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pmid:38562184

Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.

Dingyuan Tu · Qiang Xu · Yanmin Luan · Jie Sun · Xiaoli Zuo · Chaoqun Ma

Frontiers in cardiovascular medicine · 2024 doi:10.3389/fcvm.2024.1349363

Paper

complete

Experiment

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Data

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Analysis

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Execution

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title
Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.
doi
10.3389/fcvm.2024.1349363
pmid
38562184
authors array · 6 items
[0]
Dingyuan Tu
[1]
Qiang Xu
[2]
Yanmin Luan
[3]
Jie Sun
[4]
Xiaoli Zuo
[5]
Chaoqun Ma
journal
Frontiers in cardiovascular medicine
year
2024
abstract
Backgrounds: Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet. Methods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs. Results: We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity. Conclusions: Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.
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Then expression differences of CRGs between HF and NFDs samples were identified using limma.
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Then expression differences of CRGs between HF and NFDs samples were identified using limma.
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
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Four machine-learning algorithms identified DLAT, SLC31A1, and DLST as three cuprotosis-related diagnostic indicators for heart failure with good diagnostic properties.
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With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties.
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Four machine-learning algorithms identified DLAT, SLC31A1, and DLST as three cuprotosis-related diagnostic indicators for heart failure with good diagnostic properties.
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[83]
GSM1379895
[84]
GSM1379896
[85]
GSM1379897
[86]
GSM1379898
[87]
GSM1379899
[88]
GSM1379900
[89]
GSM1379901
[90]
GSM1379902
[91]
GSM1379903
[92]
GSM1379904
[93]
GSM1379905
[94]
GSM1379906
[95]
GSM1379907
[96]
GSM1379908
[97]
GSM1379909
[98]
GSM1379910
[99]
GSM1379911
[100]
GSM1379912
[101]
GSM1379913
[102]
GSM1379914
[103]
GSM1379915
[104]
GSM1379916
[105]
GSM1379917
[106]
GSM1379918
[107]
GSM1379919
[108]
GSM1379920
[109]
GSM1379921
[110]
GSM1379922
[111]
GSM1379923
[112]
GSM1379924
[113]
GSM1379925
[114]
GSM1379926
[115]
GSM1379927
[116]
GSM1379928
[117]
GSM1379929
[118]
GSM1379930
[119]
GSM1379931
[120]
GSM1379932
[121]
GSM1379933
[122]
GSM1379934
[123]
GSM1379935
[124]
GSM1379936
[125]
GSM1379937
[126]
GSM1379938
[127]
GSM1379939
[128]
GSM1379940
[129]
GSM1379941
[130]
GSM1379942
[131]
GSM1379943
[132]
GSM1379944
[133]
GSM1379945
[134]
GSM1379946
[135]
GSM1379947
[136]
GSM1379948
[137]
GSM1379949
[138]
GSM1379950
[139]
GSM1379951
[140]
GSM1379952
[141]
GSM1379953
[142]
GSM1379954
[143]
GSM1379955
[144]
GSM1379956
[145]
GSM1379957
[146]
GSM1379958
[147]
GSM1379959
[148]
GSM1379960
[149]
GSM1379961
[150]
GSM1379962
[151]
GSM1379963
[152]
GSM1379964
[153]
GSM1379965
[154]
GSM1379966
[155]
GSM1379967
[156]
GSM1379968
[157]
GSM1379969
[158]
GSM1379970
[159]
GSM1379971
[160]
GSM1379972
[161]
GSM1379973
[162]
GSM1379974
[163]
GSM1379975
[164]
GSM1379976
[165]
GSM1379977
[166]
GSM1379978
[167]
GSM1379979
[168]
GSM1379980
[169]
GSM1379981
[170]
GSM1379982
[171]
GSM1379983
[172]
GSM1379984
[173]
GSM1379985
[174]
GSM1379986
[175]
GSM1379987
[176]
GSM1379988
[177]
GSM1379989
[178]
GSM1379990
[179]
GSM1379991
[180]
GSM1379992
[181]
GSM1379993
[182]
GSM1379994
[183]
GSM1379995
[184]
GSM1379996
[185]
GSM1379997
[186]
GSM1379998
[187]
GSM1379999
[188]
GSM1380000
[189]
GSM1380001
[190]
GSM1380002
[191]
GSM1380003
[192]
GSM1380004
[193]
GSM1380005
[194]
GSM1380006
[195]
GSM1380007
[196]
GSM1380008
[197]
GSM1380009
[198]
GSM1380010
[199]
GSM1380011
[200]
GSM1380012
[201]
GSM1380013
[202]
GSM1380014
[203]
GSM1380015
[204]
GSM1380016
[205]
GSM1380017
[206]
GSM1380018
[207]
GSM1380019
[208]
GSM1380020
[209]
GSM1380021
[210]
GSM1380022
[211]
GSM1380023
[212]
GSM1380024
[213]
GSM1380025
[214]
GSM1380026
[215]
GSM1380027
[216]
GSM1380028
[217]
GSM1380029
[218]
GSM1380030
[219]
GSM1380031
[220]
GSM1380032
[221]
GSM1380033
[222]
GSM1380034
[223]
GSM1380035
[224]
GSM1380036
[225]
GSM1380037
[226]
GSM1380038
[227]
GSM1380039
[228]
GSM1380040
[229]
GSM1380041
[230]
GSM1380042
[231]
GSM1380043
[232]
GSM1380044
[233]
GSM1380045
[234]
GSM1380046
[235]
GSM1380047
[236]
GSM1380048
[237]
GSM1380049
[238]
GSM1380050
[239]
GSM1380051
[240]
GSM1380052
[241]
GSM1380053
[242]
GSM1380054
[243]
GSM1380055
[244]
GSM1380056
[245]
GSM1380057
[246]
GSM1380058
[247]
GSM1380059
[248]
GSM1380060
[249]
GSM1380061
[250]
GSM1380062
[251]
GSM1380063
[252]
GSM1380064
[253]
GSM1380065
[254]
GSM1380066
[255]
GSM1380067
[256]
GSM1380068
[257]
GSM1380069
[258]
GSM1380070
[259]
GSM1380071
[260]
GSM1380072
[261]
GSM1380073
[262]
GSM1380074
[263]
GSM1380075
[264]
GSM1380076
[265]
GSM1380077
[266]
GSM1380078
[267]
GSM1380079
[268]
GSM1380080
[269]
GSM1380081
[270]
GSM1380082
[271]
GSM1380083
[272]
GSM1380084
[273]
GSM1380085
[274]
GSM1380086
[275]
GSM1380087
[276]
GSM1380088
[277]
GSM1380089
[278]
GSM1380090
[279]
GSM1380091
[280]
GSM1380092
[281]
GSM1380093
[282]
GSM1380094
[283]
GSM1380095
[284]
GSM1380096
[285]
GSM1380097
[286]
GSM1380098
[287]
GSM1380099
[288]
GSM1380100
[289]
GSM1380101
[290]
GSM1380102
[291]
GSM1380103
[292]
GSM1380104
[293]
GSM1380105
[294]
GSM1380106
[295]
GSM1380107
[296]
GSM1380108
[297]
GSM1380109
[298]
GSM1380110
[299]
GSM1380111
[300]
GSM1380112
[301]
GSM1380113
[302]
GSM1380114
[303]
GSM1380115
[304]
GSM1380116
[305]
GSM1380117
[306]
GSM1380118
[307]
GSM1380119
[308]
GSM1380120
[309]
GSM1380121
[310]
GSM1380122
[311]
GSM1380123
[312]
GSM1380124
sample_count
313
group_sizes
null
group_sizes_basis
null
platform
11532
taxon
Homo sapiens
title
RNA-Seq Identifies Novel Myocardial Gene Expression Signatures of Heart Failure [microarray]
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[7] object · 15 fields
id
data:geo:GSE71613
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71613
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE71613
version
null
note
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
access_statement_kind
data_availability_statement
sample_accessions array · 8 items
[0]
GSM1841251
[1]
GSM1841259
[2]
GSM1841261
[3]
GSM1841263
[4]
GSM1841266
[5]
GSM1841268
[6]
GSM1841270
[7]
GSM1841273
sample_count
8
group_sizes
null
group_sizes_basis
null
platform
11154
taxon
Homo sapiens
title
RNA-Sequencing shows novel transcriptomic signatures in failing and non-failing human heart
resolution_checked_at
2026-11-01T20:16:08Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[8] object · 15 fields
id
data:geo:GSE76701
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76701
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE76701
version
null
note
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
access_statement_kind
data_availability_statement
sample_accessions array · 8 items
[0]
GSM2035924
[1]
GSM2035925
[2]
GSM2035926
[3]
GSM2035927
[4]
GSM2035928
[5]
GSM2035929
[6]
GSM2035930
[7]
GSM2035931
sample_count
8
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression in ischemic, end-stage failing and non-failing human hearts
resolution_checked_at
2026-11-01T18:06:44Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[9] object · 15 fields
id
data:geo:GSE79962
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79962
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE79962
version
null
note
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
access_statement_kind
data_availability_statement
sample_accessions array · 51 items
[0]
GSM2109130
[1]
GSM2109131
[2]
GSM2109132
[3]
GSM2109133
[4]
GSM2109134
[5]
GSM2109135
[6]
GSM2109136
[7]
GSM2109137
[8]
GSM2109138
[9]
GSM2109139
[10]
GSM2109140
[11]
GSM2109141
[12]
GSM2109142
[13]
GSM2109143
[14]
GSM2109144
[15]
GSM2109145
[16]
GSM2109146
[17]
GSM2109147
[18]
GSM2109148
[19]
GSM2109149
[20]
GSM2109150
[21]
GSM2109151
[22]
GSM2109152
[23]
GSM2109153
[24]
GSM2109154
[25]
GSM2109155
[26]
GSM2109156
[27]
GSM2109157
[28]
GSM2109158
[29]
GSM2109159
[30]
GSM2109160
[31]
GSM2109161
[32]
GSM2109162
[33]
GSM2109163
[34]
GSM2109164
[35]
GSM2109165
[36]
GSM2109166
[37]
GSM2109167
[38]
GSM2109168
[39]
GSM2109169
[40]
GSM2109170
[41]
GSM2109171
[42]
GSM2109172
[43]
GSM2109173
[44]
GSM2109174
[45]
GSM2109175
[46]
GSM2109176
[47]
GSM2109177
[48]
GSM2109178
[49]
GSM2109179
[50]
GSM2109180
sample_count
51
group_sizes
null
group_sizes_basis
null
platform
6244
taxon
Homo sapiens
title
Widespread downregulation of cardiac mitochondrial and sarcomeric genes in patients with sepsis
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 18 items
[0]
asset:data:geo:GSE116250 not downloaded
[1]
asset:data:geo:GSE116250 licence not read
[2]
asset:data:geo:GSE16499 not downloaded
[3]
asset:data:geo:GSE16499 licence not read
[4]
asset:data:geo:GSE26887 not downloaded
[5]
asset:data:geo:GSE26887 licence not read
[6]
asset:data:geo:GSE42955 not downloaded
[7]
asset:data:geo:GSE42955 licence not read
[8]
asset:data:geo:GSE48166 not downloaded
[9]
asset:data:geo:GSE48166 licence not read
[10]
asset:data:geo:GSE57338 not downloaded
[11]
asset:data:geo:GSE57338 licence not read
[12]
asset:data:geo:GSE71613 not downloaded
[13]
asset:data:geo:GSE71613 licence not read
[14]
asset:data:geo:GSE76701 not downloaded
[15]
asset:data:geo:GSE76701 licence not read
[16]
asset:data:geo:GSE79962 not downloaded
[17]
asset:data:geo:GSE79962 licence not read
analysis object · 4 fields
state
partial
methods array · 4 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
Then expression differences of CRGs between HF and NFDs samples were identified using limma.
[1] object · 10 fields
id
method-2
family
principal_component_analysis
specific
pca
label
Principal component analysis
dsl_operations array · 1 item
[0]
pca_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
wide_numeric_matrix
evidence object · 3 fields
score
0.8
band
high
quote
principal component analysis
[2] object · 10 fields
id
method-3
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.8
band
high
quote
protein-protein interaction
[3] object · 10 fields
id
method-4
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
pathway enrichment
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
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
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
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 · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
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: limma_voom.
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 · 9 items
[0]
GSE16499
[1]
GSE26887
[2]
GSE42955
[3]
GSE57338
[4]
GSE76701
[5]
GSE79962
[6]
GSE116250
[7]
GSE71613
[8]
GSE48166
resolved_accessions array · 9 items
[0]
GSE16499
[1]
GSE26887
[2]
GSE42955
[3]
GSE57338
[4]
GSE76701
[5]
GSE79962
[6]
GSE116250
[7]
GSE71613
[8]
GSE48166
statements array · 1 item
[0]
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962.
basis
The paper names GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, GSE79962, GSE116250, GSE71613, GSE48166 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
[0]
source:pmid:38562184
[1]
data:geo:GSE116250
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE116250
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:14:31Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
15390
completion_tokens
2982
cached_tokens
264
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:38562184
type
paper
label
pmid:38562184
[1] object · 3 fields
id
source:pmid:38562184
type
manuscript
label
PMID 38562184 full text
[2] object · 3 fields
id
data:geo:GSE116250
type
dataset
label
GEO GSE116250
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:38562184
type
paper
label
pmid:38562184
[1] object · 3 fields
id
source:pmid:38562184
type
manuscript
label
PMID 38562184 full text
[2] object · 3 fields
id
data:geo:GSE116250
type
dataset
label
GEO GSE116250
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-38562184
type
extraction
used array · 1 item
[0]
source:pmid:38562184
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE116250
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE116250
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:38562184
parent_hash
sha256:ce018b8ab5b2a421d44974115b2430919d0bf9c7108aea7977ed2fcfffb0061b
fork_of
null
gaps array · 23 items
[0]
asset:data:geo:GSE116250 licence not read
[1]
asset:data:geo:GSE116250 not downloaded
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asset:data:geo:GSE16499 licence not read
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asset:data:geo:GSE16499 not downloaded
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asset:data:geo:GSE26887 licence not read
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asset:data:geo:GSE26887 not downloaded
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asset:data:geo:GSE42955 licence not read
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asset:data:geo:GSE42955 not downloaded
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asset:data:geo:GSE48166 licence not read
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asset:data:geo:GSE48166 not downloaded
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asset:data:geo:GSE57338 licence not read
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asset:data:geo:GSE57338 not downloaded
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asset:data:geo:GSE71613 licence not read
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asset:data:geo:GSE71613 not downloaded
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asset:data:geo:GSE76701 licence not read
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asset:data:geo:GSE79962 licence not read
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asset:data:geo:GSE79962 not downloaded
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execution.not_run
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method.reader_differs_from_extractor
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review.human_critique
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review.independent_reader
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review.machine_critique
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99e1195c7d6f
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s not been investigated yet. Methods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57
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se-transcribed into cDNAs using ReverTra Ace qPCR RT kit (TOYOBO, Japan), and cDNA was subject
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Then expression differences of CRGs between HF and NFDs samples were identified using limma.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38562184", "kind": "science-record", "revision": 5, "content_hash": "sha256:d432c4d7f17c7666f5cc15c9a8f6da17d82f2f4ba4ecc70ff1ae5fcbbd7156cc", "created_at": "2026-11-01T19:20:31Z", "updated_at": "2026-11-01T22:54:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.", "doi": "10.3389/fcvm.2024.1349363", "pmid": "38562184", "authors": ["Dingyuan Tu", "Qiang Xu", "Yanmin Luan", "Jie Sun", "Xiaoli Zuo", "Chaoqun Ma"], "journal": "Frontiers in cardiovascular medicine", "year": 2024, "abstract": "Backgrounds: Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet.\nMethods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs.\nResults: We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity.\nConclusions: Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.", "claims": [{"id": "claim-method", "statement": "Then expression differences of CRGs between HF and NFDs samples were identified using limma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38562184", "locator": "sections[4] \"Identification of differentially expressed CRGs\" paragraph 2", "quote": "Then expression differences of CRGs between HF and NFDs samples were identified using limma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four machine-learning algorithms identified DLAT, SLC31A1, and DLST as three cuprotosis-related diagnostic indicators for heart failure with good diagnostic properties.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38562184", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four machine-learning algorithms identified DLAT, SLC31A1, and DLST as three cuprotosis-related diagnostic indicators for heart failure with good diagnostic properties.", "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:38562184"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116250"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116250"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38562184", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2024.1349363", "sha256": "sha256:29e3d516e21379c8103137ea98b673a21d7cbccd6fcb4162c5c8c56aba11d594", "bytes": 53812, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2024.1349363", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10982316", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE116250", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116250", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE116250", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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