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pmid:40529774

Bioinformatics-based screening of genes associated with dilated cardiomyopathy.

Yanghua Liu · Qingshan Wu · Fangni Mo · Ye Mai · Shengyuan Zhang · Xiang Wu

Journal of thoracic disease · 2025 doi:10.21037/jtd-2025-132

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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pmid:40529774
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title
Bioinformatics-based screening of genes associated with dilated cardiomyopathy.
doi
10.21037/jtd-2025-132
pmid
40529774
authors array · 6 items
[0]
Yanghua Liu
[1]
Qingshan Wu
[2]
Fangni Mo
[3]
Ye Mai
[4]
Shengyuan Zhang
[5]
Xiang Wu
journal
Journal of thoracic disease
year
2025
abstract
Background: Due to the lack of appropriate diagnostic biomarkers and intervention targets, the diagnosis and treatment of dilated cardiomyopathy (DCM) in clinical practice are considerably challenging. Therefore, this study aimed to identify reliable biomarker genes using bioinformatics methods to improve the clinical management of DCM. Methods: Three DCM gene datasets, GSE120895, GSE42955, and GSE3586, were downloaded from the Gene Expression Omnibus (GEO). Differential gene analysis was used to screen for differentially expressed genes in these datasets, and weighted gene coexpression network analysis (WGCNA) was used to screen for the gene coexpression modules most relevant to DCM. Machine learning algorithms and a Protein-protein interaction (PPI) network were used to screen for the core DCM genes in the gene coexpression module. Results: WGCNA identified the turquoise module as the most relevant gene module for DCM disease. Subsequently, machine learning algorithms identified 8 core genes while PPI screening identified 10 core genes. HSPA8 was found in both machine learning algorithms and PPI screening. Conclusions: In this study, the HSPA8 gene was found to be a core gene in DCM, demonstrating the closest association with this disease. Further research on HSPA8 is expected to provide a target for the diagnosis and treatment of DCM.
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Subsequently, based on five external datasets (GSE57338, GSE5406, GSE116250, GSE42955, and GSE19303), we employed receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of HSPA8 for DCM.
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sections[18] "The diagnostic efficacy of HSPA8 for DCM" paragraph 2
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Subsequently, based on five external datasets (GSE57338, GSE5406, GSE116250, GSE42955, and GSE19303), we employed receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of HSPA8 for DCM.
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sections[4] "Gene Expression Omnibus (GEO) datasets" paragraph 2
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Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586).
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HSPA8 had high diagnostic value for dilated cardiomyopathy, with an ROC area under the curve of 0.954.
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sections[18] "The diagnostic efficacy of HSPA8 for DCM" paragraph 2
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This yielded an area under the ROC curve of 0.954, indicating that HSPA8 has extremely high diagnostic value for DCM (Figure 6B).
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HSPA8 had high diagnostic value for dilated cardiomyopathy, with an ROC area under the curve of 0.954.
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GSM123561
[44]
GSM123562
[45]
GSM123563
[46]
GSM123564
[47]
GSM123565
[48]
GSM123566
[49]
GSM123567
[50]
GSM123568
[51]
GSM123569
[52]
GSM123570
[53]
GSM123571
[54]
GSM123572
[55]
GSM123573
[56]
GSM123574
[57]
GSM123575
[58]
GSM123576
[59]
GSM123577
[60]
GSM123578
[61]
GSM123579
[62]
GSM123580
[63]
GSM123581
[64]
GSM123582
[65]
GSM123583
[66]
GSM123584
[67]
GSM123585
[68]
GSM123586
[69]
GSM123587
[70]
GSM123588
[71]
GSM123589
[72]
GSM123590
[73]
GSM123591
[74]
GSM123592
[75]
GSM123593
[76]
GSM123594
[77]
GSM123595
[78]
GSM123596
[79]
GSM123597
[80]
GSM123598
[81]
GSM123599
[82]
GSM123600
[83]
GSM123601
[84]
GSM123602
[85]
GSM123603
[86]
GSM123604
[87]
GSM123605
[88]
GSM123606
[89]
GSM123607
[90]
GSM123608
[91]
GSM123609
[92]
GSM123610
[93]
GSM123611
[94]
GSM123612
[95]
GSM123613
[96]
GSM123614
[97]
GSM123615
[98]
GSM123616
[99]
GSM123617
[100]
GSM123618
[101]
GSM123619
[102]
GSM123620
[103]
GSM123621
[104]
GSM123622
[105]
GSM123623
[106]
GSM123624
[107]
GSM123625
[108]
GSM123626
[109]
GSM123627
[110]
GSM123628
[111]
GSM123629
[112]
GSM123630
[113]
GSM123631
[114]
GSM123632
[115]
GSM123633
[116]
GSM123634
[117]
GSM123635
[118]
GSM123636
[119]
GSM123637
[120]
GSM123638
[121]
GSM123639
[122]
GSM123640
[123]
GSM123641
[124]
GSM123642
[125]
GSM123643
[126]
GSM123644
[127]
GSM123645
[128]
GSM123646
[129]
GSM123647
[130]
GSM123648
[131]
GSM123649
[132]
GSM123650
[133]
GSM123651
[134]
GSM123652
[135]
GSM123653
[136]
GSM123654
[137]
GSM123655
[138]
GSM123656
[139]
GSM123657
[140]
GSM123658
[141]
GSM123659
[142]
GSM123660
[143]
GSM123661
[144]
GSM123662
[145]
GSM123663
[146]
GSM123664
[147]
GSM123665
[148]
GSM123666
[149]
GSM123667
[150]
GSM123668
[151]
GSM123669
[152]
GSM123670
[153]
GSM123671
[154]
GSM123672
[155]
GSM123673
[156]
GSM123674
[157]
GSM123675
[158]
GSM123676
[159]
GSM123677
[160]
GSM123678
[161]
GSM123679
[162]
GSM123680
[163]
GSM123681
[164]
GSM123682
[165]
GSM123683
[166]
GSM123684
[167]
GSM123685
[168]
GSM123686
[169]
GSM123687
[170]
GSM123688
[171]
GSM123689
[172]
GSM123690
[173]
GSM123691
[174]
GSM123692
[175]
GSM123693
[176]
GSM123694
[177]
GSM123695
[178]
GSM123696
[179]
GSM123697
[180]
GSM123698
[181]
GSM123699
[182]
GSM123700
[183]
GSM123701
[184]
GSM123702
[185]
GSM123703
[186]
GSM123704
[187]
GSM123705
[188]
GSM123706
[189]
GSM123707
[190]
GSM123708
[191]
GSM123709
[192]
GSM123710
[193]
GSM123711
[194]
GSM123712
[195]
GSM123713
[196]
GSM123714
[197]
GSM123715
[198]
GSM123716
[199]
GSM123717
[200]
GSM123718
[201]
GSM123719
[202]
GSM123720
[203]
GSM123721
[204]
GSM123722
[205]
GSM123723
[206]
GSM123724
[207]
GSM123725
[208]
GSM123726
[209]
GSM123727
sample_count
210
group_sizes
null
group_sizes_basis
null
platform
96
taxon
Homo sapiens
title
Human ischemic cardiomyopathy, idiopathic cardiomyopathy, and nonfailing controls
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[7] object · 15 fields
id
data:geo:GSE57338
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=GSE57338
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE57338
version
null
note
Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586).
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586).
access_statement_kind
repository_route_statement
sample_accessions array · 313 items
[0]
GSM1379812
[1]
GSM1379813
[2]
GSM1379814
[3]
GSM1379815
[4]
GSM1379816
[5]
GSM1379817
[6]
GSM1379818
[7]
GSM1379819
[8]
GSM1379820
[9]
GSM1379821
[10]
GSM1379822
[11]
GSM1379823
[12]
GSM1379824
[13]
GSM1379825
[14]
GSM1379826
[15]
GSM1379827
[16]
GSM1379828
[17]
GSM1379829
[18]
GSM1379830
[19]
GSM1379831
[20]
GSM1379832
[21]
GSM1379833
[22]
GSM1379834
[23]
GSM1379835
[24]
GSM1379836
[25]
GSM1379837
[26]
GSM1379838
[27]
GSM1379839
[28]
GSM1379840
[29]
GSM1379841
[30]
GSM1379842
[31]
GSM1379843
[32]
GSM1379844
[33]
GSM1379845
[34]
GSM1379846
[35]
GSM1379847
[36]
GSM1379848
[37]
GSM1379849
[38]
GSM1379850
[39]
GSM1379851
[40]
GSM1379852
[41]
GSM1379853
[42]
GSM1379854
[43]
GSM1379855
[44]
GSM1379856
[45]
GSM1379857
[46]
GSM1379858
[47]
GSM1379859
[48]
GSM1379860
[49]
GSM1379861
[50]
GSM1379862
[51]
GSM1379863
[52]
GSM1379864
[53]
GSM1379865
[54]
GSM1379866
[55]
GSM1379867
[56]
GSM1379868
[57]
GSM1379869
[58]
GSM1379870
[59]
GSM1379871
[60]
GSM1379872
[61]
GSM1379873
[62]
GSM1379874
[63]
GSM1379875
[64]
GSM1379876
[65]
GSM1379877
[66]
GSM1379878
[67]
GSM1379879
[68]
GSM1379880
[69]
GSM1379881
[70]
GSM1379882
[71]
GSM1379883
[72]
GSM1379884
[73]
GSM1379885
[74]
GSM1379886
[75]
GSM1379887
[76]
GSM1379888
[77]
GSM1379889
[78]
GSM1379890
[79]
GSM1379891
[80]
GSM1379892
[81]
GSM1379893
[82]
GSM1379894
[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
gaps array · 14 items
[0]
asset:data:geo:GSE116250 not downloaded
[1]
asset:data:geo:GSE116250 licence not read
[2]
asset:data:geo:GSE120895 not downloaded
[3]
asset:data:geo:GSE120895 licence not read
[4]
asset:data:geo:GSE19303 not downloaded
[5]
asset:data:geo:GSE19303 licence not read
[6]
asset:data:geo:GSE3586 not downloaded
[7]
asset:data:geo:GSE3586 licence not read
[8]
asset:data:geo:GSE42955 not downloaded
[9]
asset:data:geo:GSE42955 licence not read
[10]
asset:data:geo:GSE5406 not downloaded
[11]
asset:data:geo:GSE5406 licence not read
[12]
asset:data:geo:GSE57338 not downloaded
[13]
asset:data:geo:GSE57338 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
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
0.7
band
medium
quote
Protein-protein interaction
[1] object · 10 fields
id
method-2
family
null
specific
null
label
receiver operating characteristic (ROC) curve analysis
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Subsequently, based on five external datasets (GSE57338, GSE5406, GSE116250, GSE42955, and GSE19303), we employed receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of HSPA8 for DCM.
targets array · 0 items
gaps array · 1 item
[0]
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
[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]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
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: ppi_network.
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 · 7 items
[0]
GSE120895
[1]
GSE42955
[2]
GSE3586
[3]
GSE57338
[4]
GSE5406
[5]
GSE116250
[6]
GSE19303
resolved_accessions array · 7 items
[0]
GSE120895
[1]
GSE42955
[2]
GSE3586
[3]
GSE57338
[4]
GSE5406
[5]
GSE116250
[6]
GSE19303
statements array · 1 item
[0]
Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586).
basis
The paper names GSE120895, GSE42955, GSE3586, GSE57338, GSE5406, GSE116250, GSE19303 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]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
assets array · 2 items
[0]
source:pmid:40529774
[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
2026-11-01T19:54:29Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
null
calls
5
usage object · 3 fields
prompt_tokens
7484
completion_tokens
2320
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
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asset:data:geo:GSE120895 licence not read
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Complete file
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Therefore, this study aimed to identify reliable biomarker genes using bioinformatics methods to improve the clinical management of DCM.\nMethods: Three DCM gene datasets, GSE120895, GSE42955, and GSE3586, were downloaded from the Gene Expression Omnibus (GEO). Differential gene analysis was used to screen for differentially expressed genes in these datasets, and weighted gene coexpression network analysis (WGCNA) was used to screen for the gene coexpression modules most relevant to DCM. Machine learning algorithms and a Protein-protein interaction (PPI) network were used to screen for the core DCM genes in the gene coexpression module.\nResults: WGCNA identified the turquoise module as the most relevant gene module for DCM disease. Subsequently, machine learning algorithms identified 8 core genes while PPI screening identified 10 core genes. HSPA8 was found in both machine learning algorithms and PPI screening.\nConclusions: In this study, the HSPA8 gene was found to be a core gene in DCM, demonstrating the closest association with this disease. Further research on HSPA8 is expected to provide a target for the diagnosis and treatment of DCM.", "claims": [{"id": "claim-method", "statement": "Subsequently, based on five external datasets (GSE57338, GSE5406, GSE116250, GSE42955, and GSE19303), we employed receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of HSPA8 for DCM.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40529774", "locator": "sections[18] \"The diagnostic efficacy of HSPA8 for DCM\" paragraph 2", "quote": "Subsequently, based on five external datasets (GSE57338, GSE5406, GSE116250, GSE42955, and GSE19303), we employed receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of HSPA8 for DCM."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40529774", "locator": "sections[4] \"Gene Expression Omnibus (GEO) datasets\" paragraph 2", "quote": "Three datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), along with their expression profiles and sample information (GSE120895, GSE42955, and GSE3586)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HSPA8 had high diagnostic value for dilated cardiomyopathy, with an ROC area under the curve of 0.954.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40529774", "locator": "sections[18] \"The diagnostic efficacy of HSPA8 for DCM\" paragraph 2", "quote": "This yielded an area under the ROC curve of 0.954, indicating that HSPA8 has extremely high diagnostic value for DCM (Figure 6B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HSPA8 had high diagnostic value for dilated cardiomyopathy, with an ROC area under the curve of 0.954.", "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:40529774"], "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:40529774", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-2025-132", "sha256": 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