Corpus Engine Towards a reproducible .science file.
← All .science files

pmid:37056756

Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related prognostic biomarkers for predicting immunotherapeutic response in hepatocellular carcinoma.

Kai Zhang · Enwu Yuan

Frontiers in immunology · 2023 doi:10.3389/fimmu.2023.1142126

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:37056756
kind
science-record
content_hash
sha256:79fc745f98ad86aa82d8cf1da6278a168eb4ffa1ad779916ffa9ce92239f9bbf
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T19:01:42Z
updated_at
2026-11-01T21:30:33Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related prognostic biomarkers for predicting immunotherapeutic response in hepatocellular carcinoma.
doi
10.3389/fimmu.2023.1142126
pmid
37056756
authors array · 2 items
[0]
Kai Zhang
[1]
Enwu Yuan
journal
Frontiers in immunology
year
2023
abstract
Introduction: Natural killer (NK) cells play an irreplaceable and important role as a subtype of innate immune cells in the contemporary setting of antitumor immunity. Methods: We chose a total of 1,196 samples for this analysis from the public dataset's six separate cohorts. To identify 42 NK cell marker genes, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocellular carcinoma (HCC). Results: Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns. This signature's prognostic prediction ability was well verified across several validation cohorts. Patients with high scores had higher TIDE scores but lower immune cell infiltration percentages. Importantly, low-scoring patients had superior immunotherapy response and prognosis than high-scoring patients in an independent immunotherapy cohort (IMvigor210). Finally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we discovered a lower number of CD56+ cells in the HCC tissue sections with high TUBA1B expression. Discussion: In summary, our research created a unique prognostic profile based on NK cell marker genes that may accurately predict how well immunotherapy would work for HCC patients.
claims array · 3 items
[0] object · 6 fields
id
claim-method
statement
A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei).
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37056756
locator
sections[4] "Identification of NK cells and their hub genes" paragraph 2
quote
A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei).
state
complete
[1] object · 6 fields
id
claim-data-access
statement
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 within the article/ Supplementary Materials .
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37056756
locator
sections[19] "Data availability statement" paragraph 2
quote
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 within the article/ Supplementary Materials .
state
complete
[2] object · 6 fields
id
claim-target-result
statement
Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns.
kind
qualitative
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37056756
locator
sections[0] "Abstract" paragraph 3
quote
Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns.
state
complete
gaps array · 0 items
experiment object · 8 fields
state
complete
objective
Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns.
steps array · 3 items
[0] object · 13 fields
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.
parameters object · 0 fields
inputs array · 1 item
[0]
source:pmid:37056756
outputs array · 1 item
[0]
the published estimate and its evidence
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
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.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE14520
outputs array · 1 item
[0]
the source data with its repository provenance
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
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.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE14520
outputs array · 1 item
[0]
the replicated result and its comparison
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 0 items
data object · 3 fields
state
partial
assets array · 4 items
[0] object · 15 fields
id
source:pmid:37056756
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.3389/fimmu.2023.1142126
sha256
sha256:3ac3edc7a4eca5af2a1c463c5bd927844818e8a577ea1624f99c76a72177b1e1
bytes
29485
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.3389/fimmu.2023.1142126
version
null
note
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
structure object · 4 fields
pmcid
PMC10086229
source
europe_pmc_fulltext
coverage
whole_body_without_references
text_hash_scope
rendered_section_body
[1] object · 15 fields
id
data:geo:GSE14520
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=GSE14520
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE14520
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 within the article/ Supplementary Materials .
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 within the article/ Supplementary Materials .
access_statement_kind
data_availability_statement
sample_accessions array · 488 items
[0]
GSM362947
[1]
GSM362948
[2]
GSM362949
[3]
GSM362950
[4]
GSM362951
[5]
GSM362952
[6]
GSM362953
[7]
GSM362954
[8]
GSM362955
[9]
GSM362956
[10]
GSM362957
[11]
GSM362958
[12]
GSM362959
[13]
GSM362960
[14]
GSM362961
[15]
GSM362962
[16]
GSM362963
[17]
GSM362964
[18]
GSM362965
[19]
GSM362966
[20]
GSM362967
[21]
GSM362968
[22]
GSM362969
[23]
GSM362970
[24]
GSM362971
[25]
GSM362972
[26]
GSM362973
[27]
GSM362974
[28]
GSM362975
[29]
GSM362976
[30]
GSM362977
[31]
GSM362978
[32]
GSM362979
[33]
GSM362980
[34]
GSM362981
[35]
GSM362982
[36]
GSM362983
[37]
GSM362984
[38]
GSM362985
[39]
GSM362986
[40]
GSM362987
[41]
GSM362988
[42]
GSM362989
[43]
GSM362990
[44]
GSM362991
[45]
GSM362992
[46]
GSM362993
[47]
GSM362994
[48]
GSM362995
[49]
GSM362996
[50]
GSM362997
[51]
GSM362998
[52]
GSM362999
[53]
GSM363000
[54]
GSM363001
[55]
GSM363002
[56]
GSM363003
[57]
GSM363004
[58]
GSM363005
[59]
GSM363006
[60]
GSM363007
[61]
GSM363008
[62]
GSM363009
[63]
GSM363010
[64]
GSM363011
[65]
GSM363012
[66]
GSM363013
[67]
GSM363014
[68]
GSM363015
[69]
GSM363016
[70]
GSM363017
[71]
GSM363018
[72]
GSM363019
[73]
GSM363020
[74]
GSM363021
[75]
GSM363022
[76]
GSM363023
[77]
GSM363024
[78]
GSM363025
[79]
GSM363026
[80]
GSM363027
[81]
GSM363028
[82]
GSM363029
[83]
GSM363030
[84]
GSM363031
[85]
GSM363032
[86]
GSM363033
[87]
GSM363034
[88]
GSM363035
[89]
GSM363036
[90]
GSM363037
[91]
GSM363038
[92]
GSM363039
[93]
GSM363040
[94]
GSM363041
[95]
GSM363042
[96]
GSM363043
[97]
GSM363044
[98]
GSM363045
[99]
GSM363046
[100]
GSM363047
[101]
GSM363048
[102]
GSM363049
[103]
GSM363050
[104]
GSM363051
[105]
GSM363052
[106]
GSM363053
[107]
GSM363054
[108]
GSM363055
[109]
GSM363056
[110]
GSM363057
[111]
GSM363058
[112]
GSM363059
[113]
GSM363060
[114]
GSM363061
[115]
GSM363062
[116]
GSM363063
[117]
GSM363064
[118]
GSM363065
[119]
GSM363066
[120]
GSM363067
[121]
GSM363068
[122]
GSM363069
[123]
GSM363070
[124]
GSM363071
[125]
GSM363072
[126]
GSM363073
[127]
GSM363074
[128]
GSM363075
[129]
GSM363076
[130]
GSM363077
[131]
GSM363078
[132]
GSM363079
[133]
GSM363080
[134]
GSM363081
[135]
GSM363082
[136]
GSM363083
[137]
GSM363084
[138]
GSM363085
[139]
GSM363086
[140]
GSM363087
[141]
GSM363088
[142]
GSM363089
[143]
GSM363090
[144]
GSM363091
[145]
GSM363092
[146]
GSM363093
[147]
GSM363094
[148]
GSM363095
[149]
GSM363096
[150]
GSM363097
[151]
GSM363098
[152]
GSM363099
[153]
GSM363100
[154]
GSM363101
[155]
GSM363102
[156]
GSM363103
[157]
GSM363104
[158]
GSM363105
[159]
GSM363106
[160]
GSM363107
[161]
GSM363108
[162]
GSM363109
[163]
GSM363110
[164]
GSM363111
[165]
GSM363112
[166]
GSM363113
[167]
GSM363114
[168]
GSM363115
[169]
GSM363116
[170]
GSM363117
[171]
GSM363118
[172]
GSM363119
[173]
GSM363120
[174]
GSM363121
[175]
GSM363122
[176]
GSM363123
[177]
GSM363124
[178]
GSM363125
[179]
GSM363126
[180]
GSM363127
[181]
GSM363128
[182]
GSM363129
[183]
GSM363130
[184]
GSM363131
[185]
GSM363132
[186]
GSM363133
[187]
GSM363134
[188]
GSM363135
[189]
GSM363136
[190]
GSM363137
[191]
GSM363138
[192]
GSM363139
[193]
GSM363140
[194]
GSM363141
[195]
GSM363142
[196]
GSM363143
[197]
GSM363144
[198]
GSM363145
[199]
GSM363146
[200]
GSM363147
[201]
GSM363148
[202]
GSM363149
[203]
GSM363150
[204]
GSM363151
[205]
GSM363152
[206]
GSM363153
[207]
GSM363154
[208]
GSM363155
[209]
GSM363156
[210]
GSM363157
[211]
GSM363158
[212]
GSM363159
[213]
GSM363160
[214]
GSM363161
[215]
GSM363162
[216]
GSM363163
[217]
GSM363164
[218]
GSM363165
[219]
GSM363166
[220]
GSM363167
[221]
GSM363168
[222]
GSM363169
[223]
GSM363170
[224]
GSM363171
[225]
GSM363172
[226]
GSM363173
[227]
GSM363174
[228]
GSM363175
[229]
GSM363176
[230]
GSM363177
[231]
GSM363178
[232]
GSM363179
[233]
GSM363180
[234]
GSM363181
[235]
GSM363182
[236]
GSM363183
[237]
GSM363184
[238]
GSM363185
[239]
GSM363186
[240]
GSM363187
[241]
GSM363188
[242]
GSM363189
[243]
GSM363190
[244]
GSM363191
[245]
GSM363192
[246]
GSM363193
[247]
GSM363194
[248]
GSM363195
[249]
GSM363196
[250]
GSM363197
[251]
GSM363198
[252]
GSM363199
[253]
GSM363200
[254]
GSM363201
[255]
GSM363202
[256]
GSM363203
[257]
GSM363204
[258]
GSM363205
[259]
GSM363206
[260]
GSM363207
[261]
GSM363208
[262]
GSM363209
[263]
GSM363210
[264]
GSM363211
[265]
GSM363212
[266]
GSM363213
[267]
GSM363214
[268]
GSM363215
[269]
GSM363216
[270]
GSM363217
[271]
GSM363218
[272]
GSM363219
[273]
GSM363220
[274]
GSM363221
[275]
GSM363222
[276]
GSM363223
[277]
GSM363224
[278]
GSM363225
[279]
GSM363226
[280]
GSM363227
[281]
GSM363228
[282]
GSM363229
[283]
GSM363230
[284]
GSM363231
[285]
GSM363232
[286]
GSM363233
[287]
GSM363234
[288]
GSM363235
[289]
GSM363236
[290]
GSM363237
[291]
GSM363238
[292]
GSM363239
[293]
GSM363240
[294]
GSM363241
[295]
GSM363242
[296]
GSM363243
[297]
GSM363244
[298]
GSM363245
[299]
GSM363246
[300]
GSM363247
[301]
GSM363248
[302]
GSM363249
[303]
GSM363250
[304]
GSM363251
[305]
GSM363252
[306]
GSM363253
[307]
GSM363254
[308]
GSM363255
[309]
GSM363256
[310]
GSM363257
[311]
GSM363258
[312]
GSM363259
[313]
GSM363260
[314]
GSM363261
[315]
GSM363262
[316]
GSM363263
[317]
GSM363264
[318]
GSM363265
[319]
GSM363266
[320]
GSM363267
[321]
GSM363268
[322]
GSM363269
[323]
GSM363270
[324]
GSM363271
[325]
GSM363272
[326]
GSM363273
[327]
GSM363274
[328]
GSM363275
[329]
GSM363276
[330]
GSM363277
[331]
GSM363278
[332]
GSM363279
[333]
GSM363280
[334]
GSM363281
[335]
GSM363282
[336]
GSM363283
[337]
GSM363284
[338]
GSM363285
[339]
GSM363286
[340]
GSM363287
[341]
GSM363288
[342]
GSM363289
[343]
GSM363290
[344]
GSM363291
[345]
GSM363292
[346]
GSM363293
[347]
GSM363294
[348]
GSM363295
[349]
GSM363296
[350]
GSM363297
[351]
GSM363298
[352]
GSM363299
[353]
GSM363300
[354]
GSM363301
[355]
GSM363302
[356]
GSM363303
[357]
GSM363304
[358]
GSM363305
[359]
GSM363306
[360]
GSM363307
[361]
GSM363308
[362]
GSM363309
[363]
GSM363310
[364]
GSM363311
[365]
GSM363312
[366]
GSM363313
[367]
GSM363314
[368]
GSM363315
[369]
GSM363316
[370]
GSM363317
[371]
GSM363318
[372]
GSM363319
[373]
GSM363320
[374]
GSM363321
[375]
GSM363322
[376]
GSM363323
[377]
GSM363324
[378]
GSM363325
[379]
GSM363326
[380]
GSM363327
[381]
GSM363328
[382]
GSM363329
[383]
GSM363330
[384]
GSM363331
[385]
GSM363332
[386]
GSM363333
[387]
GSM363334
[388]
GSM363335
[389]
GSM363336
[390]
GSM363337
[391]
GSM363338
[392]
GSM363339
[393]
GSM363340
[394]
GSM363341
[395]
GSM363342
[396]
GSM363343
[397]
GSM363344
[398]
GSM363345
[399]
GSM363346
[400]
GSM363347
[401]
GSM363348
[402]
GSM363349
[403]
GSM363350
[404]
GSM363351
[405]
GSM363352
[406]
GSM363353
[407]
GSM363354
[408]
GSM363355
[409]
GSM363356
[410]
GSM363357
[411]
GSM363358
[412]
GSM363359
[413]
GSM363360
[414]
GSM363361
[415]
GSM363362
[416]
GSM363363
[417]
GSM363364
[418]
GSM363365
[419]
GSM363366
[420]
GSM363367
[421]
GSM363368
[422]
GSM363369
[423]
GSM363370
[424]
GSM363371
[425]
GSM363372
[426]
GSM363373
[427]
GSM363374
[428]
GSM363375
[429]
GSM363376
[430]
GSM363378
[431]
GSM363380
[432]
GSM363381
[433]
GSM363382
[434]
GSM363384
[435]
GSM363386
[436]
GSM363388
[437]
GSM363391
[438]
GSM363393
[439]
GSM363397
[440]
GSM363400
[441]
GSM363402
[442]
GSM363404
[443]
GSM363407
[444]
GSM363417
[445]
GSM363420
[446]
GSM363421
[447]
GSM363422
[448]
GSM363423
[449]
GSM363424
[450]
GSM363425
[451]
GSM363426
[452]
GSM363427
[453]
GSM363428
[454]
GSM363429
[455]
GSM363430
[456]
GSM363431
[457]
GSM363432
[458]
GSM363433
[459]
GSM363434
[460]
GSM363435
[461]
GSM363436
[462]
GSM363437
[463]
GSM363438
[464]
GSM363439
[465]
GSM363440
[466]
GSM363441
[467]
GSM363442
[468]
GSM363443
[469]
GSM363444
[470]
GSM363445
[471]
GSM363446
[472]
GSM363447
[473]
GSM363448
[474]
GSM363449
[475]
GSM363450
[476]
GSM363451
[477]
GSM712532
[478]
GSM712533
[479]
GSM712534
[480]
GSM712535
[481]
GSM712536
[482]
GSM712537
[483]
GSM712538
[484]
GSM712539
[485]
GSM712540
[486]
GSM712541
[487]
GSM712542
sample_count
488
group_sizes
null
group_sizes_basis
null
platform
3921;571
taxon
Homo sapiens
title
Gene expression data of human hepatocellular carcinoma (HCC)
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[2] object · 15 fields
id
data:geo:GSE149614
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=GSE149614
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE149614
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 within the article/ Supplementary Materials .
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 within the article/ Supplementary Materials .
access_statement_kind
data_availability_statement
sample_accessions array · 21 items
[0]
GSM4505944
[1]
GSM4505945
[2]
GSM4505946
[3]
GSM4505947
[4]
GSM4505948
[5]
GSM4505949
[6]
GSM4505950
[7]
GSM4505951
[8]
GSM4505952
[9]
GSM4505953
[10]
GSM4505954
[11]
GSM4505955
[12]
GSM4505956
[13]
GSM4505957
[14]
GSM4505958
[15]
GSM4505959
[16]
GSM4505960
[17]
GSM4505961
[18]
GSM4505962
[19]
GSM4505963
[20]
GSM4505964
sample_count
21
group_sizes
null
group_sizes_basis
null
platform
24676
taxon
Homo sapiens
title
A Single-Cell Atlas of the Multicellular Ecosystem of Primary and Metastatic Hepatocellular Carcinoma
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 15 fields
id
data:geo:GSE76427
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=GSE76427
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE76427
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 within the article/ Supplementary Materials .
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 within the article/ Supplementary Materials .
access_statement_kind
data_availability_statement
sample_accessions array · 167 items
[0]
GSM2011285
[1]
GSM2011286
[2]
GSM2011287
[3]
GSM2011288
[4]
GSM2011289
[5]
GSM2011290
[6]
GSM2011291
[7]
GSM2011292
[8]
GSM2011293
[9]
GSM2011294
[10]
GSM2011295
[11]
GSM2011296
[12]
GSM2011297
[13]
GSM2011298
[14]
GSM2011299
[15]
GSM2011300
[16]
GSM2011301
[17]
GSM2011302
[18]
GSM2011303
[19]
GSM2011304
[20]
GSM2011305
[21]
GSM2011306
[22]
GSM2011307
[23]
GSM2011308
[24]
GSM2011309
[25]
GSM2011310
[26]
GSM2011311
[27]
GSM2011312
[28]
GSM2011313
[29]
GSM2011314
[30]
GSM2011315
[31]
GSM2011316
[32]
GSM2011317
[33]
GSM2011318
[34]
GSM2011319
[35]
GSM2011320
[36]
GSM2011321
[37]
GSM2011322
[38]
GSM2011323
[39]
GSM2011324
[40]
GSM2011325
[41]
GSM2011326
[42]
GSM2011327
[43]
GSM2011328
[44]
GSM2011329
[45]
GSM2011330
[46]
GSM2011331
[47]
GSM2011332
[48]
GSM2011333
[49]
GSM2011334
[50]
GSM2011335
[51]
GSM2011336
[52]
GSM2011337
[53]
GSM2011338
[54]
GSM2011339
[55]
GSM2011340
[56]
GSM2011341
[57]
GSM2011342
[58]
GSM2011343
[59]
GSM2011344
[60]
GSM2011345
[61]
GSM2011346
[62]
GSM2011347
[63]
GSM2011348
[64]
GSM2011349
[65]
GSM2011350
[66]
GSM2011351
[67]
GSM2011352
[68]
GSM2011353
[69]
GSM2011354
[70]
GSM2011355
[71]
GSM2011356
[72]
GSM2011357
[73]
GSM2011358
[74]
GSM2011359
[75]
GSM2011360
[76]
GSM2011361
[77]
GSM2011362
[78]
GSM2011363
[79]
GSM2011364
[80]
GSM2011365
[81]
GSM2011366
[82]
GSM2011367
[83]
GSM2011368
[84]
GSM2011369
[85]
GSM2011370
[86]
GSM2011371
[87]
GSM2011372
[88]
GSM2011373
[89]
GSM2011374
[90]
GSM2011375
[91]
GSM2011376
[92]
GSM2011377
[93]
GSM2011378
[94]
GSM2011379
[95]
GSM2011380
[96]
GSM2011381
[97]
GSM2011382
[98]
GSM2011383
[99]
GSM2011384
[100]
GSM2011385
[101]
GSM2011386
[102]
GSM2011387
[103]
GSM2011388
[104]
GSM2011389
[105]
GSM2011390
[106]
GSM2011391
[107]
GSM2011392
[108]
GSM2011393
[109]
GSM2011394
[110]
GSM2011395
[111]
GSM2011396
[112]
GSM2011397
[113]
GSM2011398
[114]
GSM2011399
[115]
GSM2011400
[116]
GSM2011401
[117]
GSM2011402
[118]
GSM2011403
[119]
GSM2011404
[120]
GSM2011405
[121]
GSM2011406
[122]
GSM2011407
[123]
GSM2011408
[124]
GSM2011409
[125]
GSM2011410
[126]
GSM2011411
[127]
GSM2011412
[128]
GSM2011413
[129]
GSM2011414
[130]
GSM2011415
[131]
GSM2011416
[132]
GSM2011417
[133]
GSM2011418
[134]
GSM2011419
[135]
GSM2011420
[136]
GSM2011421
[137]
GSM2011422
[138]
GSM2011423
[139]
GSM2011424
[140]
GSM2011425
[141]
GSM2011426
[142]
GSM2011427
[143]
GSM2011428
[144]
GSM2011429
[145]
GSM2011430
[146]
GSM2011431
[147]
GSM2011432
[148]
GSM2011433
[149]
GSM2011434
[150]
GSM2011435
[151]
GSM2011436
[152]
GSM2011437
[153]
GSM2011438
[154]
GSM2011439
[155]
GSM2011440
[156]
GSM2011441
[157]
GSM2011442
[158]
GSM2011443
[159]
GSM2011444
[160]
GSM2011445
[161]
GSM2011446
[162]
GSM2011447
[163]
GSM2011448
[164]
GSM2011449
[165]
GSM2011450
[166]
GSM2011451
sample_count
167
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
Microarray expression data for tumor and adjacent non-tumor tissues from hepatocellular carcinoma patients
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 · 6 items
[0]
asset:data:geo:GSE14520 not downloaded
[1]
asset:data:geo:GSE14520 licence not read
[2]
asset:data:geo:GSE149614 not downloaded
[3]
asset:data:geo:GSE149614 licence not read
[4]
asset:data:geo:GSE76427 not downloaded
[5]
asset:data:geo:GSE76427 licence not read
analysis object · 4 fields
state
complete
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
A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei).
[1] object · 10 fields
id
method-2
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
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
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
Log-rank
[3] object · 10 fields
id
method-4
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
0.8
band
high
quote
Pearson correlation
[4] object · 10 fields
id
method-5
family
contingency_analysis
specific
chi_square
label
Categorical (contingency table) analysis
dsl_operations array · 1 item
[0]
chi_square_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
categorical_cross_tabulation
evidence object · 3 fields
score
0.8
band
high
quote
chi-square
targets array · 0 items
gaps array · 0 items
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 · 3 items
[0]
GSE149614
[1]
GSE14520
[2]
GSE76427
resolved_accessions array · 3 items
[0]
GSE149614
[1]
GSE14520
[2]
GSE76427
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 within the article/ Supplementary Materials .
basis
The paper names GSE149614, GSE14520, GSE76427 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 · 1 item
[0]
cox_ph
assets array · 2 items
[0]
source:pmid:37056756
[1]
data:geo:GSE14520
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:GSE14520
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
2026-11-01T18:36:23Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
8565
completion_tokens
2097
cached_tokens
128
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:37056756
type
paper
label
pmid:37056756
[1] object · 3 fields
id
source:pmid:37056756
type
manuscript
label
PMID 37056756 full text
[2] object · 3 fields
id
data:geo:GSE14520
type
dataset
label
GEO GSE14520
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:37056756
type
paper
label
pmid:37056756
[1] object · 3 fields
id
source:pmid:37056756
type
manuscript
label
PMID 37056756 full text
[2] object · 3 fields
id
data:geo:GSE14520
type
dataset
label
GEO GSE14520
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-37056756
type
extraction
used array · 1 item
[0]
source:pmid:37056756
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE14520
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE14520
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:37056756
parent_hash
sha256:c9375a5663bac19c80b5594c8a5639e4fe5b954912b282e383d0d3eebaecb527
fork_of
null
gaps array · 10 items
[0]
asset:data:geo:GSE14520 licence not read
[1]
asset:data:geo:GSE14520 not downloaded
[2]
asset:data:geo:GSE149614 licence not read
[3]
asset:data:geo:GSE149614 not downloaded
[4]
asset:data:geo:GSE76427 licence not read
[5]
asset:data:geo:GSE76427 not downloaded
[6]
execution.not_run
[7]
review.human_critique
[8]
review.independent_reader
[9]
review.machine_critique
assays array · 4 items
[0] object · 11 fields
role
data_generation
definition_id
rna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 3 items
[0]
Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related pr
[1]
carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocell
[2]
. We also retrieved the matched clinical and transcriptome data from the IMvigor210 cohort of patients
order_basis
paper_text_order:0
id
assay:1
[1] object · 11 fields
role
data_generation
definition_id
scrna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
unsupported
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related pr
[1]
es, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocell
[2]
than 0.05. Identification of NK cells in the scRNA-seq samples Figure S1 displays the full outcomes
[3]
gure 2H ). Identification of NK cells in the scRNA-seq samples. (A) The TSNE plots showed the distr
order_basis
paper_text_order:0
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 · 2 items
[0]
ally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we disc
[1]
629) and CD56 (Abcam, ab75813) antibodies in IHC experiments on formalin-fixed de-paraffinize
order_basis
paper_text_order:1192
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]
A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei).
order_basis
consumes_earlier_output
id
assay:4
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37056756", "kind": "science-record", "revision": 2, "content_hash": "sha256:79fc745f98ad86aa82d8cf1da6278a168eb4ffa1ad779916ffa9ce92239f9bbf", "created_at": "2026-11-01T19:01:42Z", "updated_at": "2026-11-01T21:30:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related prognostic biomarkers for predicting immunotherapeutic response in hepatocellular carcinoma.", "doi": "10.3389/fimmu.2023.1142126", "pmid": "37056756", "authors": ["Kai Zhang", "Enwu Yuan"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Introduction: Natural killer (NK) cells play an irreplaceable and important role as a subtype of innate immune cells in the contemporary setting of antitumor immunity.\nMethods: We chose a total of 1,196 samples for this analysis from the public dataset's six separate cohorts. To identify 42 NK cell marker genes, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocellular carcinoma (HCC).\nResults: Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns. This signature's prognostic prediction ability was well verified across several validation cohorts. Patients with high scores had higher TIDE scores but lower immune cell infiltration percentages. Importantly, low-scoring patients had superior immunotherapy response and prognosis than high-scoring patients in an independent immunotherapy cohort (IMvigor210). Finally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we discovered a lower number of CD56+ cells in the HCC tissue sections with high TUBA1B expression.\nDiscussion: In summary, our research created a unique prognostic profile based on NK cell marker genes that may accurately predict how well immunotherapy would work for HCC patients.", "claims": [{"id": "claim-method", "statement": "A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37056756", "locator": "sections[4] \"Identification of NK cells and their hub genes\" paragraph 2", "quote": "A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "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 within the article/ Supplementary Materials .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37056756", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "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 within the article/ Supplementary Materials ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37056756", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns.", "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:37056756"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37056756", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1142126", "sha256": "sha256:3ac3edc7a4eca5af2a1c463c5bd927844818e8a577ea1624f99c76a72177b1e1", "bytes": 29485, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1142126", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10086229", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "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=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "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 within the article/ Supplementary Materials .", "structure": {"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 within the article/ Supplementary Materials .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM362947", "GSM362948", "GSM362949", "GSM362950", "GSM362951", "GSM362952", "GSM362953", "GSM362954", "GSM362955", "GSM362956", "GSM362957", "GSM362958", "GSM362959", "GSM362960", "GSM362961", "GSM362962", "GSM362963", "GSM362964", "GSM362965", "GSM362966", "GSM362967", "GSM362968", "GSM362969", "GSM362970", "GSM362971", "GSM362972", "GSM362973", "GSM362974", "GSM362975", "GSM362976", "GSM362977", "GSM362978", "GSM362979", "GSM362980", "GSM362981", "GSM362982", "GSM362983", "GSM362984", "GSM362985", "GSM362986", "GSM362987", "GSM362988", "GSM362989", "GSM362990", "GSM362991", "GSM362992", "GSM362993", "GSM362994", "GSM362995", "GSM362996", "GSM362997", "GSM362998", "GSM362999", "GSM363000", "GSM363001", "GSM363002", "GSM363003", "GSM363004", "GSM363005", "GSM363006", "GSM363007", "GSM363008", "GSM363009", "GSM363010", "GSM363011", "GSM363012", "GSM363013", "GSM363014", "GSM363015", "GSM363016", "GSM363017", "GSM363018", "GSM363019", "GSM363020", "GSM363021", "GSM363022", "GSM363023", "GSM363024", "GSM363025", "GSM363026", "GSM363027", "GSM363028", "GSM363029", "GSM363030", "GSM363031", "GSM363032", "GSM363033", "GSM363034", "GSM363035", "GSM363036", "GSM363037", "GSM363038", "GSM363039", "GSM363040", "GSM363041", "GSM363042", "GSM363043", "GSM363044", "GSM363045", "GSM363046", "GSM363047", "GSM363048", "GSM363049", "GSM363050", "GSM363051", "GSM363052", "GSM363053", "GSM363054", "GSM363055", "GSM363056", "GSM363057", "GSM363058", "GSM363059", "GSM363060", "GSM363061", "GSM363062", "GSM363063", "GSM363064", "GSM363065", "GSM363066", "GSM363067", "GSM363068", "GSM363069", "GSM363070", "GSM363071", "GSM363072", "GSM363073", "GSM363074", "GSM363075", "GSM363076", "GSM363077", "GSM363078", "GSM363079", "GSM363080", "GSM363081", "GSM363082", "GSM363083", "GSM363084", "GSM363085", "GSM363086", "GSM363087", "GSM363088", "GSM363089", "GSM363090", "GSM363091", "GSM363092", "GSM363093", "GSM363094", "GSM363095", "GSM363096", "GSM363097", "GSM363098", "GSM363099", "GSM363100", "GSM363101", "GSM363102", "GSM363103", "GSM363104", "GSM363105", "GSM363106", "GSM363107", "GSM363108", "GSM363109", "GSM363110", "GSM363111", "GSM363112", "GSM363113", "GSM363114", "GSM363115", "GSM363116", "GSM363117", "GSM363118", "GSM363119", "GSM363120", "GSM363121", "GSM363122", "GSM363123", "GSM363124", "GSM363125", "GSM363126", "GSM363127", "GSM363128", "GSM363129", "GSM363130", "GSM363131", "GSM363132", "GSM363133", "GSM363134", "GSM363135", "GSM363136", "GSM363137", "GSM363138", "GSM363139", "GSM363140", "GSM363141", "GSM363142", "GSM363143", "GSM363144", "GSM363145", "GSM363146", "GSM363147", "GSM363148", "GSM363149", "GSM363150", "GSM363151", "GSM363152", "GSM363153", "GSM363154", "GSM363155", "GSM363156", "GSM363157", "GSM363158", "GSM363159", "GSM363160", "GSM363161", "GSM363162", "GSM363163", "GSM363164", "GSM363165", "GSM363166", "GSM363167", "GSM363168", "GSM363169", "GSM363170", "GSM363171", "GSM363172", "GSM363173", "GSM363174", "GSM363175", "GSM363176", "GSM363177", "GSM363178", "GSM363179", "GSM363180", "GSM363181", "GSM363182", "GSM363183", "GSM363184", "GSM363185", "GSM363186", "GSM363187", "GSM363188", "GSM363189", "GSM363190", "GSM363191", "GSM363192", "GSM363193", "GSM363194", "GSM363195", "GSM363196", "GSM363197", "GSM363198", "GSM363199", "GSM363200", "GSM363201", "GSM363202", "GSM363203", "GSM363204", "GSM363205", "GSM363206", "GSM363207", "GSM363208", "GSM363209", "GSM363210", "GSM363211", "GSM363212", "GSM363213", "GSM363214", "GSM363215", "GSM363216", "GSM363217", "GSM363218", "GSM363219", "GSM363220", "GSM363221", "GSM363222", "GSM363223", "GSM363224", "GSM363225", "GSM363226", "GSM363227", "GSM363228", "GSM363229", "GSM363230", "GSM363231", "GSM363232", "GSM363233", "GSM363234", "GSM363235", "GSM363236", "GSM363237", "GSM363238", "GSM363239", "GSM363240", "GSM363241", "GSM363242", "GSM363243", "GSM363244", "GSM363245", "GSM363246", "GSM363247", "GSM363248", "GSM363249", "GSM363250", "GSM363251", "GSM363252", "GSM363253", "GSM363254", "GSM363255", "GSM363256", "GSM363257", "GSM363258", "GSM363259", "GSM363260", "GSM363261", "GSM363262", "GSM363263", "GSM363264", "GSM363265", "GSM363266", "GSM363267", "GSM363268", "GSM363269", "GSM363270", "GSM363271", "GSM363272", "GSM363273", "GSM363274", "GSM363275", "GSM363276", "GSM363277", "GSM363278", "GSM363279", "GSM363280", "GSM363281", "GSM363282", "GSM363283", "GSM363284", "GSM363285", "GSM363286", "GSM363287", "GSM363288", "GSM363289", "GSM363290", "GSM363291", "GSM363292", "GSM363293", "GSM363294", "GSM363295", "GSM363296", "GSM363297", "GSM363298", "GSM363299", "GSM363300", "GSM363301", "GSM363302", "GSM363303", "GSM363304", "GSM363305", "GSM363306", "GSM363307", "GSM363308", "GSM363309", "GSM363310", "GSM363311", "GSM363312", "GSM363313", "GSM363314", "GSM363315", "GSM363316", "GSM363317", "GSM363318", "GSM363319", "GSM363320", "GSM363321", "GSM363322", "GSM363323", "GSM363324", "GSM363325", "GSM363326", "GSM363327", "GSM363328", "GSM363329", "GSM363330", "GSM363331", "GSM363332", "GSM363333", "GSM363334", "GSM363335", "GSM363336", "GSM363337", "GSM363338", "GSM363339", "GSM363340", "GSM363341", "GSM363342", "GSM363343", "GSM363344", "GSM363345", "GSM363346", "GSM363347", "GSM363348", "GSM363349", "GSM363350", "GSM363351", "GSM363352", "GSM363353", "GSM363354", "GSM363355", "GSM363356", "GSM363357", "GSM363358", "GSM363359", "GSM363360", "GSM363361", "GSM363362", "GSM363363", "GSM363364", "GSM363365", "GSM363366", "GSM363367", "GSM363368", "GSM363369", "GSM363370", "GSM363371", "GSM363372", "GSM363373", "GSM363374", "GSM363375", "GSM363376", "GSM363378", "GSM363380", "GSM363381", "GSM363382", "GSM363384", "GSM363386", "GSM363388", "GSM363391", "GSM363393", "GSM363397", "GSM363400", "GSM363402", "GSM363404", "GSM363407", "GSM363417", "GSM363420", "GSM363421", "GSM363422", "GSM363423", "GSM363424", "GSM363425", "GSM363426", "GSM363427", "GSM363428", "GSM363429", "GSM363430", "GSM363431", "GSM363432", "GSM363433", "GSM363434", "GSM363435", "GSM363436", "GSM363437", "GSM363438", "GSM363439", "GSM363440", "GSM363441", "GSM363442", "GSM363443", "GSM363444", "GSM363445", "GSM363446", "GSM363447", "GSM363448", "GSM363449", "GSM363450", "GSM363451", "GSM712532", "GSM712533", "GSM712534", "GSM712535", "GSM712536", "GSM712537", "GSM712538", "GSM712539", "GSM712540", "GSM712541", "GSM712542"], "sample_count": 488, "group_sizes": null, "group_sizes_basis": null, "platform": "3921;571", "taxon": "Homo sapiens", "title": "Gene expression data of human hepatocellular carcinoma (HCC)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE149614", "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=GSE149614", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE149614", "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 within the article/ Supplementary Materials .", "structure": {"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 within the article/ Supplementary Materials .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4505944", "GSM4505945", "GSM4505946", "GSM4505947", "GSM4505948", "GSM4505949", "GSM4505950", "GSM4505951", "GSM4505952", "GSM4505953", "GSM4505954", "GSM4505955", "GSM4505956", "GSM4505957", "GSM4505958", "GSM4505959", "GSM4505960", "GSM4505961", "GSM4505962", "GSM4505963", "GSM4505964"], "sample_count": 21, "group_sizes": null, "group_sizes_basis": null, "platform": "24676", "taxon": "Homo sapiens", "title": "A Single-Cell Atlas of the Multicellular Ecosystem of Primary and Metastatic Hepatocellular Carcinoma", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE76427", "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=GSE76427", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE76427", "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 within the article/ Supplementary Materials .", "structure": {"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 within the article/ Supplementary Materials .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2011285", "GSM2011286", "GSM2011287", "GSM2011288", "GSM2011289", "GSM2011290", "GSM2011291", "GSM2011292", "GSM2011293", "GSM2011294", "GSM2011295", "GSM2011296", "GSM2011297", "GSM2011298", "GSM2011299", "GSM2011300", "GSM2011301", "GSM2011302", "GSM2011303", "GSM2011304", "GSM2011305", "GSM2011306", "GSM2011307", "GSM2011308", "GSM2011309", "GSM2011310", "GSM2011311", "GSM2011312", "GSM2011313", "GSM2011314", "GSM2011315", "GSM2011316", "GSM2011317", "GSM2011318", "GSM2011319", "GSM2011320", "GSM2011321", "GSM2011322", "GSM2011323", "GSM2011324", "GSM2011325", "GSM2011326", "GSM2011327", "GSM2011328", "GSM2011329", "GSM2011330", "GSM2011331", "GSM2011332", "GSM2011333", "GSM2011334", "GSM2011335", "GSM2011336", "GSM2011337", "GSM2011338", "GSM2011339", "GSM2011340", "GSM2011341", "GSM2011342", "GSM2011343", "GSM2011344", "GSM2011345", "GSM2011346", "GSM2011347", "GSM2011348", "GSM2011349", "GSM2011350", "GSM2011351", "GSM2011352", "GSM2011353", "GSM2011354", "GSM2011355", "GSM2011356", "GSM2011357", "GSM2011358", "GSM2011359", "GSM2011360", "GSM2011361", "GSM2011362", "GSM2011363", "GSM2011364", "GSM2011365", "GSM2011366", "GSM2011367", "GSM2011368", "GSM2011369", "GSM2011370", "GSM2011371", "GSM2011372", "GSM2011373", "GSM2011374", "GSM2011375", "GSM2011376", "GSM2011377", "GSM2011378", "GSM2011379", "GSM2011380", "GSM2011381", "GSM2011382", "GSM2011383", "GSM2011384", "GSM2011385", "GSM2011386", "GSM2011387", "GSM2011388", "GSM2011389", "GSM2011390", "GSM2011391", "GSM2011392", "GSM2011393", "GSM2011394", "GSM2011395", "GSM2011396", "GSM2011397", "GSM2011398", "GSM2011399", "GSM2011400", "GSM2011401", "GSM2011402", "GSM2011403", "GSM2011404", "GSM2011405", "GSM2011406", "GSM2011407", "GSM2011408", "GSM2011409", "GSM2011410", "GSM2011411", "GSM2011412", "GSM2011413", "GSM2011414", "GSM2011415", "GSM2011416", "GSM2011417", "GSM2011418", "GSM2011419", "GSM2011420", "GSM2011421", "GSM2011422", "GSM2011423", "GSM2011424", "GSM2011425", "GSM2011426", "GSM2011427", "GSM2011428", "GSM2011429", "GSM2011430", "GSM2011431", "GSM2011432", "GSM2011433", "GSM2011434", "GSM2011435", "GSM2011436", "GSM2011437", "GSM2011438", "GSM2011439", "GSM2011440", "GSM2011441", "GSM2011442", "GSM2011443", "GSM2011444", "GSM2011445", "GSM2011446", "GSM2011447", "GSM2011448", "GSM2011449", "GSM2011450", "GSM2011451"], "sample_count": 167, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Microarray expression data for tumor and adjacent non-tumor tissues from hepatocellular carcinoma patients", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE149614 not downloaded", "asset:data:geo:GSE149614 licence not read", "asset:data:geo:GSE76427 not downloaded", "asset:data:geo:GSE76427 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei)."}}, {"id": "method-2", "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-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Log-rank"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-5", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"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": {"tier": "R1", "reachable": true, "accessions": ["GSE149614", "GSE14520", "GSE76427"], "resolved_accessions": ["GSE149614", "GSE14520", "GSE76427"], "statements": ["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 within the article/ Supplementary Materials ."], "basis": "The paper names GSE149614, GSE14520, GSE76427 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37056756", "data:geo:GSE14520"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE14520"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:36:23Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8565, "completion_tokens": 2097, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37056756", "type": "paper", "label": "pmid:37056756"}, {"id": "source:pmid:37056756", "type": "manuscript", "label": "PMID 37056756 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37056756", "type": "paper", "label": "pmid:37056756"}, {"id": "source:pmid:37056756", "type": "manuscript", "label": "PMID 37056756 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37056756", "type": "extraction", "used": ["source:pmid:37056756"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE14520", "type": "accession_resolution", "used": ["data:geo:GSE14520"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"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": {"parent_id": "pmid:37056756", "parent_hash": "sha256:c9375a5663bac19c80b5594c8a5639e4fe5b954912b282e383d0d3eebaecb527", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE149614 licence not read", "asset:data:geo:GSE149614 not downloaded", "asset:data:geo:GSE76427 licence not read", "asset:data:geo:GSE76427 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related pr", "carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocell", ". We also retrieved the matched clinical and transcriptome data from the IMvigor210 cohort of patients"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related pr", "es, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocell", "than 0.05. Identification of NK cells in the scRNA-seq samples Figure S1 displays the full outcomes", "gure 2H ). Identification of NK cells in the scRNA-seq samples. (A) The TSNE plots showed the distr"], "order_basis": "paper_text_order:0", "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": ["ally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we disc", "629) and CD56 (Abcam, ab75813) antibodies in IHC experiments on formalin-fixed de-paraffinize"], "order_basis": "paper_text_order:1192", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A univariate Cox regression analysis with P less than 0.05 was then used to further identify the genes among these NK marker genes that are associated with prognosis. We used the LASO-Cox regression to compress the number of genes and created a risk profile based on the outcomes of the multivariate Cox model using the equation: Risk score =∑iCoefficient (Genei)*Expression (Genei)."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}