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pmid:36386851

The types of tumor infiltrating lymphocytes are valuable for the diagnosis and prognosis of breast cancer.

Ying Sun · Chunyan Zhang

Frontiers in genetics · 2022 doi:10.3389/fgene.2022.1019062

Paper

complete

Experiment

complete

Data

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Analysis

complete

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The types of tumor infiltrating lymphocytes are valuable for the diagnosis and prognosis of breast cancer.
doi
10.3389/fgene.2022.1019062
pmid
36386851
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[0]
Ying Sun
[1]
Chunyan Zhang
journal
Frontiers in genetics
year
2022
abstract
This study aimed at constructing a diagnostic immune risk score (dIRS) system and a prognostic immune risk score (pIRS) system for diagnose and prognosis of breast cancer (BC). The gene expression data of BC were downloaded from TCGA dataset (training set), and from GSE65194, GSE29044, GSE42568, and GSE20685 (validation sets). Then, the immune cell type proportions in each dataset were assessed using EPIC tool, and the dIRS system was built based on the SVM-RFE and RF-VIMP algorithms. Subsequently, the pIRS system and the nomogram survival model were established separately using penalized and rms packages. Finally, the differential expressed genes (DEGs) between low and high pIRS groups were screened, and submitted for functional analysis. The dIRS system consisted of B cells, CD8 + T cells, endothelial cells, NK cells, and other cells had high accuracy in distinguishing BC patients from the healthy controls (AUROC >0.7). Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05). Based on age, pathologic stage and the pIRS values, the nomogram survival model was built. The AUROC value, Specificity value, Sensitivity value and C-index of the nomogram survival model were higher than 0.7000, and had a good predictive ability for BC. Finally, a total of 539 DEGs were identified, and significantly enriched in six pathways. The dIRS system and the pIRS system composed of immune cells might be critical for the diagnosis and prognosis of BC patients.
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Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells.
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Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells.
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sections[17] "Data availability statement" paragraph 2
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
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The study constructed dIRS and pIRS systems based on estimated immune-cell proportions, with the dIRS system showing AUROC values above 0.7 for distinguishing breast cancer patients from healthy controls and the low-pIRS group showing significantly better survival than the high-pIRS group.
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Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05).
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The dIRS system consisted of B cells, CD8 + T cells, endothelial cells, NK cells, and other cells had high accuracy in distinguishing BC patients from the healthy controls (AUROC >0.7).
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sections[0] "Abstract" paragraph 1
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Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05).
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The study constructed dIRS and pIRS systems based on estimated immune-cell proportions, with the dIRS system showing AUROC values above 0.7 for distinguishing breast cancer patients from healthy controls and the low-pIRS group showing significantly better survival than the high-pIRS group.
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[117]
GSM736484
[118]
GSM736485
[119]
GSM736486
[120]
GSM736487
[121]
GSM736488
[122]
GSM736489
[123]
GSM736490
[124]
GSM740839
[125]
GSM740840
[126]
GSM740841
[127]
GSM740842
[128]
GSM740843
[129]
GSM740844
[130]
GSM740845
[131]
GSM740846
[132]
GSM740847
[133]
GSM740848
[134]
GSM740849
[135]
GSM740850
[136]
GSM740851
[137]
GSM740852
sample_count
138
group_sizes
null
group_sizes_basis
null
platform
570;5175
taxon
Homo sapiens
title
Expression data from breast tumors in different age-specific cohorts and for different sequentional disease stages
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:GSE42568
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=GSE42568
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE42568
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 in the article/Supplementary Material.
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 in the article/Supplementary Material.
access_statement_kind
data_availability_statement
sample_accessions array · 121 items
[0]
GSM1045191
[1]
GSM1045192
[2]
GSM1045193
[3]
GSM1045194
[4]
GSM1045195
[5]
GSM1045196
[6]
GSM1045197
[7]
GSM1045198
[8]
GSM1045199
[9]
GSM1045200
[10]
GSM1045201
[11]
GSM1045202
[12]
GSM1045203
[13]
GSM1045204
[14]
GSM1045205
[15]
GSM1045206
[16]
GSM1045207
[17]
GSM1045208
[18]
GSM1045209
[19]
GSM1045210
[20]
GSM1045211
[21]
GSM1045212
[22]
GSM1045213
[23]
GSM1045214
[24]
GSM1045215
[25]
GSM1045216
[26]
GSM1045217
[27]
GSM1045218
[28]
GSM1045219
[29]
GSM1045220
[30]
GSM1045221
[31]
GSM1045222
[32]
GSM1045223
[33]
GSM1045224
[34]
GSM1045225
[35]
GSM1045226
[36]
GSM1045227
[37]
GSM1045228
[38]
GSM1045229
[39]
GSM1045230
[40]
GSM1045231
[41]
GSM1045232
[42]
GSM1045233
[43]
GSM1045234
[44]
GSM1045235
[45]
GSM1045236
[46]
GSM1045237
[47]
GSM1045238
[48]
GSM1045239
[49]
GSM1045240
[50]
GSM1045241
[51]
GSM1045242
[52]
GSM1045243
[53]
GSM1045244
[54]
GSM1045245
[55]
GSM1045246
[56]
GSM1045247
[57]
GSM1045248
[58]
GSM1045249
[59]
GSM1045250
[60]
GSM1045251
[61]
GSM1045252
[62]
GSM1045253
[63]
GSM1045254
[64]
GSM1045255
[65]
GSM1045256
[66]
GSM1045257
[67]
GSM1045258
[68]
GSM1045259
[69]
GSM1045260
[70]
GSM1045261
[71]
GSM1045262
[72]
GSM1045263
[73]
GSM1045264
[74]
GSM1045265
[75]
GSM1045266
[76]
GSM1045267
[77]
GSM1045268
[78]
GSM1045269
[79]
GSM1045270
[80]
GSM1045271
[81]
GSM1045272
[82]
GSM1045273
[83]
GSM1045274
[84]
GSM1045275
[85]
GSM1045276
[86]
GSM1045277
[87]
GSM1045278
[88]
GSM1045279
[89]
GSM1045280
[90]
GSM1045281
[91]
GSM1045282
[92]
GSM1045283
[93]
GSM1045284
[94]
GSM1045285
[95]
GSM1045286
[96]
GSM1045287
[97]
GSM1045288
[98]
GSM1045289
[99]
GSM1045290
[100]
GSM1045291
[101]
GSM1045292
[102]
GSM1045293
[103]
GSM1045294
[104]
GSM1045295
[105]
GSM1045296
[106]
GSM1045297
[107]
GSM1045298
[108]
GSM1045299
[109]
GSM1045300
[110]
GSM1045301
[111]
GSM1045302
[112]
GSM1045303
[113]
GSM1045304
[114]
GSM1045305
[115]
GSM1045306
[116]
GSM1045307
[117]
GSM1045308
[118]
GSM1045309
[119]
GSM1045310
[120]
GSM1045311
sample_count
121
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Breast Cancer Gene Expression Analysis
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:GSE65194
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=GSE65194
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE65194
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 in the article/Supplementary Material.
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 in the article/Supplementary Material.
access_statement_kind
data_availability_statement
sample_accessions array · 178 items
[0]
GSM1588970
[1]
GSM1588971
[2]
GSM1588972
[3]
GSM1588973
[4]
GSM1588974
[5]
GSM1588975
[6]
GSM1588976
[7]
GSM1588977
[8]
GSM1588978
[9]
GSM1588979
[10]
GSM1588980
[11]
GSM1588981
[12]
GSM1588982
[13]
GSM1588983
[14]
GSM1588984
[15]
GSM1588985
[16]
GSM1588986
[17]
GSM1588987
[18]
GSM1588988
[19]
GSM1588989
[20]
GSM1588990
[21]
GSM1588991
[22]
GSM1588992
[23]
GSM1588993
[24]
GSM1588994
[25]
GSM1588995
[26]
GSM1588996
[27]
GSM1588997
[28]
GSM1588998
[29]
GSM1588999
[30]
GSM1589000
[31]
GSM1589001
[32]
GSM1589002
[33]
GSM1589003
[34]
GSM1589004
[35]
GSM1589005
[36]
GSM1589006
[37]
GSM1589007
[38]
GSM1589008
[39]
GSM1589009
[40]
GSM1589010
[41]
GSM1589011
[42]
GSM1589012
[43]
GSM1589013
[44]
GSM1589014
[45]
GSM1589015
[46]
GSM1589016
[47]
GSM1589017
[48]
GSM1589018
[49]
GSM1589019
[50]
GSM1589020
[51]
GSM1589021
[52]
GSM1589022
[53]
GSM1589023
[54]
GSM1589024
[55]
GSM1589025
[56]
GSM1589026
[57]
GSM1589027
[58]
GSM1589028
[59]
GSM1589029
[60]
GSM1589030
[61]
GSM1589031
[62]
GSM1589032
[63]
GSM1589033
[64]
GSM1589034
[65]
GSM1589035
[66]
GSM1589036
[67]
GSM1589037
[68]
GSM1589038
[69]
GSM1589039
[70]
GSM1589040
[71]
GSM1589041
[72]
GSM1589042
[73]
GSM1589043
[74]
GSM1589044
[75]
GSM1589045
[76]
GSM1589046
[77]
GSM1589047
[78]
GSM1589048
[79]
GSM1589049
[80]
GSM1589050
[81]
GSM1589051
[82]
GSM1589052
[83]
GSM1589053
[84]
GSM1589054
[85]
GSM1589055
[86]
GSM1589056
[87]
GSM1589057
[88]
GSM1589058
[89]
GSM1589059
[90]
GSM1589060
[91]
GSM1589061
[92]
GSM1589062
[93]
GSM1589063
[94]
GSM1589064
[95]
GSM1589065
[96]
GSM1589066
[97]
GSM1589067
[98]
GSM1589068
[99]
GSM1589069
[100]
GSM1589070
[101]
GSM1589071
[102]
GSM1589072
[103]
GSM1589073
[104]
GSM1589074
[105]
GSM1589075
[106]
GSM1589076
[107]
GSM1589077
[108]
GSM1589078
[109]
GSM1589079
[110]
GSM1589080
[111]
GSM1589081
[112]
GSM1589082
[113]
GSM1589083
[114]
GSM1589084
[115]
GSM1589085
[116]
GSM1589086
[117]
GSM1589087
[118]
GSM1589088
[119]
GSM1589089
[120]
GSM1589090
[121]
GSM1589091
[122]
GSM1589092
[123]
GSM1589093
[124]
GSM1589094
[125]
GSM1589095
[126]
GSM1589097
[127]
GSM1589098
[128]
GSM1589099
[129]
GSM1589101
[130]
GSM1589103
[131]
GSM1589105
[132]
GSM1589107
[133]
GSM1589109
[134]
GSM1589110
[135]
GSM1589111
[136]
GSM1589112
[137]
GSM1589113
[138]
GSM1589114
[139]
GSM1589115
[140]
GSM1589116
[141]
GSM1589117
[142]
GSM1589118
[143]
GSM1589119
[144]
GSM1589120
[145]
GSM1589121
[146]
GSM1589122
[147]
GSM1589123
[148]
GSM1589124
[149]
GSM1589125
[150]
GSM1589126
[151]
GSM1589127
[152]
GSM1589128
[153]
GSM1589129
[154]
GSM1589130
[155]
GSM1589131
[156]
GSM1589132
[157]
GSM1589133
[158]
GSM1589134
[159]
GSM1589135
[160]
GSM1589136
[161]
GSM1589137
[162]
GSM1589138
[163]
GSM1589139
[164]
GSM1589140
[165]
GSM1589141
[166]
GSM1589142
[167]
GSM1589143
[168]
GSM1589144
[169]
GSM1589145
[170]
GSM1589146
[171]
GSM1589147
[172]
GSM1589148
[173]
GSM1589149
[174]
GSM1589150
[175]
GSM1589151
[176]
GSM1589152
[177]
GSM1589153
sample_count
178
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression profiling of breast cancer samples from Institut Curie (Maire cohort) --Affy CDF
resolution_checked_at
2026-11-01T11:38:18Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 8 items
[0]
asset:data:geo:GSE20685 not downloaded
[1]
asset:data:geo:GSE20685 licence not read
[2]
asset:data:geo:GSE29044 not downloaded
[3]
asset:data:geo:GSE29044 licence not read
[4]
asset:data:geo:GSE42568 not downloaded
[5]
asset:data:geo:GSE42568 licence not read
[6]
asset:data:geo:GSE65194 not downloaded
[7]
asset:data:geo:GSE65194 licence not read
analysis object · 4 fields
state
complete
methods array · 4 items
[0] object · 10 fields
id
method-1
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
1
band
verbatim
quote
Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells.
[1] object · 10 fields
id
method-2
family
machine_learning
specific
random_forest
label
Machine learning
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
machine_learning_random_forest
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.8
band
high
quote
Random Forest
[2] object · 10 fields
id
method-3
family
machine_learning
specific
support_vector_machine
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
SVM
[3] object · 10 fields
id
method-4
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.7
band
medium
quote
AUROC
targets array · 1 item
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
classification_metrics
unit
p-value
tolerance
null
state
complete
note
The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
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]
survival_km
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: kaplan_meier.
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 · 4 items
[0]
GSE65194
[1]
GSE29044
[2]
GSE42568
[3]
GSE20685
resolved_accessions array · 4 items
[0]
GSE65194
[1]
GSE29044
[2]
GSE42568
[3]
GSE20685
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 in the article/Supplementary Material.
basis
The paper names GSE65194, GSE29044, GSE42568, GSE20685 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]
survival_km
assets array · 2 items
[0]
source:pmid:36386851
[1]
data:geo:GSE20685
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:GSE20685
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
2026-11-01T18:15:34Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
12587
completion_tokens
2829
cached_tokens
264
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:36386851
type
paper
label
pmid:36386851
[1] object · 3 fields
id
source:pmid:36386851
type
manuscript
label
PMID 36386851 full text
[2] object · 3 fields
id
data:geo:GSE20685
type
dataset
label
GEO GSE20685
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:36386851
type
paper
label
pmid:36386851
[1] object · 3 fields
id
source:pmid:36386851
type
manuscript
label
PMID 36386851 full text
[2] object · 3 fields
id
data:geo:GSE20685
type
dataset
label
GEO GSE20685
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-36386851
type
extraction
used array · 1 item
[0]
source:pmid:36386851
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE20685
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE20685
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:36386851
parent_hash
sha256:ecaaa4084e25512176a6d687c466c9641d341aada5b89e09cea05ba029273369
fork_of
null
gaps array · 12 items
[0]
asset:data:geo:GSE20685 licence not read
[1]
asset:data:geo:GSE20685 not downloaded
[2]
asset:data:geo:GSE29044 licence not read
[3]
asset:data:geo:GSE29044 not downloaded
[4]
asset:data:geo:GSE42568 licence not read
[5]
asset:data:geo:GSE42568 not downloaded
[6]
asset:data:geo:GSE65194 licence not read
[7]
asset:data:geo:GSE65194 not downloaded
[8]
execution.not_run
[9]
review.human_critique
[10]
review.independent_reader
[11]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
methylation_array
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.85
evidence array · 1 item
[0]
portions in each dataset were assessed using EPIC tool, and the dIRS system was built based on
order_basis
paper_text_order:466
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
survival_km
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells.
order_basis
consumes_earlier_output
id
assay:2
Complete file
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The gene expression data of BC were downloaded from TCGA dataset (training set), and from GSE65194, GSE29044, GSE42568, and GSE20685 (validation sets). Then, the immune cell type proportions in each dataset were assessed using EPIC tool, and the dIRS system was built based on the SVM-RFE and RF-VIMP algorithms. Subsequently, the pIRS system and the nomogram survival model were established separately using penalized and rms packages. Finally, the differential expressed genes (DEGs) between low and high pIRS groups were screened, and submitted for functional analysis. The dIRS system consisted of B cells, CD8 + T cells, endothelial cells, NK cells, and other cells had high accuracy in distinguishing BC patients from the healthy controls (AUROC >0.7). Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05). Based on age, pathologic stage and the pIRS values, the nomogram survival model was built. The AUROC value, Specificity value, Sensitivity value and C-index of the nomogram survival model were higher than 0.7000, and had a good predictive ability for BC. Finally, a total of 539 DEGs were identified, and significantly enriched in six pathways. The dIRS system and the pIRS system composed of immune cells might be critical for the diagnosis and prognosis of BC patients.", "claims": [{"id": "claim-method", "statement": "Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36386851", "locator": "sections[5] \"Diagnostic analysis\" paragraph 2", "quote": "Based on the quantitative evaluation proportions of immune cells in the training set (TCGA), Support Vector Machine (SVM)-Recursive Feature Elimination (RFE) and Random Forest Variable Importance (RF-VIMP) algorithms were used to screen the infiltration types of characteristic immune cells."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed dIRS and pIRS systems based on estimated immune-cell proportions, with the dIRS system showing AUROC values above 0.7 for distinguishing breast cancer patients from healthy controls and the low-pIRS group showing significantly better survival than the high-pIRS group.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05)."}, "evidence": [{"asset": "source:pmid:36386851", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The dIRS system consisted of B cells, CD8 + T cells, endothelial cells, NK cells, and other cells had high accuracy in distinguishing BC patients from the healthy controls (AUROC >0.7)."}, {"asset": "source:pmid:36386851", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed dIRS and pIRS systems based on estimated immune-cell proportions, with the dIRS system showing AUROC values above 0.7 for distinguishing breast cancer patients from healthy controls and the low-pIRS group showing significantly better survival than the high-pIRS group.", "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:36386851"], "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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