID: 23693024].

  • 44.

    Kim JI, Chakraborty P, Wang Z, Daaka Y. G-protein coupled receptor kinase 5 regulates prostate tumor growth. J. Urol. 2012;187:322-9. [PubMed ID: 22099983].

  • 45.

    O’Neill LA, Bryant CE, Doyle S. Therapeutic targeting of Toll-like receptors for infectious and inflammatory diseases and cancer. Pharmacol. Rev. 2009;61:177-97. [PubMed ID: 19474110].

  • 46.

    Packiriswamy N, Parvataneni S, Parameswaran N. Overlapping and distinct roles of GRK5 in TLR2-, and TLR3-induced inflammatory response in-vivo, Cell. Immunol. 2012;272:107-11.

  • 47.

    Delaney A, Yoganathan T. G protein coupled receptor kinase 5 (GRK5) and its uses. Google Patents. 2002;649:1-16.

  • 48.

    Grabsch H, Takeno S, Parsons WJ, Pomjanski N, Boecking A, Gabbert HE, Mueller W. Overexpression of the mitotic checkpoint genes BUB1, BUBR1, and BUB3 in gastric cancer—association with tumour cell proliferation. J. Pathol. 2003;200:16-22. [PubMed ID: 12692836].

  • 49.

    Sudakin V, Chan GK, Yen TJ. Checkpoint inhibition of the APC/C in HeLa cells is mediated by a complex of BUBR1, BUB3, CDC20, and MAD2. J. Cell Biol. 2001;154:925-36. [PubMed ID: 11535616].

  • 50.

    de Voer RM, van Kessel AG, Weren RD, Ligtenberg MJ, Smeets D, Fu L, Vreede L, Kamping EJ, Verwiel ET, Hahn MM. Germline mutations in the spindle assembly checkpoint genes BUB1 and BUB3 are risk factors for colorectal cancer. Gastroenterology. 2013;145:544-7. [PubMed ID: 23747338].

  • 51.

    Mukherjee A, Joseph C, Craze M, Chrysanthou E, Ellis IO. The role of BUB and CDC proteins in low-grade breast cancers. Lancet. 2015;385:S72. [PubMed ID: 26312894].

  • 52.

    Schneider MV, Orchard S. Omics technologies, data and bioinformatics principles, Bioinformatics for omics Data. Springer. 2011:3-30.