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pmid:38163157

Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis.

Zhongyi Sun · Qiuyue Zhao · Jiahao Zhang · Yanan Hu · Jiachen Qu · Han Gao · Zhiyong Peng

Heliyon · 2024 doi:10.1016/j.heliyon.2023.e22664

Paper

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Experiment

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Data

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Analysis

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Execution

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title
Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis.
doi
10.1016/j.heliyon.2023.e22664
pmid
38163157
authors array · 7 items
[0]
Zhongyi Sun
[1]
Qiuyue Zhao
[2]
Jiahao Zhang
[3]
Yanan Hu
[4]
Jiachen Qu
[5]
Han Gao
[6]
Zhiyong Peng
journal
Heliyon
year
2024
abstract
Background: Multiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear. Method: We screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Results: In sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793-0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues. Conclusions: Cuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.
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Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival.
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Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival.
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Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.
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sections[29] "Data availability" paragraph 2
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Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.
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LIAS and PDHB showed diagnostic performance for sepsis in the training and validation datasets.
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The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793–0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively.
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LIAS and PDHB showed diagnostic performance for sepsis in the training and validation datasets.
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[136]
GSM1602937
[137]
GSM1602938
[138]
GSM1602939
[139]
GSM1602940
[140]
GSM1602941
[141]
GSM1602942
[142]
GSM1602943
[143]
GSM1602944
[144]
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[145]
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[146]
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[147]
GSM1602948
[148]
GSM1602949
[149]
GSM1602950
[150]
GSM1602951
[151]
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[152]
GSM1602953
[153]
GSM1602954
[154]
GSM1602955
[155]
GSM1602956
[156]
GSM1602957
[157]
GSM1602958
[158]
GSM1602959
[159]
GSM1602960
[160]
GSM1602961
[161]
GSM1602962
[162]
GSM1602963
[163]
GSM1602964
[164]
GSM1602965
[165]
GSM1602966
[166]
GSM1602967
[167]
GSM1602968
[168]
GSM1602969
[169]
GSM1602970
[170]
GSM1602971
[171]
GSM1602972
[172]
GSM1602973
[173]
GSM1602974
[174]
GSM1602975
[175]
GSM1602976
[176]
GSM1691861
[177]
GSM1691862
[178]
GSM1691863
[179]
GSM1691864
[180]
GSM1691865
[181]
GSM1691866
[182]
GSM1691867
[183]
GSM1691868
[184]
GSM1691869
[185]
GSM1691870
[186]
GSM1691871
[187]
GSM1691873
[188]
GSM1691874
[189]
GSM1691875
[190]
GSM1691876
[191]
GSM1691877
[192]
GSM1691878
[193]
GSM1691879
[194]
GSM1691880
[195]
GSM1691881
[196]
GSM1691882
[197]
GSM1691883
[198]
GSM1691884
[199]
GSM1691885
[200]
GSM1691886
[201]
GSM1691887
[202]
GSM1691888
[203]
GSM1691889
[204]
GSM1691890
[205]
GSM1691891
[206]
GSM1691892
[207]
GSM1691893
[208]
GSM1691894
[209]
GSM1691895
[210]
GSM1691896
[211]
GSM1691897
[212]
GSM1691898
[213]
GSM1691899
[214]
GSM1691900
[215]
GSM1691901
[216]
GSM1691902
[217]
GSM1691903
[218]
GSM1691904
[219]
GSM1691905
[220]
GSM1691906
[221]
GSM1691907
[222]
GSM1691908
[223]
GSM1691909
[224]
GSM1691910
[225]
GSM1691911
[226]
GSM1691912
[227]
GSM1691913
[228]
GSM1691914
[229]
GSM1691915
[230]
GSM1691916
[231]
GSM1691917
[232]
GSM1691918
[233]
GSM1691919
[234]
GSM1691920
[235]
GSM1691921
[236]
GSM1691922
[237]
GSM1691923
[238]
GSM1691924
[239]
GSM1691925
[240]
GSM1691926
[241]
GSM1691927
[242]
GSM1691928
[243]
GSM1691929
[244]
GSM1691930
[245]
GSM1691932
[246]
GSM1691933
[247]
GSM1691934
[248]
GSM1691935
[249]
GSM1691936
[250]
GSM1691937
[251]
GSM1691938
[252]
GSM1691939
[253]
GSM1691940
[254]
GSM1691941
[255]
GSM1691942
[256]
GSM1691943
[257]
GSM1691944
[258]
GSM1691945
[259]
GSM1691946
[260]
GSM1691947
[261]
GSM1691948
[262]
GSM1691949
[263]
GSM1691950
[264]
GSM1691951
[265]
GSM1691952
[266]
GSM1691953
[267]
GSM1691954
[268]
GSM1691955
[269]
GSM1691957
[270]
GSM1691958
[271]
GSM1691959
[272]
GSM1691960
[273]
GSM1691961
[274]
GSM1691962
[275]
GSM1691963
[276]
GSM1691964
[277]
GSM1691965
[278]
GSM1691966
[279]
GSM1691967
[280]
GSM1691968
[281]
GSM1691969
[282]
GSM1691970
[283]
GSM1691971
[284]
GSM1691972
[285]
GSM1691973
[286]
GSM1691974
[287]
GSM1691975
[288]
GSM1691976
[289]
GSM1691977
[290]
GSM1691978
[291]
GSM1691979
[292]
GSM1691980
[293]
GSM1691981
[294]
GSM1691982
[295]
GSM1691983
[296]
GSM1691984
[297]
GSM1691985
[298]
GSM1691986
[299]
GSM1691987
[300]
GSM1691988
[301]
GSM1691989
[302]
GSM1691990
[303]
GSM1691991
[304]
GSM1691992
[305]
GSM1691993
[306]
GSM1691994
[307]
GSM1691995
[308]
GSM1691996
[309]
GSM1691997
[310]
GSM1691998
[311]
GSM1691999
[312]
GSM1692000
[313]
GSM1692001
[314]
GSM1692002
[315]
GSM1692003
[316]
GSM1692004
[317]
GSM1692005
[318]
GSM1692006
[319]
GSM1692007
[320]
GSM1692008
[321]
GSM1692009
[322]
GSM1692010
[323]
GSM1692011
[324]
GSM1692012
[325]
GSM1692015
[326]
GSM1692016
[327]
GSM1692017
[328]
GSM1692018
[329]
GSM1692019
[330]
GSM1692020
[331]
GSM1692021
[332]
GSM1692022
[333]
GSM1692023
[334]
GSM1692024
[335]
GSM1692025
[336]
GSM1692026
[337]
GSM1692027
[338]
GSM1692028
[339]
GSM1692029
[340]
GSM1692030
[341]
GSM1692032
[342]
GSM1692033
[343]
GSM1692034
[344]
GSM1692035
[345]
GSM1692036
[346]
GSM1692037
[347]
GSM1692038
[348]
GSM1692039
[349]
GSM1692040
[350]
GSM1692041
[351]
GSM1692042
[352]
GSM1692043
[353]
GSM1692044
[354]
GSM1692045
[355]
GSM1692046
[356]
GSM1692047
[357]
GSM1692048
[358]
GSM1692049
[359]
GSM1692050
[360]
GSM1692051
[361]
GSM1692052
[362]
GSM1692053
[363]
GSM1692054
[364]
GSM1692055
[365]
GSM1692056
[366]
GSM1692057
[367]
GSM1692058
[368]
GSM1692059
[369]
GSM1692060
[370]
GSM1692061
[371]
GSM1692062
[372]
GSM1692063
[373]
GSM1692064
[374]
GSM1692065
[375]
GSM1692066
[376]
GSM1692067
[377]
GSM1692068
[378]
GSM1692069
[379]
GSM1692070
[380]
GSM1692071
[381]
GSM1692072
[382]
GSM1692073
[383]
GSM1692074
[384]
GSM1692075
[385]
GSM1692077
[386]
GSM1692078
[387]
GSM1692079
[388]
GSM1692080
[389]
GSM1692081
[390]
GSM1692082
[391]
GSM1692083
[392]
GSM1692084
[393]
GSM1692085
[394]
GSM1692086
[395]
GSM1692087
[396]
GSM1692088
[397]
GSM1692089
[398]
GSM1692090
[399]
GSM1692091
[400]
GSM1692092
[401]
GSM1692093
[402]
GSM1692094
[403]
GSM1692095
[404]
GSM1692096
[405]
GSM1692097
[406]
GSM1692098
[407]
GSM1692099
[408]
GSM1692100
[409]
GSM1692101
[410]
GSM1692102
[411]
GSM1692103
[412]
GSM1692104
[413]
GSM1692105
[414]
GSM1692106
[415]
GSM1692107
[416]
GSM1692108
[417]
GSM1692109
[418]
GSM1692110
[419]
GSM1692111
[420]
GSM1692112
[421]
GSM1692114
[422]
GSM1692115
[423]
GSM1692116
[424]
GSM1692117
[425]
GSM1692118
[426]
GSM1692119
[427]
GSM1692120
[428]
GSM1692121
[429]
GSM1692122
[430]
GSM1692123
[431]
GSM1692124
[432]
GSM1692125
[433]
GSM1692126
[434]
GSM1692127
[435]
GSM1692128
[436]
GSM1692129
[437]
GSM1692130
[438]
GSM1692131
[439]
GSM1692132
[440]
GSM1692133
[441]
GSM1692135
[442]
GSM1692136
[443]
GSM1692137
[444]
GSM1692138
[445]
GSM1692139
[446]
GSM1692140
[447]
GSM1692141
[448]
GSM1692142
[449]
GSM1692143
[450]
GSM1692144
[451]
GSM1692145
[452]
GSM1692146
[453]
GSM1692147
[454]
GSM1692148
[455]
GSM1692149
[456]
GSM1692150
[457]
GSM1692151
[458]
GSM1692152
[459]
GSM1692153
[460]
GSM1692154
[461]
GSM1692155
[462]
GSM1692156
[463]
GSM1692157
[464]
GSM1692158
[465]
GSM1692159
[466]
GSM1692160
[467]
GSM1692161
[468]
GSM1692162
[469]
GSM1692163
[470]
GSM1692164
[471]
GSM1692165
[472]
GSM1692166
[473]
GSM1692167
[474]
GSM1692168
[475]
GSM1692169
[476]
GSM1692170
[477]
GSM1692171
[478]
GSM1692172
[479]
GSM1692173
[480]
GSM1692174
[481]
GSM1692175
[482]
GSM1692176
[483]
GSM1692177
[484]
GSM1692178
[485]
GSM1692179
[486]
GSM1692180
[487]
GSM1692181
[488]
GSM1692182
[489]
GSM1692183
[490]
GSM1692184
[491]
GSM1692185
[492]
GSM1692186
[493]
GSM1692187
[494]
GSM1692188
[495]
GSM1692189
[496]
GSM1692190
[497]
GSM1692191
[498]
GSM1692192
[499]
GSM1692193
[500]
GSM1692194
[501]
GSM1692195
[502]
GSM1692196
[503]
GSM1692197
[504]
GSM1692198
[505]
GSM1692199
[506]
GSM1692200
[507]
GSM1692201
[508]
GSM1692202
[509]
GSM1692203
[510]
GSM1692204
[511]
GSM1692205
[512]
GSM1692206
[513]
GSM1692207
[514]
GSM1692208
[515]
GSM1692209
[516]
GSM1692210
[517]
GSM1692211
[518]
GSM1692212
[519]
GSM1692213
[520]
GSM1692214
[521]
GSM1692215
[522]
GSM1692216
[523]
GSM1692217
[524]
GSM1692218
[525]
GSM1692219
[526]
GSM1692220
[527]
GSM1692221
[528]
GSM1692222
[529]
GSM1692223
[530]
GSM1692224
[531]
GSM1692225
[532]
GSM1692226
[533]
GSM1692227
[534]
GSM1692228
[535]
GSM1692229
[536]
GSM1692230
[537]
GSM1692231
[538]
GSM1692232
[539]
GSM1692233
[540]
GSM1692234
[541]
GSM1692235
[542]
GSM1692237
[543]
GSM1692238
[544]
GSM1692239
[545]
GSM1692240
[546]
GSM1692241
[547]
GSM1692242
[548]
GSM1692243
[549]
GSM1692244
[550]
GSM1692245
[551]
GSM1692246
[552]
GSM1692247
[553]
GSM1692248
[554]
GSM1692249
[555]
GSM1692250
[556]
GSM1692251
[557]
GSM1692252
[558]
GSM1692253
[559]
GSM1692254
[560]
GSM1692255
[561]
GSM1692256
[562]
GSM1692257
[563]
GSM1692258
[564]
GSM1692259
[565]
GSM1692260
[566]
GSM1692261
[567]
GSM1692262
[568]
GSM1692263
[569]
GSM1692264
[570]
GSM1692265
[571]
GSM1692266
[572]
GSM1692267
[573]
GSM1692268
[574]
GSM1692269
[575]
GSM1692270
[576]
GSM1692271
[577]
GSM1692272
[578]
GSM1692273
[579]
GSM1692274
[580]
GSM1692275
[581]
GSM1692276
[582]
GSM1692277
[583]
GSM1692278
[584]
GSM1692279
[585]
GSM1692280
[586]
GSM1692281
[587]
GSM1692282
[588]
GSM1692283
[589]
GSM1692284
[590]
GSM1692286
[591]
GSM1692287
[592]
GSM1692288
[593]
GSM1692289
[594]
GSM1692290
[595]
GSM1692291
[596]
GSM1692292
[597]
GSM1692293
[598]
GSM1692294
[599]
GSM1692295
[600]
GSM1692296
[601]
GSM1692297
[602]
GSM1692298
[603]
GSM1692299
[604]
GSM1692300
[605]
GSM1692301
[606]
GSM1692302
[607]
GSM1692303
[608]
GSM1692304
[609]
GSM1692305
[610]
GSM1692306
[611]
GSM1692307
[612]
GSM1692308
[613]
GSM1692309
[614]
GSM1692310
[615]
GSM1692311
[616]
GSM1692312
[617]
GSM1692313
[618]
GSM1692314
[619]
GSM1692315
[620]
GSM1692316
[621]
GSM1692317
[622]
GSM1692318
[623]
GSM1692319
[624]
GSM1692320
[625]
GSM1692321
[626]
GSM1692322
[627]
GSM1692323
[628]
GSM1692324
[629]
GSM1692326
[630]
GSM1692327
[631]
GSM1692328
[632]
GSM1692329
[633]
GSM1692330
[634]
GSM1692331
[635]
GSM1692332
[636]
GSM1692333
[637]
GSM1692334
[638]
GSM1692335
[639]
GSM1692336
[640]
GSM1692337
[641]
GSM1692338
[642]
GSM1692339
[643]
GSM1692340
[644]
GSM1692341
[645]
GSM1692342
[646]
GSM1692343
[647]
GSM1692344
[648]
GSM1692345
[649]
GSM1692346
[650]
GSM1692347
[651]
GSM1692348
[652]
GSM1692349
[653]
GSM1692350
[654]
GSM1692352
[655]
GSM1692353
[656]
GSM1692354
[657]
GSM1692355
[658]
GSM1692356
[659]
GSM1692357
[660]
GSM1692358
[661]
GSM1692359
[662]
GSM1692360
[663]
GSM1692361
[664]
GSM1692362
[665]
GSM1692363
[666]
GSM1692364
[667]
GSM1692365
[668]
GSM1692366
[669]
GSM1692367
[670]
GSM1692368
[671]
GSM1692369
[672]
GSM1692370
[673]
GSM1692371
[674]
GSM1692372
[675]
GSM1692373
[676]
GSM1692374
[677]
GSM1692375
[678]
GSM1692376
[679]
GSM1692377
[680]
GSM1692378
[681]
GSM1692379
[682]
GSM1692380
[683]
GSM1692381
[684]
GSM1692382
[685]
GSM1692383
[686]
GSM1692384
[687]
GSM1692385
[688]
GSM1692386
[689]
GSM1692387
[690]
GSM1692388
[691]
GSM1692389
[692]
GSM1692390
[693]
GSM1692391
[694]
GSM1692392
[695]
GSM1692393
[696]
GSM1692394
[697]
GSM1692395
[698]
GSM1692396
[699]
GSM1692397
[700]
GSM1692398
[701]
GSM1692399
[702]
GSM1692400
[703]
GSM1692402
[704]
GSM1692404
[705]
GSM1692405
[706]
GSM1692406
[707]
GSM1692407
[708]
GSM1692408
[709]
GSM1692409
[710]
GSM1692410
[711]
GSM1692411
[712]
GSM1692412
[713]
GSM1692413
[714]
GSM1692414
[715]
GSM1692415
[716]
GSM1692416
[717]
GSM1692417
[718]
GSM1692418
[719]
GSM1692419
[720]
GSM1692420
[721]
GSM1692421
[722]
GSM1692422
[723]
GSM1692423
[724]
GSM1692424
[725]
GSM1692425
[726]
GSM1692426
[727]
GSM1692427
[728]
GSM1692428
[729]
GSM1692429
[730]
GSM1692430
[731]
GSM1692431
[732]
GSM1692432
[733]
GSM1692433
[734]
GSM1692434
[735]
GSM1692435
[736]
GSM1692436
[737]
GSM1692437
[738]
GSM1692438
[739]
GSM1692439
[740]
GSM1692440
[741]
GSM1692441
[742]
GSM1692442
[743]
GSM1692443
[744]
GSM1692444
[745]
GSM1692445
[746]
GSM1692446
[747]
GSM1692447
[748]
GSM1692448
[749]
GSM1692449
[750]
GSM1692450
[751]
GSM1692451
[752]
GSM1692452
[753]
GSM1692453
[754]
GSM1692454
[755]
GSM1692455
[756]
GSM1692456
[757]
GSM1692457
[758]
GSM1692458
[759]
GSM1692459
[760]
GSM1692460
[761]
GSM1692461
[762]
GSM1692462
[763]
GSM1692463
[764]
GSM1692465
[765]
GSM1692466
[766]
GSM1692467
[767]
GSM1692468
[768]
GSM1692469
[769]
GSM1692470
[770]
GSM1692471
[771]
GSM1692472
[772]
GSM1692473
[773]
GSM1692474
[774]
GSM1692475
[775]
GSM1692476
[776]
GSM1692477
[777]
GSM1692478
[778]
GSM1692479
[779]
GSM1692480
[780]
GSM1692481
[781]
GSM1692482
[782]
GSM1692483
[783]
GSM1692484
[784]
GSM1692485
[785]
GSM1692487
[786]
GSM1692488
[787]
GSM1692489
[788]
GSM1692490
[789]
GSM1692491
[790]
GSM1692492
[791]
GSM1692493
[792]
GSM1692494
[793]
GSM1692495
[794]
GSM1692496
[795]
GSM1692497
[796]
GSM1692498
[797]
GSM1692499
[798]
GSM1692501
[799]
GSM1692502
[800]
GSM1692503
[801]
GSM1692504
sample_count
802
group_sizes
null
group_sizes_basis
null
platform
13667
taxon
Homo sapiens
title
Genome-wide blood transcriptional profiling in critically ill patients - MARS consortium
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 · 4 items
[0]
asset:data:geo:GSE134347 not downloaded
[1]
asset:data:geo:GSE134347 licence not read
[2]
asset:data:geo:GSE65682 not downloaded
[3]
asset:data:geo:GSE65682 licence not read
analysis object · 4 fields
state
partial
methods array · 5 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival.
[1] object · 10 fields
id
method-2
family
group_comparison
specific
mann_whitney
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
group_comparison_mann_whitney
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
Mann-Whitney
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
kaplan_meier
label
Survival analysis
dsl_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Kaplan-Meier
[3] object · 10 fields
id
method-4
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
0.8
band
high
quote
Limma
[4] object · 10 fields
id
method-5
family
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.7
band
medium
quote
Pearson correlation
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 · 1 item
[0]
cox_ph
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: cox_proportional_hazards.
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 · 2 items
[0]
GSE65682
[1]
GSE134347
resolved_accessions array · 2 items
[0]
GSE65682
[1]
GSE134347
statements array · 1 item
[0]
Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.
basis
The paper names GSE65682, GSE134347 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 2 items
[0]
source:pmid:38163157
[1]
data:geo:GSE134347
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:GSE134347
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
2026-11-01T19:03:51Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
13400
completion_tokens
1365
cached_tokens
256
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:38163157
type
paper
label
pmid:38163157
[1] object · 3 fields
id
source:pmid:38163157
type
manuscript
label
PMID 38163157 full text
[2] object · 3 fields
id
data:geo:GSE134347
type
dataset
label
GEO GSE134347
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:38163157
type
paper
label
pmid:38163157
[1] object · 3 fields
id
source:pmid:38163157
type
manuscript
label
PMID 38163157 full text
[2] object · 3 fields
id
data:geo:GSE134347
type
dataset
label
GEO GSE134347
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-38163157
type
extraction
used array · 1 item
[0]
source:pmid:38163157
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE134347
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE134347
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:38163157
parent_hash
sha256:e9fa5eb95d49f5900019ec775aed006bb5496f2d9e95f2c690279ce975376af8
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE134347 licence not read
[1]
asset:data:geo:GSE134347 not downloaded
[2]
asset:data:geo:GSE65682 licence not read
[3]
asset:data:geo:GSE65682 not downloaded
[4]
execution.not_run
[5]
method.reader_differs_from_extractor
[6]
review.human_critique
[7]
review.independent_reader
[8]
review.machine_critique
assays array · 4 items
[0] object · 11 fields
role
data_generation
definition_id
animal_model
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
ionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) a
[1]
nd CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mR
[2]
ionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-
[3]
nd CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference
order_basis
paper_text_order:942
id
assay:1
[1] object · 11 fields
role
data_generation
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Re
order_basis
paper_text_order:960
id
assay:2
[2] object · 11 fields
role
data_generation
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
ing real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Results: In sepsis, most CRGs were up
[1]
tive PCR (qRT-PCR) and immunohistochemistry (IHC). Results: In sepsis, most CRGs were upregul
[2]
ant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and
[3]
Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulate
order_basis
paper_text_order:1001
id
assay:3
[3] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival.
order_basis
consumes_earlier_output
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assay:4
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38163157", "kind": "science-record", "revision": 3, "content_hash": "sha256:6b753e59e259c19581c3b0ce586bd51fedcabc5a73b1c299392b11cba6a270f4", "created_at": "2026-11-01T19:20:05Z", "updated_at": "2026-11-01T22:10:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis.", "doi": "10.1016/j.heliyon.2023.e22664", "pmid": "38163157", "authors": ["Zhongyi Sun", "Qiuyue Zhao", "Jiahao Zhang", "Yanan Hu", "Jiachen Qu", "Han Gao", "Zhiyong Peng"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Multiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear.\nMethod: We screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC).\nResults: In sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793-0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues.\nConclusions: Cuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38163157", "locator": "sections[21] \"Prognosis-related analysis\" paragraph 2", "quote": "Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38163157", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LIAS and PDHB showed diagnostic performance for sepsis in the training and validation datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38163157", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793–0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LIAS and PDHB showed diagnostic performance for sepsis in the training and validation datasets.", "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:38163157"], "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:GSE134347"], "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:GSE134347"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38163157", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2023.e22664", "sha256": "sha256:1e5db17d92efbdad77006f272e218232d2f523a38616fe326481dc09eb370c27", "bytes": 45687, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2023.e22664", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10754710", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE134347", "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=GSE134347", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE134347", "version": null, "note": "Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3942706", "GSM3942707", "GSM3942708", "GSM3942709", "GSM3942710", "GSM3942711", "GSM3942712", "GSM3942713", "GSM3942714", "GSM3942715", "GSM3942716", "GSM3942717", "GSM3942718", "GSM3942719", "GSM3942720", "GSM3942721", "GSM3942722", "GSM3942723", "GSM3942724", "GSM3942725", "GSM3942726", "GSM3942727", "GSM3942728", "GSM3942729", "GSM3942730", "GSM3942731", "GSM3942732", "GSM3942733", "GSM3942734", "GSM3942735", "GSM3942736", "GSM3942737", "GSM3942738", "GSM3942739", "GSM3942740", "GSM3942741", "GSM3942742", "GSM3942743", "GSM3942744", "GSM3942745", "GSM3942746", "GSM3942747", "GSM3942748", "GSM3942749", "GSM3942750", "GSM3942751", "GSM3942752", 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"evidence": {"score": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was used to estimate the hazard ratio (HR) of survival."}}, {"id": "method-2", "family": "group_comparison", "specific": "mann_whitney", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_mann_whitney"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Mann-Whitney"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE65682", "GSE134347"], "resolved_accessions": ["GSE65682", "GSE134347"], "statements": ["Data associated with this study has been deposited at https://www.ncbi.nlm.nih.gov/gds under the accession number GSE65682 and GSE134347."], "basis": "The paper names GSE65682, GSE134347 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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Analysis in the septic mouse model demonstrated no significant difference in mR", "ionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-", "nd CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference"], "order_basis": "paper_text_order:942", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Re"], "order_basis": "paper_text_order:960", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ing real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Results: In sepsis, most CRGs were up", "tive PCR (qRT-PCR) and immunohistochemistry (IHC). 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