Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2015.4349 mmr-12-05-7358 Articles Gene microarray analyses for potential biomarkers of single and recurrent venous thromboembolism ZHOUWUGANG1 ZHANGKE2 CHENDONGRUI2 GAOPINGJIN2 WANGQIAO1 Emergency Department, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, P.R. China Department of Hypertension, Ruijin Hospital, Shanghai Key Lab of Hypertension, Shanghai Institute of Hypertension, Shanghai Jiaotong University School of Medicine, Shanghai 200025, P.R. China Correspondence to: Dr Qiao Wang, Emergency Department, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, P.R. China, E-mail: qiaowangqw@163.com 11 2015 22 09 2015 12 5 7358 7366 13 11 2014 04 08 2015 Copyright: © Zhou et al. 2015 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Venous thromboembolism is a major cause of morbidity and mortality with a high recurrence rate. The present study aimed to explore the molecular mechanisms and potential biomarkers of single venous thromboembolism (SVTE) and recurrent venous thromboembolism (RVTE). The microarray dataset GSE19151 was downloaded from Gene Expression Omnibus, which contained data from whole blood samples from 63 healthy controls, 32 SVTE and 38 RVTE patients. Differentially expressed genes (DEGs) in the SVTE and RVTE groups compared with those in the controls were identified using the t-test, followed by clustering analysis of DEGs and samples. Functional and pathway enrichment analyses were performed for DEGs in patients with RVTE and SVTE, as well as specific DEGs in patients with RVTE. The identified 42 DEGs in RVTE were mainly enriched in biological processes of cellular protein metabolism, gene expression and translational elongation as well as in pathways associated with ribosomes, Parkinson's disease and oxidative phosphorylation. In SVTE, 20 DEGs were identified, which were mainly involved in biological processes of biopolymer biosynthesis, translational elongation and cellular protein metabolism as well as pathways associated with ribosomes and cardiac muscle contraction. In RVTE, 22 specific DEGs were mainly involved in translational elongation, negative regulation of the force of heart contraction by chemical signals, cell proliferation, ribosomal pathways and protein export. The identified DEGs of SVTE, including COX7C and UQCRQ, may be potential biomarkers for SVTE, and the specific DEGs of RVTE, including ADRBK1, NDUFA5 and ATP5O, may be potential biomarkers for RVTE.

 venous thromboembolism recurrence biomarkers bioinformatics Introduction

Venous thromboembolism (VTE), comprising deep venous thrombosis and pulmonary embolism, is a frequent disease with high morbidity and mortality, which affects 1–2 per 1,000 individuals (1–3). Furthermore, VTE is associated with a significant rate of recurrence in as many as 30% of VTE patients after termination of the standard course of anti-coagulant therapy (4–6). Therefore, it is desired to explore the molecular mechanisms and potential biomarkers that enable clinicians to identify patients at a risk of single VTE (SVTE) or recurrent VTE (RVTE) for prompt clinical diagnosis and early prevention (7).

Kyrle et al (8) reported that patients with a high level of factor VIII have an increased risk of RVTE. A study by Comp and Esmon (9) suggested that the levels of protein S may be used in the evaluation of RVTE. Various established and novel biomarkers, including D-dimer, E-selectin, P-selectin, thrombin, inflammatory markers and C-reactive protein, have been investigated for their predictive value in SVTE and RVTE (10–13). However, only a small number of biomarkers, such as D-dimer, associated with a first or recurrent event of VTE were highlighted by these studies, while novel and promising biomarkers, including P-selectin and inflammatory cytokines, are still controversial (1). A study by Lewis et al (14) performed a pathway enrichment analysis of differentially expressed genes (DEGs) in samples from patients with SVTE and samples from patients with RVTE and found that insulin-like growth factor receptor 1 and Akt pathways may be useful for distinguishing patients with SVTE from those with RVTE.

The present study identified DEGs in RVTE and SVTE, as well as specific DEGs in RVTE. Functional and pathway enrichment analyses for these DEGs were performed to explore the molecular mechanisms and potential biomarkers of SVTE and RVTE in order to facilitate the diagnosis and clinical therapy management of VTE.

 Materials and methods Affymetrix microarray data

The gene expression profile dataset GSE19151 was obtained from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), which was deposited by Lewis et al (14). Microarray data from 133 whole blood specimens were available, including 63 samples from healthy controls, 32 samples from patients with SVTE (sampled at <three years since their most recent VTE) and 38 samples from subjects with recurrent venous thromboembolism (RVTE) on warfarin. The platform was GPL571 (HG-U133A_2) Affymetrix Human Genome U133A 2.0 Array (Affymetrix, Inc., Santa Clara, CA, USA).

 DEG analysis and gene clustering analysis

For the GSE19151 dataset, the Bioconductor software package in R (http://www.bioconductor.org/; version 3.1) was implemented to analyze the 133 blood gene chips (15). Background correction and quartile data normalization were performed using the robust multiarray average algorithm with defaulted parameters in the Affy package (http://www.bioconductor.org/packages/release/bioc/html/affy.html; version 1.46.1) (16). The t-test was used to identify DEGs using the Simpleaffy package (http://www.bioconductor.org/packages/release/bioc/html/simpleaffy.html; version 2.44.0) (17). The DEGs were selected with the cutoff criteria of P<0.05 and |log(fold change)|>1. Hierarchical clustering analysis of the DEGs was performed using the Hclust command in R and the default complete linkage method (18).

 Gene ontology (GO) functional and pathway enrichment analyses

The Integrated GEne and PROtein annotation Server (IGEPROS; http://www.biosino.org/iGepros/index.jsp) (19) bioinformatics resources consist of an integrated biological knowledge base and analytic tools aimed at systematically extracting biological information from large gene or protein lists. IGEPROS was used to perform the GO (http://www.geneontology.org/) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/pathway.html) pathway enrichment analyses for the identified DEGs with the threshold of P<0.05. The pathview package in R was utilized to depict the KEGG pathway (20).

 Results DEG selection and hierarchical clustering analysis

A total of 42 DEGs were identified between RVTE and normal whole-blood specimens (RVTE vs. control), including 35 up- and 7 downregulated genes. Subsequently, 20 DEGs between SVTE and normal whole-blood specimens (SVTE vs. control) were identified, including 17 up- and 3 downregulated genes. A total of 22 non-overlapping genes were selected as specific DEGs of RVTE, including 18 up- and 4 downregulated genes (Table I). Hierarchical clustering analysis was performed for the 42 DEGs from the 133 whole blood specimens of patients with SVTE, patients with RVTE and healthy controls. The result of this clustering analysis suggested that these DEGs may have important roles in VTE (Fig. 1).

 GO enrichment analysis of DEGs

GO enrichment analysis was performed for 42 DEGs in RVTE, 20 DEGs in SVTE and 22 specific DEGs of RVTE. In RVTE, most enriched GO terms of DEGs in biological processes were associated with biopolymer biosynthesis, including cellular protein metabolism (P=1.12×10−8), gene expression (P=1.36×10−6), translational elongation (P=9.65×10−27) and cellular macromolecular biosynthetic processes (P=8.78×10−6). In the cellular component category, enriched GO terms were mainly associated with the ribosomal sub-unit (P=2.03×10−18), cytosol (P=2.43×10−12) and macromolecular complexes (P=8.61×10−11). In the molecular function category, GO terms enriched for DEGs in RVTE included structural constituents of ribosomes (P=9.13×10−24), insulin-like growth factor binding (P=0.002) and beta-adrenergic receptor kinase activity (P=0.005) (Tables II–IV).

In SVTE, the most significantly enriched GO terms for DEGs in biological processes included biopolymer biosynthesis (P=2.35×10−5), translational elongation (P=2.35×10−5), cellular protein metabolism (P=1.24×10−7) and rRNA processing (P=0.006). The predominantly enriched GO terms of the cellular component category were the cytosol (P=1.68×10−11), the cytosolic large ribosomal sub-unit (P=8.62×10−10) and macromolecular complexes (P=4.86×10−9). The main GO terms of DEGs in SVTE in the molecular function category were structural constituents of ribosomes (P=4.69×10−20), RNA binding (P=2.79×10−5) and protein channel activity (P=0.001). Among the DEGs in SVTE, 12 genes, including RPS3A and RPS7, were involved in translational elongation, 2 genes (UQCRQ and COX7C) were involved in oxidoreductase activity and 2 genes (HINT1 and LAMP1) were associated with proteolysis (Tables II–IV).

The GO terms for biological processes of the 22 specific DEGs of RVTE were mainly translational elongation (P=3.52×10−7), negative regulation of the force of heart contraction by chemical signals (P=0.001) and cell proliferation (P=0.002). In the cellular component category, enriched GO terms were mainly associated with the cytosolic small ribosomal sub-unit (P=1.56×10−5), ribosomal sub-unit (P=1.84×10−5) and macromolecular complexes (P=0.0008). The specific GO terms of the 22 specific DEGs in RVTE in the molecular function category were mainly associated with structural constituents of ribosomes (P=2.55×10−6), insulin-like growth factor binding (P=0.0006), beta-adrenergic receptor kinase activity (P=0.003) and phospholipase A2 inhibitor activity (P=0.004). Two genes (IGF2R and IGFBP7) were shown to be involved in anti-apoptotic signaling, ten genes, including RPL21 and RPS21, were involved in translational elongation and six genes, including NDUFA5, ATP5O and ADRBK1, were associated with the force of heart contraction (Tables II–IV).

 Pathway enrichment analysis of DEGs

The DEGs identified in the present study were enriched in nine pathways (Table V). The RVTE DEGs were mainly enriched in ribosomal pathways (P=1.59×10−23), Parkinson's disease (P=0.007) and oxidative phosphorylation (P=0.008). The SVTE DEGs were enriched in ribosomal pathways (P=4.58×10−19) and cardiac muscle contraction (P=0.025). The non-overlapping DEGs were enriched in ribosomal pathways (P=2.25×10−6) and protein export (P=0.03).

 Metabolic pathway visualization analysis of specific DEGs of RVTE

The metabolic pathways were visualized in schemes depicting the ribosomal pathway (Fig. 2) and the protein export pathway (Fig. 3; http://www.genome.jp/kegg/tool/map_pathway2.html). In the ribosomal pathway, RPL21, RPS21, RPS24 and RPS27 were upregulated and in the protein export pathway, SRP9 was upregulated. The results suggested that these genes may be critical in RVTE and that certain variations in the expression of these genes may lead to an increased risk of recurrence.

 Discussion

In recent years, the application of adequate thromboprophylaxis has led to significant progress in the management of VTE by successfully reducing morbidity and mortality (21). However, to date, methods for effectively preventing and diagnosing SVTE and RVTE have remained controversial (22). The present study used bioinformatics methods to investigate the molecular mechanisms and potential biomarkers of SVTE and RVTE.

In the present study, gene expression profiles of whole blood samples were successfully used to screen for DEGs in specimens from patients with SVTE compared with those in control specimens. With regard to enriched biological processes and pathways for DEGs in SVTE, genes involved in ribosomal pathways, including RPS3A and RPS7, and mitochondrial function, including UQCRQ and COX7C, were indicated to be most consistently affected and modulated. Ribosomal proteins have remained highly conserved during evolution and reflect critical functions in ribosome biogenesis; in addition, several ribosomal proteins were shown to have extra-ribosomal functions in apoptosis, DNA repair and genetic disease (23). A total of 12 DEGs were involved in ribosomal pathways. A paucity of studies have explored the pathogenesis of VTE. It has previously been indicated that the ribosomal-related RP-MDM2-P53 axis may be involved in the molecular pathogenesis of the 5q syndrome, and VTE was reported in 3% of patients with 5q syndrome (24). COX7C and UQCRQ are constituents of the mitochondrial respiratory chain (25). Mutations of these two genes may increase oxidative stress in coronary artery disease (26). The mortality after VTE is strongly associated with presentation of underlying cardiovascular disease (27). First-time VTE in numerous patients is idiopathic and challenging to diagnose, while COX7C and UQCRQ may represent novel biomarkers to identify SVTE.

The specific DEGs in RVTE were found to be mainly involved in ribosomal pathways, heart contraction and oxidative phosphorylation. Pathway visualization revealed that RPL21, RPS21, RPS24 and RPS27, which all encode ribosomal proteins, were enriched in ribosomal pathways, while SRP9 was enriched in the protein export pathway. Furthermore, RPL21, RPS21, RPS24 and RPS27 were found to be involved in RVTE through critical ribosome biogenesis or extra-ribosomal functions; of note, the expression of these genes was upregulated in patients with RVTE but unchanged in patients with SVTE. It has been reported that certain diseases, including transient ischemia/reperfusion and preeclampsia, are associated with ribosomes (28,29). RPS24 mutation was potentially linked to pathologies of Diamond-Blackfan anemia (30). The above results suggested that RPL21, RPS21, RPS24 and RPS27 may have critical roles in RVTE.

The results of the present study showed that β-adrenergic receptor kinase 1 (ADRBK1) was closely associated with RVTE and involved in heart contraction. Polymorphisms in ADRB2 and LPL genes are known to have central roles in vascular biology (31). A previous study suggested that the use of ADRBK1 as a biomarker significantly improved heart failure therapy (32). Certain studies showed that the ADRBK1/phosphoinositide-3 kinase (PI3K) complex improved cardiac function and that PI3K-dependent phosphorylation of ADRBK1 on Ser670 is responsible for the downregulation of ADRBK1 protein via a proteasome-dependent pathway (33,34). Furthermore, PI3K influences insulin-like growth factor and blood vessel-related factor through G protein beta gamma (35). These results may suggest that ADRBK1 has a critical role in RVTE by serving as a dual effector of the compensatory myocardial diastole and the PI3K response.

The present study also identified NDUFA5 and ATP ATP5O as DEGs, which were significantly associated with oxidative phosphorylation. The oxidative stress injury and exitotoxicity in mitochondria induced by NDUFA5 and ATP5O have been proved to be the cause of a variety of nervous system degenerative diseases, including Parkinson's, Alzheimer's and Huntington's disease (36,37). Free-radical generation and consequent oxidative stress in platelet activation and thrombotic vascular diseases have a distinctive role with the potential injurious effects of homocysteine (38,39). Therefore, NDUFA5 and ATP5O inducing oxidative stress injury and exitotoxicity in mitochondria may also have an impact on RVTE.

In conclusion, the screening performed in the present study identified 42 DEGs in RVTE, including 35 up- and 7 downregulated genes, 20 DEGs in SVTE, including 17 up- and 3 downregulated genes, and 22 specific DEGs in RVTE. Furthermore, functional and pathway enrichment analysis was performed for these identified DEGs. The results indicated that DEGs in SVTE, including COX7C and UQCRQ, may be used as potential biomarkers for SVTE and that specific DEGs in RVTE, including ADRBK1, NDUFA5 and ATP5O, may be considered as potential biomarkers of RVTE. However, experimental studies are required to confirm these results.

 Acknowledgments

The present study was supported by the National Natural Science Foundation of China (grant no. 81370255).

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Clustering dendrogram constructed using the Hclust clustering algorithm. Horizontal red and blue bars represent the patient specimens (single or recurrent venous thromboembolism) and healthy specimens, respectively, and vertical axes represent differentially expressed genes. Expression levels are represented by a color key in which bright red represents the lowest levels and bright yellow represents the highest levels, and less saturated shades represent intermediate levels of expression.

Visualization of the ribosome pathway. Red nodes represent upregulated differentially expressed genes.

Visualization of the protein export pathway. Red nodes represent upregulated differentially expressed genes.

Specific differentially expressed genes of recurrent VTE.

Probe ID Mean of recurrent VTE (R) Mean of healthy (N) Log2 (R/N) P-value Gene symbol ENTREZGENE_ID
201304_at 6.22 4.64 1.58 2.55×10−5 NDUFA5 4698
200679_x_at 9.86 8.67 1.19 4.85×10−10 HMGB1 3146
201317_s_at 9.49 8.16 1.33 2.18×10−7 PSMA2 5683
200977_s_at 9.41 8.16 1.24 1.39×10−5 TAX1BP1 8887
201152_s_at 11.86 10.69 1.17 1.17×10−6 MBNL1 4154
201163_s_at 8.79 7.54 1.24 1.19×10−12 IGFBP7 3490
201593_s_at 9.38 8.26 1.12 3.27×10−10 ZC3H15 55854
201401_s_at 8.57 9.69 −1.13 1.39×10−11 ADRBK1 156
200834_s_at 12.54 11.54 1.01 1.97×10−11 RPS21 6227
201367_s_at 8.91 10.14 −1.24 2.12×10−7 ZFP36L2 678
201392_s_at 9.09 10.22 −1.13 5.39×10−10 IGF2R 3482
201200_at 10.98 9.82 1.16 1.72×10−11 CREG1 8804
200012_x_at 13.08 11.66 1.42 1.40×10−6 RPL21 6144
200061_s_at 11.34 9.94 1.40 4.02×10−9 RPS24 6229
200741_s_at 13.56 12.34 1.22 1.28×10−11 RPS27 6232
200880_at 8.92 7.73 1.19 1.17×10−7 DNAJA1 3301
201012_at 10.84 9.29 1.55 2.33×10−9 ANXA1 301
201273_s_at 10.76 9.69 1.07 0.000332 SRP9 6726
201332_s_at 8.22 9.35 −1.13 1.44×10−13 STAT6 6778
201406_at 10.35 9.03 1.32 6.59×10−5 RPL36A 6173
201023_at 9.93 8.76 1.17 0.000185 TAF7 6879
200818_at 10.17 9.16 1.02 5.51×10−5 ATP5O 539

VTE, venous thromboembolism; R, recurrent; N, normal.

GO functional enrichment analysis of DEGs in patients with RVTE and SVTE, and top 10 specific DEGs of RVTE associated with biological processes.

Category/GOBPID P-value Count (n) Size (n) Term
RVTE
 GO:0006414 9.65×10−27 17 104 Translational elongation
 GO:0044267 1.12×10−8 22 2361 Cellular protein metabolic process
 GO:0010467 1.36×10−6 25 3592 Gene expression
 GO:0043284 2.88×10−6 23 3183 Biopolymer biosynthetic process
 GO:0034645 8.78×10−6 23 3386 Cellular macromolecule biosynthetic process
 GO:0044237 1.49×10−5 34 7160 Cellular metabolic process
 GO:0009058 0.000114 24 4223 Biosynthetic process
 GO:0042274 0.000360 2 10 Ribosomal small subunit biogenesis
 GO:0044238 0.001536 31 7368 Primary metabolic process
 GO:0006364 0.002059 3 88 rRNA processing
SVTE
 GO:0006414 7.47×10−22 12 106 Translational elongation
 GO:0044267 1.24×10−7 14 2499 Cellular protein metabolic process
 GO:0043284 2.35×10−5 13 3183 Biopolymer biosynthetic process
 GO:0034645 4.76×10−5 13 3386 Cellular macromolecule biosynthetic process
 GO:0010467 9.42×10−5 11 3052 Gene expression
 GO:0044237 0.000402 17 7160 Cellular metabolic process
 GO:0009058 0.000549 13 4223 Biosynthetic process
 GO:0006610 0.004003 1 3 Ribosomal protein import into nucleus
 GO:0006364 0.006046 2 88 rRNA processing
 GO:0042273 0.010641 1 8 Ribosomal large subunit biogenesis
Non-overlap
 GO:0006414 3.52×10−7 5 104 Translational elongation
 GO:0000296 0.001547 1 1 Spermine transport
 GO:0003108 0.001547 1 1 Negative regulation of the force of heart contraction by chemical signal
 GO:0008283 0.002157 6 917 Cell proliferation
 GO:0044267 0.002941 9 2361 Cellular protein metabolic process
 GO:0006916 0.002998 3 190 Anti-apoptosis
 GO:0048523 0.003033 7 1334 Negative regulation of cellular process
 GO:0045988 0.003092 1 2 Negative regulation of striated muscle contraction
 GO:0045900 0.003092 1 2 Negative regulation of translational elongation
 GO:0048295 0.003092 1 2 Positive regulation of isotype switching to IgE isotypes

GO, gene ontology; DEG, differentially expressed genes; RVTE, recurrent venous thromboembolism; SVTE, single venous thromboembolism; non-overlap, non-overlapping DEGs as specific DEGs of RVTE; GOBPID, gene ontology biological process identification number; IgE, immunoglobulin E; Count, DEG numbers enriched in the specific GO term; Size, total gene numbers in the GO database.

GO functional enrichment analysis of DEGs in patients with RVTE and SVTE, and top 10 specific DEGs of RVTE associated with cellular components.

Category/GOCCID P-value Count (n) Size (n) Term
RVTE
 GO:0033279 2.03×10−18 13 118 Ribosomal sub-unit
 GO:0005829 2.43×10−12 19 1039 Cytosol
 GO:0032991 8.61×10−11 25 2482 Macromolecular complex
 GO:0022627 4.11×10−10 6 36 Cytosolic small ribosomal sub-unit
 GO:0022625 5.80×10−10 6 38 Cytosolic large ribosomal sub-unit
 GO:0043228 2.18×10−9 23 2388 Non-membrane-bound organelle
 GO:0005737 1.12×10−7 35 6946 Cytoplasm
 GO:0005840 1.16×10−5 4 78 Ribosome
 GO:0005622 5.89×10−5 39 10646 Intracellular
 GO:0005730 0.001773 7 680 Nucleolus
SVTE
 GO:0005829 1.68×10−11 13 1039 Cytosol
 GO:0022625 8.62×10−10 5 38 Cytosolic large ribosomal sub-unit
 GO:0032991 4.86×10−9 15 2482 Macromolecular complex
 GO:0043228 4.10×10−8 14 2388 Non-membrane-bound organelle
 GO:0044422 2.04×10−6 15 3829 Organelle part
 GO:0022627 1.16×10−5 3 36 Cytosolic small ribosomal sub-unit
 GO:0033279 1.53×10−5 3 57 Ribosomal sub-unit
 GO:0005840 1.82×10−5 3 78 Ribosome
 GO:0043229 7.82×10−5 19 8645 Intracellular organelle
 GO:0005622 0.000279 20 10646 Intracellular
Non-overlap
 GO:0022627 1.56×10−5 3 36 Cytosolic small ribosomal sub-unit
 GO:0033279 1.84×10−5 4 118 Ribosomal sub-unit
 GO:0032991 0.000837 10 2482 Macromolecular complex
 GO:0005737 0.001286 17 6946 Cytoplasm
 GO:0008024 0.001373 1 1 Positive transcription elongation factor complex b
 GO:0005829 0.002221 6 1039 Cytosol
 GO:0005785 0.002744 1 2 Signal recognition particle receptor complex
 GO:0043232 0.002811 9 2388 Intracellular non-membrane-bound organelle
 GO:0005641 0.006847 1 5 Nuclear envelope lumen
 GO:0044422 0.006937 11 3829 Organelle part

GO, gene ontology; DEG, differentially expressed genes; RVTE, recurrent venous thromboembolism; SVTE, single venous thromboembolism; Non-overlap, non-overlapping DEGs as specific DEGs of RVTE; GOCCID, gene ontology cellular component identification number; Count, DEG numbers enriched in the specific GO term; Size, total gene numbers in the GO database.

GO functional enrichment analysis of DEGs in patients with RVTE and SVTE, and top 10 specific DEGs of RVTE associated with molecular function.

Category/GOMFID P-value Count (n) Size (n) Term
RVTE
 GO:0003735 9.13×10−24 17 158 Structural constituent of ribosome
 GO:0003723 2.87×10−6 10 646 RNA binding
 GO:0005520 0.002031 2 25 Insulin-like growth factor binding
 GO:0015266 0.002686 1 1 Protein channel activity
 GO:0015077 0.002942 3 107 Monovalent inorganic cation transmembrane transporter activity
 GO:0047696 0.005365 1 2 Beta-adrenergic receptor kinase activity
 GO:0005010 0.008037 1 3 Insulin-like growth factor receptor activity
 GO:0019834 0.008037 1 3 Phospholipase A2 inhibitor activity
 GO:0003729 0.008574 2 52 mRNA binding
 GO:0005047 0.010702 1 4 Signal recognition particle binding
SVTE
 GO:0003735 4.69×10−20 12 158 Structural constituent of ribosome
 GO:0003723 2.79×10−5 6 630 RNA binding
 GO:0015266 0.001245 1 1 Protein channel activity
 GO:0003729 0.001876 2 52 mRNA binding
 GO:0015077 0.007702 2 107 Monovalent inorganic cation transmembrane transporter activity
 GO:0008121 0.009917 1 8 Ubiquinol-cytochrome C reductase activity
 GO:0016679 0.009917 1 8 Oxidoreductase activity, acting on diphenols and associated substances as donors
 GO:0030552 0.017294 1 14 cAMP binding
 GO:0005080 0.018518 1 15 Protein kinase C binding
 GO:0008603 0.019741 1 16 cAMP-dependent protein kinase regulator activity
Non-overlap
 GO:0003735 2.55×10−6 5 158 Structural constituent of ribosome
 GO:0005520 0.000583 2 25 Insulin-like growth factor binding
 GO:0047696 0.002881 1 2 Beta-adrenergic receptor kinase activity
 GO:0005010 0.004318 1 3 Insulin-like growth factor receptor activity
 GO:0019834 0.004318 1 3 Phospholipase A2 inhibitor activity
 GO:0005047 0.005753 1 4 Signal recognition particle binding
 GO:0035035 0.008618 1 6 Histone acetyltransferase binding
 GO:0008312 0.008618 1 6 7S RNA binding
 GO:0004703 0.010048 1 7 G-protein coupled receptor kinase activity
 GO:0031369 0.011475 1 8 Translation initiation factor binding

GO, gene ontology; DEG, differentially expressed genes; RVTE, recurrent venous thromboembolism; SVTE, single venous thromboembolism; Non-overlap, non-overlapping DEGs as specific DEGs of RVTE; cAMP, cyclic adenosine monophosphate; GOMFID, gene ontology molecular function identification number ; Count, DEG numbers enriched in the specific GO term; Size, total gene numbers in the GO database.

KEGG pathway enrichment analysis of DEGs in patients with RVTE and SVTE, and specific DEGs of RVTE.

Category/KEGGID P-value Count (n) Size (n) Term
RVTE
 K03010 1.59×10−23 17 86 Ribosome
 K05012 0.007377 4 133 Parkinson's disease
 K00190 0.007772 4 135 Oxidative phosphorylation
 K05010 0.017421 4 171 Alzheimer's disease
 K05016 0.022594 4 185 Huntington's disease
SVTE
 K03010 4.58×10−19 12 86 Ribosome
 K04260 0.025106 2 79 Cardiac muscle contraction
Non-overlap
 K03010 2.25×10−6 5 86 Ribosome
 K03060 0.03007 1 11 Protein export

KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, differentially expressed genes; RVTE, recurrent venous thromboembolism; SVTE, single venous thromboembolism; Non-overlap, non-overlapping DEGs as specific DEGs of RVTE. KEGGID, KEGG identification number. Count, number of DEGs enriched in the specific GO term; size, number of genes in the GO database.