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pmid:34179317

The transcriptome of circulating cells indicates potential biomarkers and therapeutic targets in the course of hypertension-related myocardial infarction.

Zilun Wei · Yining Yang · Qiaoling Li · Yong Yin · Zhonghai Wei · Wenfeng Zhang · Dan Mu · Jie Ni · Xuan Sun · Biao Xu

Genes & diseases · 2021 doi:10.1016/j.gendis.2020.01.007

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complete

Experiment

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Data

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Analysis

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Execution

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paper object · 10 fields
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title
The transcriptome of circulating cells indicates potential biomarkers and therapeutic targets in the course of hypertension-related myocardial infarction.
doi
10.1016/j.gendis.2020.01.007
pmid
34179317
authors array · 10 items
[0]
Zilun Wei
[1]
Yining Yang
[2]
Qiaoling Li
[3]
Yong Yin
[4]
Zhonghai Wei
[5]
Wenfeng Zhang
[6]
Dan Mu
[7]
Jie Ni
[8]
Xuan Sun
[9]
Biao Xu
journal
Genes & diseases
year
2021
abstract
Hypertension (HT) is the most common public-health challenge and shows a high incidence around the world. Cardiovascular diseases are the leading cause of mortality and morbidity among the elderly (age > 65 years) in the United States. Now, there is widespread acceptance of the causal link between HT and acute myocardial infarction (MI). This is the first data-mining study to identify co-expressed differentially expressed genes (co-DEGs) between HT and MI (relative to normal control) and to uncover potential biomarkers and therapeutic targets of HT-related MI. In this manuscript, HT-specific DEGs and MI-specific DEGs and differentially expressed microRNAs (DE-miRNAs) were identified in Gene Expression Omnibus (GEO) datasets GSE24752, GSE60993, GSE62646, and GSE24548 after data consolidation and batch correction. Subsequently, enrichment in Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways as well as protein-protein interaction networks were identified, and single-gene gene set enrichment analysis was performed to determine the affected biological categories and networks. Cross-matching of the results on co-DE-miRNAs and predicted miRNAs targeting the co-DEGs was conducted and discussed as well. We found that MYC and HIST1H2BO may be associated with HT, whereas FCGR1A, FYN, KLRD1, KLRB1, and FOLR3 may be implicated in MI. Moreover, co-DEGs FOLR3 and NFE2 with predicted miRNAs and DE-miRNAs, especially miR-7 and miR-548, may be significantly associated and show huge potential as a new set of novel biomarkers and important molecular targets in the course of HT-related MI.
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Original P values were adjusted by the Benjamini–Hochberg method.
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The microarray datasets GSE60993, GSE62646, GSE24752, and GSE24548 can be obtained from the NCBI-GEO online database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).
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microRNA expression profiling of patients affected by first acute myocardial infarction (FAMI)
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Microarray Analysis of differential gene expression in peripheral blood cells of patients with human essential hypertension
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Assessment and Diagnostic Relevance of Novel Serum Biomarkers for Early Decision of ST-elevation Myocardial Infarction
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Altered gene expression pattern in peripheral blood mononuclear cells in patients with acute myocardial infarction
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Cardiovascular disease portal and gene ontology annotation overview of human species and human synteny.
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Biological-category enrichment and disease-related prediction scores for the co-DEGs
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Table 1
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Cardiovascular disease portal and gene ontology annotation overview of human species and human synteny.
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[0]
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[1]
Category
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[1] array · 4 items
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[1]
GO (Cellular Component)
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Cell
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[1]
693
[3] array · 2 items
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Venn diagrams of DEGs of hypertension patients (HT-DEGs) and myocardial infarction patients (MI-DEGs).
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Biological-category enrichment and disease-related prediction scores for the co-DEGs
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Table 2
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Venn diagrams of DEGs of hypertension patients (HT-DEGs) and myocardial infarction patients (MI-DEGs).
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STAB1, FLOT2, KCTD12, DOK3, CYP27A1, NCF4, S100A12, ASGR2, TRIB1, ZFP36, LILRA2, GZMH, CTSD, TGFBR3, CEBPB, KLRC3, AQP9, RNASE2, LOC441081, FAM20A, ACSL1, SOCS3, SH2D1A, HIST1H4C, TCN2, CDA, CSF3R, SAMD3, ZNF467, FAM101B, LRG1, SLC11A1, ADM, TARP, SDAD1, SIGLEC9, LILRA5, FCGR1A, MCOLN2, CLC, LILRA3, TBC1D2, KLRD1, ANPEP, SPI1, GZMA, PADI4, CYP1B1, EGR1, P2RX1, DYSF, MCEMP1, GZMK, C1orf21, BCL3, MLF2, CR1, KLRB1, KLRC2, MS4A3, KLRG1, WAS, KLRF1, HP, HSPA1A
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[2]
ZDHHC14, NKAPP1, ACIN1, PVRIG, LINC00473, FAM186B, LOC102724275, C4BPA, SLC4A1, LOC101928893, ZNF891, COQ3, PSMB6, TMEM43, PLA1A, C3AR1, LINC00689, HLA-DPB1, MLH3, HOTAIRM1, E2F5, IL36A, LOC100506922, RP11-395I6.3, RP11-466A19.8, OR2L13, HIP1, DTHD1, LMTK3, RP11-554J4.1, KCNV1, ACP1, HLA-DPA1, XK, LINC00403, HIST1H2BO, MYC, ANKS4B, C10orf126, RHOH, VPREB3, LDHB, SPEF1, WFDC6, NOG, GGT6, RBMS3-AS3, ND6, OTUB2, HHEX, LOC285556, LOC643085, ERICH4, CSN1S2AP, PLEKHF2, PRDX6, FAM32A, ATF1, HERC2, SLC6A14, POU6F2-AS2, FAM170A, RP11-184E9.2, TMEM55A, RP6-99M1.2, AKNAD1, FUCA1, SOX14, CELF2-AS1, TMIE, CFD, RP11-843B15.2, C17orf98, TRAC, LINC00551, LOC100507537, CTC-510F12.4, PCTP, AX747191, IRF8, VWA5A, CTBP1-AS, LOC100506563, TCL1A, RP11-568N6.1, ZNF385B, CHRM3-AS2, HIST1H2AE, MAGEB6, SKAP2
[3] array · 3 items
[0]
Overlapped DEGs
[1]
2
[2]
NFE2, FOLR3
locator
section[5]/table-wrap[2]#tbl2
[7] object · 16 fields
id
asset:table:pmid:34179317:3
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:a767ab521d1007f4a8192be5e35227eaffd3f6f961cf11b4cced4641d5c8798c
bytes
1508
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[5]/table-wrap[3]#tbl3
version
null
note
GSEA enrichment results for FOLR3 and NEF2C genes with original data obtained from GSE60993 和 GSE62646.
structure object · 5 fields
pmcid
PMC8209311
section_index
5
section_title
Biological-category enrichment and disease-related prediction scores for the co-DEGs
row_count
18
column_count
6
table object · 8 fields
label
Table 3
caption
GSEA enrichment results for FOLR3 and NEF2C genes with original data obtained from GSE60993 和 GSE62646.
rows array · 18 items
[0] array · 6 items
[0]
Gene
[1]
Regulation
[2]
Term
[3]
ES
[4]
NES
[5]
P value
[1] array · 6 items
[0]
FOLR3
[1]
Up
[2]
KEGG_REGULATION_OF_AUTOPHAGY
[3]
0.5199144
[4]
1.668857
[5]
0.008180
[2] array · 5 items
[0]
Down
[1]
KEGG_PYRUVATE_METABOLISM
[2]
−0.58483
[3]
−1.67831
[4]
0.010395
[3] array · 4 items
[0]
KEGG_BUTANOATE_METABOLISM
[1]
−0.57011
[2]
−1.67309
[3]
0.010309
[4] array · 4 items
[0]
KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM
[1]
−0.50376
[2]
−1.66442
[3]
0.002066
[5] array · 4 items
[0]
KEGG_GLYCOLYSIS_GLUCONEOGENESIS
[1]
−0.4773
[2]
−1.65101
[3]
0.017647
[6] array · 4 items
[0]
KEGG_PRIMARY_IMMUNODEFICIENCY
[1]
−0.73625
[2]
−1.59229
[3]
0.012605
[7] array · 4 items
[0]
KEGG_THYROID_CANCER
[1]
−0.49717
[2]
−1.5756
[3]
0.047325
[8] array · 4 items
[0]
KEGG_CITRATE_CYCLE_TCA_CYCLE
[1]
−0.5862
[2]
−1.54846
[3]
0.069106
[9] array · 6 items
[0]
NFE2C
[1]
Up
[2]
KEGG_REGULATION_OF_AUTOPHAGY
[3]
0.519914
[4]
1.695634
[5]
0.012000
[10] array · 4 items
[0]
KEGG_PYRUVATE_METABOLISM
[1]
−0.58483
[2]
−1.75248
[3]
0.012121
[11] array · 4 items
[0]
KEGG_BUTANOATE_METABOLISM
[1]
−0.57011
[2]
−1.72362
[3]
0.010204
[12] array · 4 items
[0]
KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM
[1]
−0.50376
[2]
−1.70064
[3]
0.009921
[13] array · 4 items
[0]
KEGG_PRIMARY_IMMUNODEFICIENCY
[1]
−0.73625
[2]
−1.64576
[3]
0.005769
[14] array · 4 items
[0]
KEGG_GLYCOLYSIS_GLUCONEOGENESIS
[1]
−0.4773
[2]
−1.61349
[3]
0.009843
[15] array · 4 items
[0]
KEGG_CITRATE_CYCLE_TCA_CYCLE
[1]
−0.5862
[2]
−1.58633
[3]
0.062124
[16] array · 4 items
[0]
KEGG_THYROID_CANCER
[1]
−0.49717
[2]
−1.56898
[3]
0.039293
[17] array · 4 items
[0]
KEGG_WNT_SIGNALING_PATHWAY
[1]
[2]
[3]
0.062500
locator
section[5]/table-wrap[3]#tbl3
[8] object · 16 fields
id
asset:table:pmid:34179317:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:b0f13f0c86d771314ae579c78a60cdd51f022347c5becaffabde0c71e66ce29f
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1736
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open
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verified
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read_table
reader_status
available
identifier
section[6]/table-wrap[1]#tbl4
version
null
note
The Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment among predicted miRNAs and Co-DEGs.
structure object · 5 fields
pmcid
PMC8209311
section_index
6
section_title
Prediction of miRNAs targeting co-DEGs and analysis of the actual expression levels
row_count
21
column_count
7
table object · 8 fields
label
Table 4
caption
The Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment among predicted miRNAs and Co-DEGs.
rows array · 21 items
[0] array · 7 items
[0]
Genes
[1]
Predicted miRNAs
[2]
Category
[3]
Term
[4]
genes
[5]
miRNAs
[6]
P value
[1] array · 7 items
[0]
FOLR3
[1]
hsa-miR-7-5p
[2]
KEGG
[3]
Fatty acid biosynthesis
[4]
4
[5]
2
[6]
2.42e-19
[2] array · 6 items
[0]
hsa-miR-548-5p
[1]
[2]
Hippo signaling pathway
[3]
46
[4]
5
[5]
6.31e-06
[3] array · 6 items
[0]
hsa-miR-6077
[1]
[2]
Adherens junction
[3]
29
[4]
5
[5]
1.92e-04
[4] array · 6 items
[0]
hsa-miR-876-5p
[1]
[2]
Estrogen signaling pathway
[3]
21
[4]
5
[5]
2.66e-03
[5] array · 6 items
[0]
hsa-miR-1290
[1]
[2]
Protein processing in endoplasmic reticulum
[3]
18
[4]
5
[5]
1.77 e-03
[6] array · 6 items
[0]
[1]
GO
[2]
Cellular nitrogen compound metabolic process
[3]
880
[4]
5
[5]
1.58e-173
[7] array · 6 items
[0]
[1]
[2]
Ion binding
[3]
994
[4]
5
[5]
2.62e-88
[8] array · 6 items
[0]
[1]
[2]
Response to stress
[3]
396
[4]
5
[5]
3.33e-23
[9] array · 6 items
[0]
[1]
[2]
Epidermal growth factor receptor signaling pathway
[3]
57
[4]
5
[5]
1.97e-14
[10] array · 6 items
[0]
[1]
[2]
Fibroblast growth factor receptor signaling pathway
[3]
52
[4]
5
[5]
5.82e-12
[11] array · 7 items
[0]
NFE2
[1]
hsa-miR-146b-5p
[2]
KEGG
[3]
Cocaine addiction
[4]
14
[5]
4
[6]
5.22e-04
[12] array · 6 items
[0]
hsa-miR-2115-3p
[1]
[2]
Thyroid hormone synthesis
[3]
16
[4]
3
[5]
1.40e-03
[13] array · 6 items
[0]
hsa-miR-518f-5p
[1]
[2]
ErbB signaling pathway
[3]
25
[4]
4
[5]
1.86e-03
[14] array · 6 items
[0]
hsa-miR-7515
[1]
[2]
Circadian rhythm
[3]
11
[4]
3
[5]
0.029
[15] array · 6 items
[0]
hsa-miR-329-3p
[1]
[2]
Vasopressin-regulated water reabsorption
[3]
15
[4]
4
[5]
0.043
[16] array · 6 items
[0]
[1]
GO
[2]
Cellular nitrogen compound metabolic process
[3]
648
[4]
5
[5]
5.19e-83
[17] array · 6 items
[0]
[1]
[2]
Ion binding
[3]
770
[4]
5
[5]
4.70e-48
[18] array · 6 items
[0]
[1]
[2]
Response to stress
[3]
297
[4]
5
[5]
1.12e-12
[19] array · 6 items
[0]
[1]
[2]
Protein binding transcription factor activity
[3]
79
[4]
5
[5]
7.14e-10
[20] array · 6 items
[0]
[1]
[2]
Epidermal growth factor receptor signaling pathway
[3]
42
[4]
5
[5]
6.71e-09
locator
section[6]/table-wrap[1]#tbl4
gaps array · 8 items
[0]
asset:data:geo:GSE24548 not downloaded
[1]
asset:data:geo:GSE24548 licence not read
[2]
asset:data:geo:GSE24752 not downloaded
[3]
asset:data:geo:GSE24752 licence not read
[4]
asset:data:geo:GSE60993 not downloaded
[5]
asset:data:geo:GSE60993 licence not read
[6]
asset:data:geo:GSE62646 not downloaded
[7]
asset:data:geo:GSE62646 licence not read
analysis object · 4 fields
state
partial
methods array · 3 items
[0] object · 10 fields
id
method-1
family
clustering
specific
hierarchical
label
Clustering
dsl_operations array · 1 item
[0]
hierarchical_cluster
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
feature_matrix
evidence object · 3 fields
score
0.8
band
high
quote
hierarchical clustering
[1] object · 10 fields
id
method-2
family
network_analysis
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ppi_network
label
Network / pathway analysis
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[0]
build_graph
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centrality_scores
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pathway_enrich
missing_operations array · 0 items
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edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
protein-protein interaction
[2] object · 10 fields
id
method-3
family
null
specific
null
label
Benjamini–Hochberg method
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missing_operations array · 0 items
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evidence object · 3 fields
score
1
band
verbatim
quote
Original P values were adjusted by the Benjamini–Hochberg method.
targets array · 0 items
gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
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survival_analysis
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statistical_regression
[4]
pca
[5]
molecular_docking
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network_analysis
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differential_expression
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gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
hierarchical_cluster
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: hierarchical.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE24752
[1]
GSE60993
[2]
GSE62646
[3]
GSE24548
resolved_accessions array · 4 items
[0]
GSE24752
[1]
GSE60993
[2]
GSE62646
[3]
GSE24548
statements array · 1 item
[0]
The microarray datasets GSE60993, GSE62646, GSE24752, and GSE24548 can be obtained from the NCBI-GEO online database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).
basis
The paper names GSE24752, GSE60993, GSE62646, GSE24548 in NCBI Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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hierarchical_cluster
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source:pmid:34179317
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asset:table:pmid:34179317:1
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asset:table:pmid:34179317:2
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asset:table:pmid:34179317:3
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asset:table:pmid:34179317:4
steps array · 1 item
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run_analysis
must_build object · 5 fields
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
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data:geo:GSE24548
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review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
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minimax
provider_family
minimax
read_at
2026-10-31T18:31:28Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
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3
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33010
completion_tokens
1958
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784
stages array · 1 item
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id
extraction
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complete
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MiniMax-M3.1-Flash
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minimax
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review.independent_reader
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review.machine_critique
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review.human_critique
execution object · 5 fields
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absent
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not_run
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gaps array · 1 item
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execution.not_run
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pmid:34179317
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paper
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pmid:34179317
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source:pmid:34179317
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PMID 34179317 full text
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data:geo:GSE24548
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GEO GSE24548
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GEO GSE24548
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agent:corpus-assay-engine
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source:pmid:34179317
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data:geo:GSE24548
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:minimax-reader
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gaps array · 13 items
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asset:data:geo:GSE24548 licence not read
[1]
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[3]
asset:data:geo:GSE24752 not downloaded
[4]
asset:data:geo:GSE60993 licence not read
[5]
asset:data:geo:GSE60993 not downloaded
[6]
asset:data:geo:GSE62646 licence not read
[7]
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execution.not_run
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[0]
f expression of MI-specific DE-miRNAs. (B-C) Microarray expression levels of miR-7 and miR-538 in GS
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ethods Availability of data and material The microarray datasets GSE60993, GSE62646, GSE24752, and G
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34179317", "kind": "science-record", "revision": 7, "content_hash": "sha256:4876fedb56f1127d6fdd8c58c35fccf937f9cc3dd725bde900c87860afc88a35", "created_at": "2026-11-01T15:41:40Z", "updated_at": "2026-11-01T22:04:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The transcriptome of circulating cells indicates potential biomarkers and therapeutic targets in the course of hypertension-related myocardial infarction.", "doi": "10.1016/j.gendis.2020.01.007", "pmid": "34179317", "authors": ["Zilun Wei", "Yining Yang", "Qiaoling Li", "Yong Yin", "Zhonghai Wei", "Wenfeng Zhang", "Dan Mu", "Jie Ni", "Xuan Sun", "Biao Xu"], "journal": "Genes & diseases", "year": 2021, "abstract": "Hypertension (HT) is the most common public-health challenge and shows a high incidence around the world. Cardiovascular diseases are the leading cause of mortality and morbidity among the elderly (age > 65 years) in the United States. Now, there is widespread acceptance of the causal link between HT and acute myocardial infarction (MI). This is the first data-mining study to identify co-expressed differentially expressed genes (co-DEGs) between HT and MI (relative to normal control) and to uncover potential biomarkers and therapeutic targets of HT-related MI. In this manuscript, HT-specific DEGs and MI-specific DEGs and differentially expressed microRNAs (DE-miRNAs) were identified in Gene Expression Omnibus (GEO) datasets GSE24752, GSE60993, GSE62646, and GSE24548 after data consolidation and batch correction. Subsequently, enrichment in Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways as well as protein-protein interaction networks were identified, and single-gene gene set enrichment analysis was performed to determine the affected biological categories and networks. Cross-matching of the results on co-DE-miRNAs and predicted miRNAs targeting the co-DEGs was conducted and discussed as well. We found that MYC and HIST1H2BO may be associated with HT, whereas FCGR1A, FYN, KLRD1, KLRB1, and FOLR3 may be implicated in MI. Moreover, co-DEGs FOLR3 and NFE2 with predicted miRNAs and DE-miRNAs, especially miR-7 and miR-548, may be significantly associated and show huge potential as a new set of novel biomarkers and important molecular targets in the course of HT-related MI.", "claims": [{"id": "claim-method", "statement": "Original P values were adjusted by the Benjamini–Hochberg method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34179317", "locator": "sections[10] \"Screening of DEGs\" paragraph 2", "quote": "Original P values were adjusted by the Benjamini–Hochberg method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray datasets GSE60993, GSE62646, GSE24752, and GSE24548 can be obtained from the NCBI-GEO online database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34179317", "locator": "sections[14] \"Availability of data and material\" paragraph 2", "quote": "The microarray datasets GSE60993, GSE62646, GSE24752, and GSE24548 can be obtained from the NCBI-GEO online database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34179317"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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