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pmid:34313877

Study on potential differentially expressed genes in stroke by bioinformatics analysis.

Xitong Yang · Pengyu Wang · Shanquan Yan · Guangming Wang

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022 doi:10.1007/s10072-021-05470-1

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complete

Experiment

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Data

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:34313877
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science-record
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sha256:b515ea121a755b9eb2524710931c2f5adb79926a732f8259b81a87d554e3deb4
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T15:41:45Z
updated_at
2026-11-01T22:04:19Z
generator object · 2 fields
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paper object · 10 fields
state
complete
title
Study on potential differentially expressed genes in stroke by bioinformatics analysis.
doi
10.1007/s10072-021-05470-1
pmid
34313877
authors array · 4 items
[0]
Xitong Yang
[1]
Pengyu Wang
[2]
Shanquan Yan
[3]
Guangming Wang
journal
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
year
2022
abstract
Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.
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GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was applied to perform DEGs analysis between serum samples from IS and control groups, and corrected P-value calculations to obtain |log2FC|.
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null
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sections[6] "Differentially expressed Gene Identification" paragraph 2
quote
GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was applied to perform DEGs analysis between serum samples from IS and control groups, and corrected P-value calculations to obtain |log2FC|.
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The data used in this study were downloaded from the GEO database. The data used to support the findings of this study are available from corresponding websites upon request.
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sections[20] "notes" paragraph 1
quote
The data used in this study were downloaded from the GEO database. The data used to support the findings of this study are available from corresponding websites upon request.
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Gene expression analysis of peripheral whole blood RNA following ischemic stroke
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The data used in this study were downloaded from the GEO database. The data used to support the findings of this study are available from corresponding websites upon request.
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Gene Expression Following Cardioembolic Stroke
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Details for GEO IS data
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Table 1
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Details for GEO IS data
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[1]
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[3]
24/39
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[3]
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GO functional enrichment analysis and KEGG enrichment pathway
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Table 2
caption
GO biological process terms for DEGs between the control and IS groups
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[0] array · 7 items
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GO molecular function terms for DEGs between the control and IS groups
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12
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GO functional enrichment analysis and KEGG enrichment pathway
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Table 3
caption
GO molecular function terms for DEGs between the control and IS groups
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[0] array · 7 items
[0]
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[2]
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[3]
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[6]
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[0]
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[1]
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[3]
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[4]
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[5]
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[5]
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[1]
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[5]
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[6]
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[5]
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[5]
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VASP, EVL
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[5]
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HMGB2, S100A12
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[3]
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[5]
2.352941176
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GO cellular component terms for DEGs between the control and IS group
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PMC8789718
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12
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GO functional enrichment analysis and KEGG enrichment pathway
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11
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Table 4
caption
GO cellular component terms for DEGs between the control and IS group
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GOTERM_CC
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Term
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P value
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1
[1]
GO:0,005,886plasma membrane
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5.44E-05
[3]
1.895240406
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36
[5]
42.35294118
[6]
SLC22A4, SNAP23, DYSF, LY96, IRS2, ITPR3, IQGAP1, CD79B, CD79A, CD19, LAMP2, CA4, S100A12, CCR7, ABCA1, VASP, SVIL, MGAM, CD163, PLEKHA1, MME, ACSL1, ANXA3, IL1R2, KCNJ15, CKAP4, MARCKS, CLEC4D, ACOX1, VNN3, CPD, IL2RB, TNFRSF25, CEACAM8, DSC2, SKAP2
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GO:0,005,737cytoplasm
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1.287921067
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31
[5]
36.47058824
[6]
SNAP23, LEF1, HMGB2, NEDD9, ITPR3, ABHD5, IQGAP1, CORO1C, CD79B, CD79A, REPS2, S100A12, VASP, SVIL, PLEKHA1, MME, ANXA3, ARG1, IL1R2, EIF2AK2, IRAK3, PASK, RBP7, PRKAR1A, TAOK1, DPYD, ID3, EVL, PADI4, GRAP, SKAP2
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[0]
3
[1]
GO:0,070,062extracellular exosome
[2]
5.30E-04
[3]
2.006674459
[4]
26
[5]
30.58823529
[6]
ORM1, SNAP23, DYSF, IQGAP1, CD79B, CD19, LAMP2, CA4, TNFSF10, VASP, MGAM, PLEKHA1, MME, ANXA3, ARG1, PLXDC2, MMP9, CKAP4, RAB33B, MARCKS, CRISPLD2, TAOK1, CPD, CEACAM8, MFGE8, DSC2
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[1]
GO:0,016,020membrane
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21
[5]
24.70588235
[6]
CD163, ACSL1, ANXA3, DOCK8, KCNJ15, EIF2AK2, FLT3LG, ITPR3, CKAP4, HK2, TPST1, CD6, PRKAR1A, ACOX1, CPD, IL2RB, LAMP2, CA4, EVL, FOLR3, MFGE8
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5
[1]
GO:0,005,887integral component of plasma membrane
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[4]
14
[5]
16.47058824
[6]
ABCA1, SLC22A4, CD163, MME, KCNJ15, ITPR3, CD79B, CD79A, CD6, CD19, IL2RB, TNFSF10, TNFRSF25, CEACAM8
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6
[1]
GO:0,005,576extracellular region
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0.029681411
[3]
1.886542443
[4]
14
[5]
16.47058824
[6]
ORM1, CD163, DEFA4, IL1R2, FLT3LG, MMP9, NELL2, CD6, CRISPLD2, TNFSF10, S100A12, TNFRSF25, FOLR3, MFGE8
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7
[1]
GO:0,005,615extracellular space
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0.018583106
[3]
2.093824018
[4]
13
[5]
15.29411765
[6]
ORM1, ARG1, DEFA4, HMGB2, FLT3LG, LY96, MMP9, VNN3, CPD, LAMP2, TNFSF10, CEACAM8, MFGE8
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[0]
8
[1]
GO:0,005,925focal adhesion
[2]
4.05E-04
[3]
4.99378882
[4]
9
[5]
10.58823529
[6]
VASP, SVIL, MARCKS, MME, SNAP23, NEDD9, EVL, IQGAP1, CORO1C
[9] array · 7 items
[0]
9
[1]
GO:0,009,897external side of plasma membrane
[2]
4.24E-04
[3]
7.129890454
[4]
7
[5]
8.235294118
[6]
ABCA1, CD79B, CD79A, CD19, IL2RB, CCR7, MFGE8
[10] array · 7 items
[0]
10
[1]
GO:0,030,027lamellipodium
[2]
0.006143732
[3]
6.779761905
[4]
5
[5]
5.882352941
[6]
VASP, DYSF, NEDD9, EVL, CORO1C
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section[12]/table-wrap[3]#Tab4
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KEGG pathway enrichment terms for DEGs between the control and IS group
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PMC8789718
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12
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GO functional enrichment analysis and KEGG enrichment pathway
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Table 5
caption
KEGG pathway enrichment terms for DEGs between the control and IS group
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IL1R2, IL2RB, TNFSF10, CCR7, TNFRSF25
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3.529411765
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GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was applied to perform DEGs analysis between serum samples from IS and control groups, and corrected P-value calculations to obtain |log2FC|.
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Automated Assays Laravel application and simulated adapters
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The data used in this study were downloaded from the GEO database. The data used to support the findings of this study are available from corresponding websites upon request.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34313877", "kind": "science-record", "revision": 5, "content_hash": "sha256:b515ea121a755b9eb2524710931c2f5adb79926a732f8259b81a87d554e3deb4", "created_at": "2026-11-01T15:41:45Z", "updated_at": "2026-11-01T22:04:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Study on potential differentially expressed genes in stroke by bioinformatics analysis.", "doi": "10.1007/s10072-021-05470-1", "pmid": "34313877", "authors": ["Xitong Yang", "Pengyu Wang", "Shanquan Yan", "Guangming Wang"], "journal": "Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology", "year": 2022, "abstract": "Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.", "claims": [{"id": "claim-method", "statement": "GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was applied to perform DEGs analysis between serum samples from IS and control groups, and corrected P-value calculations to obtain |log2FC|.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34313877", "locator": "sections[6] \"Differentially expressed Gene Identification\" paragraph 2", "quote": "GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was applied to perform DEGs analysis between serum samples from IS and control groups, and corrected P-value calculations to obtain |log2FC|."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study were downloaded from the GEO database. 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[["GOTERM_MF", "Term", "PValue", "Fold enrichment", "Count", "GeneRatio", "Genes"], ["1", "GO:0,005,509calcium ion binding", "0.018741709", "2.648692469", "9", "10.58823529", "NELL2, ANXA3, REPS2, DYSF, S100A12, ITPR3, PADI4, IQGAP1, DSC2"], ["2", "GO:0,019,904protein domain-specific binding", "0.016406406", "5.072415865", "5", "5.882352941", "LAMP2, HMGB2, ID3, IRS2, IQGAP1"], ["3", "GO:0,016,301kinase activity", "0.026474471", "4.377852697", "5", "5.882352941", "NELL2, PRKAR1A, TAOK1, PDK4, FLT3LG"], ["4", "GO:0,005,102receptor binding", "0.083617609", "2.988845609", "5", "5.882352941", "ABCA1, ACOX1, MATK, TNFSF10, FLT3LG"], ["5", "GO:0,003,824catalytic activity", "0.058139482", "4.48962766", "4", "4.705882353", "HAL, MGAM, ACSL1, ECHDC3"], ["6", "GO:0,003,714transcription corepressor activity", "0.069792981", "4.157881773", "4", "4.705882353", "NFIL3, ID3, SAP30, AES"], ["7", "GO:0,030,165PDZ domain binding", "0.061420984", "7.360901163", "3", "3.529411765", "SLC22A4, PLEKHA1, 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"section_title": "GO functional enrichment analysis and KEGG enrichment pathway", "row_count": 11, "column_count": 7}, "table": {"label": "Table 4", "caption": "GO cellular component terms for DEGs between the control and IS group", "rows": [["GOTERM_CC", "Term", "P value", "Fold enrichment", "Count", "GeneRatio", "Genes"], ["1", "GO:0,005,886plasma membrane", "5.44E-05", "1.895240406", "36", "42.35294118", "SLC22A4, SNAP23, DYSF, LY96, IRS2, ITPR3, IQGAP1, CD79B, CD79A, CD19, LAMP2, CA4, S100A12, CCR7, ABCA1, VASP, SVIL, MGAM, CD163, PLEKHA1, MME, ACSL1, ANXA3, IL1R2, KCNJ15, CKAP4, MARCKS, CLEC4D, ACOX1, VNN3, CPD, IL2RB, TNFRSF25, CEACAM8, DSC2, SKAP2"], ["2", "GO:0,005,737cytoplasm", "0.08417535", "1.287921067", "31", "36.47058824", "SNAP23, LEF1, HMGB2, NEDD9, ITPR3, ABHD5, IQGAP1, CORO1C, CD79B, CD79A, REPS2, S100A12, VASP, SVIL, PLEKHA1, MME, ANXA3, ARG1, IL1R2, EIF2AK2, IRAK3, PASK, RBP7, PRKAR1A, TAOK1, DPYD, ID3, EVL, PADI4, GRAP, SKAP2"], ["3", "GO:0,070,062extracellular 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HMGB2, FLT3LG, LY96, MMP9, VNN3, CPD, LAMP2, TNFSF10, CEACAM8, MFGE8"], ["8", "GO:0,005,925focal adhesion", "4.05E-04", "4.99378882", "9", "10.58823529", "VASP, SVIL, MARCKS, MME, SNAP23, NEDD9, EVL, IQGAP1, CORO1C"], ["9", "GO:0,009,897external side of plasma membrane", "4.24E-04", "7.129890454", "7", "8.235294118", "ABCA1, CD79B, CD79A, CD19, IL2RB, CCR7, MFGE8"], ["10", "GO:0,030,027lamellipodium", "0.006143732", "6.779761905", "5", "5.882352941", "VASP, DYSF, NEDD9, EVL, CORO1C"]], "row_count": 11, "column_count": 7, "numeric_cells": 69, "header_rows": 1, "locator": "section[12]/table-wrap[3]#Tab4"}}, {"id": "asset:table:pmid:34313877:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:3ff0ba6d98ecb18dd84f0f793442b62105602ccbe8c9aec2c9786e6a253e31ee", "bytes": 673, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[4]#Tab5", 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