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pmid:35614936

A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients.

Yuquan Bai · Yun Pei · Liang Xia · Lin Ma · Senyi Deng

Frontiers in pharmacology · 2022 doi:10.3389/fphar.2022.818170

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Experiment

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Data

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title
A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients.
doi
10.3389/fphar.2022.818170
pmid
35614936
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Yuquan Bai
[1]
Yun Pei
[2]
Liang Xia
[3]
Lin Ma
[4]
Senyi Deng
journal
Frontiers in pharmacology
year
2022
abstract
Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment. Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed. Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment. Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.
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The “survival” and “survminer” R packages were used for further survival analysis. The Kaplan-Meier method was used to estimate the survival curve, and the log-rank test was used to analyze the difference in survival time.
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sections[5] "2.3 Construction and Verification of Immune-Prognosis Index" paragraph 2
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The “survival” and “survminer” R packages were used for further survival analysis. The Kaplan-Meier method was used to estimate the survival curve, and the log-rank test was used to analyze the difference in survival time.
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The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
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sections[15] "Data Availability Statement" paragraph 2
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The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
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The immune-prognosis index was prognostic in lung adenocarcinoma, with the IPI-high subgroup having a higher AUC and 5-year survival rate than the IPI-low subgroup (AUC = 0.725, p = 0.00389).
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Grouping by the median of IPI score, we found that the AUC and 5-years survival rate of LUAD patients in the IPI-high subgroup was significantly higher than IPI-low subgroup (AUC = 0.725, p = 0.00389, Figures 2G,K).
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sections[9] "3.2 Construction and Verification of Immune-Prognosis Index in Lung Adenocarcinoma" paragraph 2
quote
Grouping by the median of IPI score, we found that the AUC and 5-years survival rate of LUAD patients in the IPI-high subgroup was significantly higher than IPI-low subgroup (AUC = 0.725, p = 0.00389, Figures 2G,K).
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The immune-prognosis index was prognostic in lung adenocarcinoma, with the IPI-high subgroup having a higher AUC and 5-year survival rate than the IPI-low subgroup (AUC = 0.725, p = 0.00389).
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[304]
GSM748364
[305]
GSM748365
[306]
GSM748366
sample_count
307
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
"Off-context" gene expression in lung cancer identifies a group of metastatic-prone tumors
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 15 fields
id
data:geo:GSE37745
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=GSE37745
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE37745
version
null
note
The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
access_statement_kind
data_availability_statement
sample_accessions array · 196 items
[0]
GSM1019138
[1]
GSM1019139
[2]
GSM1019140
[3]
GSM1019141
[4]
GSM1019142
[5]
GSM1019143
[6]
GSM1019144
[7]
GSM1019145
[8]
GSM1019146
[9]
GSM1019147
[10]
GSM1019148
[11]
GSM1019149
[12]
GSM1019150
[13]
GSM1019151
[14]
GSM1019152
[15]
GSM1019153
[16]
GSM1019154
[17]
GSM1019155
[18]
GSM1019156
[19]
GSM1019157
[20]
GSM1019158
[21]
GSM1019159
[22]
GSM1019160
[23]
GSM1019161
[24]
GSM1019162
[25]
GSM1019163
[26]
GSM1019164
[27]
GSM1019165
[28]
GSM1019166
[29]
GSM1019167
[30]
GSM1019168
[31]
GSM1019169
[32]
GSM1019170
[33]
GSM1019171
[34]
GSM1019172
[35]
GSM1019173
[36]
GSM1019174
[37]
GSM1019175
[38]
GSM1019176
[39]
GSM1019177
[40]
GSM1019178
[41]
GSM1019179
[42]
GSM1019180
[43]
GSM1019181
[44]
GSM1019182
[45]
GSM1019183
[46]
GSM1019184
[47]
GSM1019185
[48]
GSM1019186
[49]
GSM1019187
[50]
GSM1019188
[51]
GSM1019189
[52]
GSM1019190
[53]
GSM1019191
[54]
GSM1019192
[55]
GSM1019193
[56]
GSM1019194
[57]
GSM1019195
[58]
GSM1019196
[59]
GSM1019197
[60]
GSM1019198
[61]
GSM1019199
[62]
GSM1019200
[63]
GSM1019201
[64]
GSM1019202
[65]
GSM1019203
[66]
GSM1019204
[67]
GSM1019205
[68]
GSM1019206
[69]
GSM1019207
[70]
GSM1019208
[71]
GSM1019209
[72]
GSM1019210
[73]
GSM1019211
[74]
GSM1019212
[75]
GSM1019213
[76]
GSM1019214
[77]
GSM1019215
[78]
GSM1019216
[79]
GSM1019217
[80]
GSM1019218
[81]
GSM1019219
[82]
GSM1019220
[83]
GSM1019221
[84]
GSM1019222
[85]
GSM1019223
[86]
GSM1019224
[87]
GSM1019225
[88]
GSM1019226
[89]
GSM1019227
[90]
GSM1019228
[91]
GSM1019229
[92]
GSM1019230
[93]
GSM1019231
[94]
GSM1019232
[95]
GSM1019233
[96]
GSM1019234
[97]
GSM1019235
[98]
GSM1019236
[99]
GSM1019237
[100]
GSM1019238
[101]
GSM1019239
[102]
GSM1019240
[103]
GSM1019241
[104]
GSM1019242
[105]
GSM1019243
[106]
GSM1019244
[107]
GSM1019245
[108]
GSM1019246
[109]
GSM1019247
[110]
GSM1019248
[111]
GSM1019249
[112]
GSM1019250
[113]
GSM1019251
[114]
GSM1019252
[115]
GSM1019253
[116]
GSM1019254
[117]
GSM1019255
[118]
GSM1019256
[119]
GSM1019257
[120]
GSM1019258
[121]
GSM1019259
[122]
GSM1019260
[123]
GSM1019261
[124]
GSM1019262
[125]
GSM1019263
[126]
GSM1019264
[127]
GSM1019265
[128]
GSM1019266
[129]
GSM1019267
[130]
GSM1019268
[131]
GSM1019269
[132]
GSM1019270
[133]
GSM1019271
[134]
GSM1019272
[135]
GSM1019273
[136]
GSM1019274
[137]
GSM1019275
[138]
GSM1019276
[139]
GSM1019277
[140]
GSM1019278
[141]
GSM1019279
[142]
GSM1019280
[143]
GSM1019281
[144]
GSM1019282
[145]
GSM1019283
[146]
GSM1019284
[147]
GSM1019285
[148]
GSM1019286
[149]
GSM1019287
[150]
GSM1019288
[151]
GSM1019289
[152]
GSM1019290
[153]
GSM1019291
[154]
GSM1019292
[155]
GSM1019293
[156]
GSM1019294
[157]
GSM1019295
[158]
GSM1019296
[159]
GSM1019297
[160]
GSM1019298
[161]
GSM1019299
[162]
GSM1019300
[163]
GSM1019301
[164]
GSM1019302
[165]
GSM1019303
[166]
GSM1019304
[167]
GSM1019305
[168]
GSM1019306
[169]
GSM1019307
[170]
GSM1019308
[171]
GSM1019309
[172]
GSM1019310
[173]
GSM1019311
[174]
GSM1019312
[175]
GSM1019313
[176]
GSM1019314
[177]
GSM1019315
[178]
GSM1019316
[179]
GSM1019317
[180]
GSM1019318
[181]
GSM1019319
[182]
GSM1019320
[183]
GSM1019321
[184]
GSM1019322
[185]
GSM1019323
[186]
GSM1019324
[187]
GSM1019325
[188]
GSM1019326
[189]
GSM1019327
[190]
GSM1019328
[191]
GSM1019329
[192]
GSM1019330
[193]
GSM1019331
[194]
GSM1019332
[195]
GSM1019333
sample_count
196
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis and tissue microarray validation
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE68465
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=GSE68465
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE68465
version
null
note
The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.
access_statement_kind
data_availability_statement
sample_accessions array · 462 items
[0]
GSM1672281
[1]
GSM1672282
[2]
GSM1672283
[3]
GSM1672284
[4]
GSM1672285
[5]
GSM1672286
[6]
GSM1672287
[7]
GSM1672288
[8]
GSM1672289
[9]
GSM1672290
[10]
GSM1672291
[11]
GSM1672292
[12]
GSM1672293
[13]
GSM1672294
[14]
GSM1672295
[15]
GSM1672296
[16]
GSM1672297
[17]
GSM1672298
[18]
GSM1672299
[19]
GSM1672300
[20]
GSM1672301
[21]
GSM1672302
[22]
GSM1672303
[23]
GSM1672304
[24]
GSM1672305
[25]
GSM1672306
[26]
GSM1672307
[27]
GSM1672308
[28]
GSM1672309
[29]
GSM1672310
[30]
GSM1672311
[31]
GSM1672312
[32]
GSM1672313
[33]
GSM1672314
[34]
GSM1672315
[35]
GSM1672316
[36]
GSM1672317
[37]
GSM1672318
[38]
GSM1672319
[39]
GSM1672320
[40]
GSM1672321
[41]
GSM1672322
[42]
GSM1672323
[43]
GSM1672324
[44]
GSM1672325
[45]
GSM1672326
[46]
GSM1672327
[47]
GSM1672328
[48]
GSM1672329
[49]
GSM1672330
[50]
GSM1672331
[51]
GSM1672332
[52]
GSM1672333
[53]
GSM1672334
[54]
GSM1672335
[55]
GSM1672336
[56]
GSM1672337
[57]
GSM1672338
[58]
GSM1672339
[59]
GSM1672340
[60]
GSM1672341
[61]
GSM1672342
[62]
GSM1672343
[63]
GSM1672344
[64]
GSM1672345
[65]
GSM1672346
[66]
GSM1672347
[67]
GSM1672348
[68]
GSM1672349
[69]
GSM1672350
[70]
GSM1672351
[71]
GSM1672352
[72]
GSM1672353
[73]
GSM1672354
[74]
GSM1672355
[75]
GSM1672356
[76]
GSM1672357
[77]
GSM1672358
[78]
GSM1672359
[79]
GSM1672360
[80]
GSM1672361
[81]
GSM1672362
[82]
GSM1672363
[83]
GSM1672364
[84]
GSM1672365
[85]
GSM1672366
[86]
GSM1672367
[87]
GSM1672368
[88]
GSM1672369
[89]
GSM1672370
[90]
GSM1672371
[91]
GSM1672372
[92]
GSM1672373
[93]
GSM1672374
[94]
GSM1672375
[95]
GSM1672376
[96]
GSM1672377
[97]
GSM1672378
[98]
GSM1672379
[99]
GSM1672380
[100]
GSM1672381
[101]
GSM1672382
[102]
GSM1672383
[103]
GSM1672384
[104]
GSM1672385
[105]
GSM1672386
[106]
GSM1672387
[107]
GSM1672388
[108]
GSM1672389
[109]
GSM1672390
[110]
GSM1672391
[111]
GSM1672392
[112]
GSM1672393
[113]
GSM1672394
[114]
GSM1672395
[115]
GSM1672396
[116]
GSM1672397
[117]
GSM1672398
[118]
GSM1672399
[119]
GSM1672400
[120]
GSM1672401
[121]
GSM1672402
[122]
GSM1672403
[123]
GSM1672404
[124]
GSM1672405
[125]
GSM1672406
[126]
GSM1672407
[127]
GSM1672408
[128]
GSM1672409
[129]
GSM1672410
[130]
GSM1672411
[131]
GSM1672412
[132]
GSM1672413
[133]
GSM1672414
[134]
GSM1672415
[135]
GSM1672416
[136]
GSM1672417
[137]
GSM1672418
[138]
GSM1672419
[139]
GSM1672420
[140]
GSM1672421
[141]
GSM1672422
[142]
GSM1672423
[143]
GSM1672424
[144]
GSM1672425
[145]
GSM1672426
[146]
GSM1672427
[147]
GSM1672428
[148]
GSM1672429
[149]
GSM1672430
[150]
GSM1672431
[151]
GSM1672432
[152]
GSM1672433
[153]
GSM1672434
[154]
GSM1672435
[155]
GSM1672436
[156]
GSM1672437
[157]
GSM1672438
[158]
GSM1672439
[159]
GSM1672440
[160]
GSM1672441
[161]
GSM1672442
[162]
GSM1672443
[163]
GSM1672444
[164]
GSM1672445
[165]
GSM1672446
[166]
GSM1672447
[167]
GSM1672448
[168]
GSM1672449
[169]
GSM1672450
[170]
GSM1672451
[171]
GSM1672452
[172]
GSM1672453
[173]
GSM1672454
[174]
GSM1672455
[175]
GSM1672456
[176]
GSM1672457
[177]
GSM1672458
[178]
GSM1672459
[179]
GSM1672460
[180]
GSM1672461
[181]
GSM1672462
[182]
GSM1672463
[183]
GSM1672464
[184]
GSM1672465
[185]
GSM1672466
[186]
GSM1672467
[187]
GSM1672468
[188]
GSM1672469
[189]
GSM1672470
[190]
GSM1672471
[191]
GSM1672472
[192]
GSM1672473
[193]
GSM1672474
[194]
GSM1672475
[195]
GSM1672476
[196]
GSM1672477
[197]
GSM1672478
[198]
GSM1672479
[199]
GSM1672480
[200]
GSM1672481
[201]
GSM1672482
[202]
GSM1672483
[203]
GSM1672484
[204]
GSM1672485
[205]
GSM1672486
[206]
GSM1672487
[207]
GSM1672488
[208]
GSM1672489
[209]
GSM1672490
[210]
GSM1672491
[211]
GSM1672492
[212]
GSM1672493
[213]
GSM1672494
[214]
GSM1672495
[215]
GSM1672496
[216]
GSM1672497
[217]
GSM1672498
[218]
GSM1672499
[219]
GSM1672500
[220]
GSM1672501
[221]
GSM1672502
[222]
GSM1672503
[223]
GSM1672504
[224]
GSM1672505
[225]
GSM1672506
[226]
GSM1672507
[227]
GSM1672508
[228]
GSM1672509
[229]
GSM1672510
[230]
GSM1672511
[231]
GSM1672512
[232]
GSM1672513
[233]
GSM1672514
[234]
GSM1672515
[235]
GSM1672516
[236]
GSM1672517
[237]
GSM1672518
[238]
GSM1672519
[239]
GSM1672520
[240]
GSM1672521
[241]
GSM1672522
[242]
GSM1672523
[243]
GSM1672524
[244]
GSM1672525
[245]
GSM1672526
[246]
GSM1672527
[247]
GSM1672528
[248]
GSM1672529
[249]
GSM1672530
[250]
GSM1672531
[251]
GSM1672532
[252]
GSM1672533
[253]
GSM1672534
[254]
GSM1672535
[255]
GSM1672536
[256]
GSM1672537
[257]
GSM1672538
[258]
GSM1672539
[259]
GSM1672540
[260]
GSM1672541
[261]
GSM1672542
[262]
GSM1672543
[263]
GSM1672544
[264]
GSM1672545
[265]
GSM1672546
[266]
GSM1672547
[267]
GSM1672548
[268]
GSM1672549
[269]
GSM1672550
[270]
GSM1672551
[271]
GSM1672552
[272]
GSM1672553
[273]
GSM1672554
[274]
GSM1672555
[275]
GSM1672556
[276]
GSM1672557
[277]
GSM1672558
[278]
GSM1672559
[279]
GSM1672560
[280]
GSM1672561
[281]
GSM1672562
[282]
GSM1672563
[283]
GSM1672564
[284]
GSM1672565
[285]
GSM1672566
[286]
GSM1672567
[287]
GSM1672568
[288]
GSM1672569
[289]
GSM1672570
[290]
GSM1672571
[291]
GSM1672572
[292]
GSM1672573
[293]
GSM1672574
[294]
GSM1672575
[295]
GSM1672576
[296]
GSM1672577
[297]
GSM1672578
[298]
GSM1672579
[299]
GSM1672580
[300]
GSM1672581
[301]
GSM1672582
[302]
GSM1672583
[303]
GSM1672584
[304]
GSM1672585
[305]
GSM1672586
[306]
GSM1672587
[307]
GSM1672588
[308]
GSM1672589
[309]
GSM1672590
[310]
GSM1672591
[311]
GSM1672592
[312]
GSM1672593
[313]
GSM1672594
[314]
GSM1672595
[315]
GSM1672596
[316]
GSM1672597
[317]
GSM1672598
[318]
GSM1672599
[319]
GSM1672600
[320]
GSM1672601
[321]
GSM1672602
[322]
GSM1672603
[323]
GSM1672604
[324]
GSM1672605
[325]
GSM1672606
[326]
GSM1672607
[327]
GSM1672608
[328]
GSM1672609
[329]
GSM1672610
[330]
GSM1672611
[331]
GSM1672612
[332]
GSM1672613
[333]
GSM1672614
[334]
GSM1672615
[335]
GSM1672616
[336]
GSM1672617
[337]
GSM1672618
[338]
GSM1672619
[339]
GSM1672620
[340]
GSM1672621
[341]
GSM1672622
[342]
GSM1672623
[343]
GSM1672624
[344]
GSM1672625
[345]
GSM1672626
[346]
GSM1672627
[347]
GSM1672628
[348]
GSM1672629
[349]
GSM1672630
[350]
GSM1672631
[351]
GSM1672632
[352]
GSM1672633
[353]
GSM1672634
[354]
GSM1672635
[355]
GSM1672636
[356]
GSM1672637
[357]
GSM1672638
[358]
GSM1672639
[359]
GSM1672640
[360]
GSM1672641
[361]
GSM1672642
[362]
GSM1672643
[363]
GSM1672644
[364]
GSM1672645
[365]
GSM1672646
[366]
GSM1672647
[367]
GSM1672648
[368]
GSM1672649
[369]
GSM1672650
[370]
GSM1672651
[371]
GSM1672652
[372]
GSM1672653
[373]
GSM1672654
[374]
GSM1672655
[375]
GSM1672656
[376]
GSM1672657
[377]
GSM1672658
[378]
GSM1672659
[379]
GSM1672660
[380]
GSM1672661
[381]
GSM1672662
[382]
GSM1672663
[383]
GSM1672664
[384]
GSM1672665
[385]
GSM1672666
[386]
GSM1672667
[387]
GSM1672668
[388]
GSM1672669
[389]
GSM1672670
[390]
GSM1672671
[391]
GSM1672672
[392]
GSM1672673
[393]
GSM1672674
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title
caArray_jacob-00182: Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35614936", "kind": "science-record", "revision": 4, "content_hash": "sha256:8e84cc96c714cdc9a6bb7db0154bbbb97bdd6140761e06dc35d465959b0fd432", "created_at": "2026-11-01T18:14:31Z", "updated_at": "2026-11-01T22:49:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients.", "doi": "10.3389/fphar.2022.818170", "pmid": "35614936", "authors": ["Yuquan Bai", "Yun Pei", "Liang Xia", "Lin Ma", "Senyi Deng"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment. Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed. Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment. Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.", "claims": [{"id": "claim-method", "statement": "The “survival” and “survminer” R packages were used for further survival analysis. The Kaplan-Meier method was used to estimate the survival curve, and the log-rank test was used to analyze the difference in survival time.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35614936", "locator": "sections[5] \"2.3 Construction and Verification of Immune-Prognosis Index\" paragraph 2", "quote": "The “survival” and “survminer” R packages were used for further survival analysis. The Kaplan-Meier method was used to estimate the survival curve, and the log-rank test was used to analyze the difference in survival time."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35614936", "locator": "sections[15] \"Data Availability Statement\" paragraph 2", "quote": "The datasets analyzed during this research are available in TCGA (http://cancergenome.nih.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo, GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222) data bases. The data are also available from the corresponding author (senyi_deng@scu.edu.cn) on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The immune-prognosis index was prognostic in lung adenocarcinoma, with the IPI-high subgroup having a higher AUC and 5-year survival rate than the IPI-low subgroup (AUC = 0.725, p = 0.00389).", "kind": "quantitative", "target": {"value": 0.725, "unit": "auc", "tolerance": null, "source": "Grouping by the median of IPI score, we found that the AUC and 5-years survival rate of LUAD patients in the IPI-high subgroup was significantly higher than IPI-low subgroup (AUC = 0.725, p = 0.00389, Figures 2G,K)."}, "evidence": [{"asset": "source:pmid:35614936", "locator": "sections[9] \"3.2 Construction and Verification of Immune-Prognosis Index in Lung Adenocarcinoma\" paragraph 2", "quote": "Grouping by the median of IPI score, we found that the AUC and 5-years survival rate of LUAD patients in the IPI-high subgroup was significantly higher than IPI-low subgroup (AUC = 0.725, p = 0.00389, Figures 2G,K)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The immune-prognosis index was prognostic in lung adenocarcinoma, with the IPI-high subgroup having a higher AUC and 5-year survival rate than the IPI-low subgroup (AUC = 0.725, p = 0.00389).", "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:35614936"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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"resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE126044 not downloaded", "asset:data:geo:GSE126044 licence not read", "asset:data:geo:GSE135222 not downloaded", "asset:data:geo:GSE135222 licence not read", "asset:data:geo:GSE30219 not downloaded", "asset:data:geo:GSE30219 licence not read", "asset:data:geo:GSE37745 not downloaded", "asset:data:geo:GSE37745 licence not read", "asset:data:geo:GSE68465 not downloaded", "asset:data:geo:GSE68465 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "The “survival” and “survminer” R packages were used for further survival analysis. The Kaplan-Meier method was used to estimate the survival curve, and the log-rank test was used to analyze the difference in survival time."}}, {"id": "method-2", "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": 0.7, "band": "medium", "quote": "COX regression"}}, {"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"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.725, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. 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