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pmid:34887861

S100A2 Is a Prognostic Biomarker Involved in Immune Infiltration and Predict Immunotherapy Response in Pancreatic Cancer.

Yuan Chen · Chengcheng Wang · Jianlu Song · Ruiyuan Xu · Rexiati Ruze · Yupei Zhao

Frontiers in immunology · 2021 doi:10.3389/fimmu.2021.758004

Paper

complete

Experiment

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Data

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Analysis

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title
S100A2 Is a Prognostic Biomarker Involved in Immune Infiltration and Predict Immunotherapy Response in Pancreatic Cancer.
doi
10.3389/fimmu.2021.758004
pmid
34887861
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Yuan Chen
[1]
Chengcheng Wang
[2]
Jianlu Song
[3]
Ruiyuan Xu
[4]
Rexiati Ruze
[5]
Yupei Zhao
journal
Frontiers in immunology
year
2021
abstract
Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. It is of great significance to construct an effective prognostic signature of PC and find the novel biomarker for the optimization of the clinical decision-making. Due to the crucial role of immunity in tumor development, a prognostic model based on nine immune-related genes was constructed, which was proved to be effective in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set, GSE78229 set, and GSE62452 set. Furthermore, S100A2 (S100 Calcium Binding Protein A2) was identified as the gene occupying the most paramount position in risk model. Gene set enrichment analysis (GSEA), ESTIMATE and CIBERSORT algorithm revealed that S100A2 was closely associated with the immune status in PC microenvironment, mainly related to lower proportion of CD8+T cells and activated NK cells and higher proportion of M0 macrophages. Meanwhile, patients with high S100A2 expression might get more benefit from immunotherapy according to immunophenoscore algorithm. Afterwards, our independent cohort was also used to demonstrate S100A2 was an unfavorable marker of PC, as well as its remarkably positive correlation with the expression of PD-L1. In conclusion, our results demonstrate S100A2 might be responsible for the preservation of immune-suppressive status in PC microenvironment, which was identified with significant potentiality in predicting prognosis and immunotherapy response in PC patients.
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Subsequently, Least absolute shrinkage and selection operator (LASSO) regression analysis was further used to screen out the optimal gene combination for constructing the risk signature.
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sections[4] "Construction and Validation of a Risk Signature Associated With Survival of PC Patients" paragraph 2
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Subsequently, Least absolute shrinkage and selection operator (LASSO) regression analysis was further used to screen out the optimal gene combination for constructing the risk signature.
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
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The study constructed and validated a nine-immune-related-gene prognostic risk signature for pancreatic cancer and identified S100A2 as the gene occupying the most paramount position in the model.
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sections[1] "Introduction" paragraph 4
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In the present study, we constructed a prognostic model based on nine IRGs and the corresponding nomogram, which were proved to be an independent risk factor and was validated in the training set, testing set, entire set, GSE78229 set and GSE62452 set.
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sections[1] "Introduction" paragraph 4
quote
S100A2 (S100 Calcium Binding Protein A2), a highly conserved elongation factor (EF)-hand calcium-binding protein, was identified as the gene occupying the most paramount position in the risk signature.
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GSM1527188
[84]
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[85]
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[86]
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[87]
GSM1527192
[88]
GSM1527193
[89]
GSM1527194
[90]
GSM1527195
[91]
GSM1527196
[92]
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[93]
GSM1527198
[94]
GSM1527199
[95]
GSM1527200
[96]
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[97]
GSM1527202
[98]
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[99]
GSM1527204
[100]
GSM1527205
[101]
GSM1527206
[102]
GSM1527207
[103]
GSM1527208
[104]
GSM1527209
[105]
GSM1527210
[106]
GSM1527211
[107]
GSM1527212
[108]
GSM1527213
[109]
GSM1527214
[110]
GSM1527215
[111]
GSM1527216
[112]
GSM1527217
[113]
GSM1527218
[114]
GSM1527219
[115]
GSM1527220
[116]
GSM1527221
[117]
GSM1527222
[118]
GSM1527223
[119]
GSM1527224
[120]
GSM1527225
[121]
GSM1527226
[122]
GSM1527227
[123]
GSM1527228
[124]
GSM1527229
[125]
GSM1527230
[126]
GSM1527231
[127]
GSM1527232
[128]
GSM1527233
[129]
GSM1527234
sample_count
130
group_sizes
null
group_sizes_basis
null
platform
6244
taxon
Homo sapiens
title
Microarray gene-expression profiles of 69 pancreatic tumors and 61 adjacent non-tumor tissue from patients with pancreatic ductal adenocarcinoma
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE71729
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=GSE71729
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE71729
version
null
note
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 357 items
[0]
GSM1843893
[1]
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[2]
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[3]
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[4]
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[5]
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[6]
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[7]
GSM1843900
[8]
GSM1843901
[9]
GSM1843902
[10]
GSM1843903
[11]
GSM1843904
[12]
GSM1843905
[13]
GSM1843906
[14]
GSM1843907
[15]
GSM1843908
[16]
GSM1843909
[17]
GSM1843910
[18]
GSM1843911
[19]
GSM1843912
[20]
GSM1843913
[21]
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[22]
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[23]
GSM1843916
[24]
GSM1843917
[25]
GSM1843918
[26]
GSM1843919
[27]
GSM1843920
[28]
GSM1843921
[29]
GSM1843922
[30]
GSM1843923
[31]
GSM1843924
[32]
GSM1843925
[33]
GSM1843926
[34]
GSM1843927
[35]
GSM1843928
[36]
GSM1843929
[37]
GSM1843930
[38]
GSM1843931
[39]
GSM1843932
[40]
GSM1843933
[41]
GSM1843934
[42]
GSM1843935
[43]
GSM1843936
[44]
GSM1843937
[45]
GSM1843938
[46]
GSM1843939
[47]
GSM1843940
[48]
GSM1843941
[49]
GSM1843942
[50]
GSM1843943
[51]
GSM1843944
[52]
GSM1843945
[53]
GSM1843946
[54]
GSM1843947
[55]
GSM1843948
[56]
GSM1843949
[57]
GSM1843950
[58]
GSM1843951
[59]
GSM1843952
[60]
GSM1843953
[61]
GSM1843954
[62]
GSM1843955
[63]
GSM1843956
[64]
GSM1843957
[65]
GSM1843958
[66]
GSM1843959
[67]
GSM1843960
[68]
GSM1843961
[69]
GSM1843962
[70]
GSM1843963
[71]
GSM1843964
[72]
GSM1843965
[73]
GSM1843966
[74]
GSM1843967
[75]
GSM1843968
[76]
GSM1843969
[77]
GSM1843970
[78]
GSM1843971
[79]
GSM1843972
[80]
GSM1843973
[81]
GSM1843974
[82]
GSM1843975
[83]
GSM1843976
[84]
GSM1843977
[85]
GSM1843978
[86]
GSM1843979
[87]
GSM1843980
[88]
GSM1843981
[89]
GSM1843982
[90]
GSM1843983
[91]
GSM1843984
[92]
GSM1843985
[93]
GSM1843986
[94]
GSM1843987
[95]
GSM1843988
[96]
GSM1843989
[97]
GSM1843990
[98]
GSM1843991
[99]
GSM1843992
[100]
GSM1843993
[101]
GSM1843994
[102]
GSM1843995
[103]
GSM1843996
[104]
GSM1843997
[105]
GSM1843998
[106]
GSM1843999
[107]
GSM1844000
[108]
GSM1844001
[109]
GSM1844002
[110]
GSM1844003
[111]
GSM1844004
[112]
GSM1844005
[113]
GSM1844006
[114]
GSM1844007
[115]
GSM1844008
[116]
GSM1844009
[117]
GSM1844010
[118]
GSM1844011
[119]
GSM1844012
[120]
GSM1844013
[121]
GSM1844014
[122]
GSM1844015
[123]
GSM1844016
[124]
GSM1844017
[125]
GSM1844018
[126]
GSM1844019
[127]
GSM1844020
[128]
GSM1844021
[129]
GSM1844022
[130]
GSM1844023
[131]
GSM1844024
[132]
GSM1844025
[133]
GSM1844026
[134]
GSM1844027
[135]
GSM1844028
[136]
GSM1844029
[137]
GSM1844030
[138]
GSM1844031
[139]
GSM1844032
[140]
GSM1844033
[141]
GSM1844034
[142]
GSM1844035
[143]
GSM1844036
[144]
GSM1844037
[145]
GSM1844038
[146]
GSM1844039
[147]
GSM1844040
[148]
GSM1844041
[149]
GSM1844042
[150]
GSM1844043
[151]
GSM1844044
[152]
GSM1844045
[153]
GSM1844046
[154]
GSM1844047
[155]
GSM1844049
[156]
GSM1844050
[157]
GSM1844051
[158]
GSM1844052
[159]
GSM1844053
[160]
GSM1844054
[161]
GSM1844055
[162]
GSM1844056
[163]
GSM1844057
[164]
GSM1844058
[165]
GSM1844059
[166]
GSM1844060
[167]
GSM1844061
[168]
GSM1844062
[169]
GSM1844063
[170]
GSM1844064
[171]
GSM1844065
[172]
GSM1844066
[173]
GSM1844067
[174]
GSM1844068
[175]
GSM1844069
[176]
GSM1844070
[177]
GSM1844071
[178]
GSM1844072
[179]
GSM1844073
[180]
GSM1844074
[181]
GSM1844075
[182]
GSM1844076
[183]
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[184]
GSM1844078
[185]
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[186]
GSM1844080
[187]
GSM1844081
[188]
GSM1844082
[189]
GSM1844083
[190]
GSM1844084
[191]
GSM1844085
[192]
GSM1844086
[193]
GSM1844088
[194]
GSM1844089
[195]
GSM1844090
[196]
GSM1844091
[197]
GSM1844092
[198]
GSM1844093
[199]
GSM1844094
[200]
GSM1844095
[201]
GSM1844096
[202]
GSM1844097
[203]
GSM1844098
[204]
GSM1844099
[205]
GSM1844100
[206]
GSM1844101
[207]
GSM1844102
[208]
GSM1844103
[209]
GSM1844104
[210]
GSM1844105
[211]
GSM1844106
[212]
GSM1844107
[213]
GSM1844108
[214]
GSM1844109
[215]
GSM1844110
[216]
GSM1844111
[217]
GSM1844112
[218]
GSM1844113
[219]
GSM1844114
[220]
GSM1844115
[221]
GSM1844116
[222]
GSM1844117
[223]
GSM1844118
[224]
GSM1844119
[225]
GSM1844120
[226]
GSM1844121
[227]
GSM1844122
[228]
GSM1844123
[229]
GSM1844124
[230]
GSM1844125
[231]
GSM1844126
[232]
GSM1844127
[233]
GSM1844128
[234]
GSM1844129
[235]
GSM1844130
[236]
GSM1844131
[237]
GSM1844132
[238]
GSM1844133
[239]
GSM1844134
[240]
GSM1844135
[241]
GSM1844136
[242]
GSM1844137
[243]
GSM1844138
[244]
GSM1844139
[245]
GSM1844140
[246]
GSM1844141
[247]
GSM1844142
[248]
GSM1844143
[249]
GSM1844144
[250]
GSM1844145
[251]
GSM1844146
[252]
GSM1844147
[253]
GSM1844148
[254]
GSM1844149
[255]
GSM1844150
[256]
GSM1844151
[257]
GSM1844152
[258]
GSM1844153
[259]
GSM1844154
[260]
GSM1844155
[261]
GSM1844156
[262]
GSM1844157
[263]
GSM1844158
[264]
GSM1844159
[265]
GSM1844160
[266]
GSM1844161
[267]
GSM1844162
[268]
GSM1844163
[269]
GSM1844164
[270]
GSM1844165
[271]
GSM1844166
[272]
GSM1844167
[273]
GSM1844168
[274]
GSM1844169
[275]
GSM1844170
[276]
GSM1844171
[277]
GSM1844172
[278]
GSM1844173
[279]
GSM1844174
[280]
GSM1844175
[281]
GSM1844176
[282]
GSM1844177
[283]
GSM1844178
[284]
GSM1844179
[285]
GSM1844180
[286]
GSM1844181
[287]
GSM1844182
[288]
GSM1844183
[289]
GSM1844184
[290]
GSM1844185
[291]
GSM1844186
[292]
GSM1844187
[293]
GSM1844188
[294]
GSM1844189
[295]
GSM1844190
[296]
GSM1844191
[297]
GSM1844192
[298]
GSM1844193
[299]
GSM1844194
[300]
GSM1844195
[301]
GSM1844196
[302]
GSM1844197
[303]
GSM1844198
[304]
GSM1844199
[305]
GSM1844200
[306]
GSM1844201
[307]
GSM1844202
[308]
GSM1844203
[309]
GSM1844204
[310]
GSM1844205
[311]
GSM1844206
[312]
GSM1844207
[313]
GSM1844208
[314]
GSM1844209
[315]
GSM1844210
[316]
GSM1844211
[317]
GSM1844212
[318]
GSM1844213
[319]
GSM1844214
[320]
GSM1844215
[321]
GSM1844216
[322]
GSM1844217
[323]
GSM1844218
[324]
GSM1844219
[325]
GSM1844220
[326]
GSM1844221
[327]
GSM1844222
[328]
GSM1844223
[329]
GSM1844224
[330]
GSM1844225
[331]
GSM1844226
[332]
GSM1844227
[333]
GSM1844228
[334]
GSM1844229
[335]
GSM1844230
[336]
GSM1844231
[337]
GSM1844232
[338]
GSM1844233
[339]
GSM1844234
[340]
GSM1844235
[341]
GSM1844236
[342]
GSM1844237
[343]
GSM1844238
[344]
GSM1844239
[345]
GSM1844240
[346]
GSM1844241
[347]
GSM1844242
[348]
GSM1844243
[349]
GSM1844244
[350]
GSM1844245
[351]
GSM1844246
[352]
GSM1844247
[353]
GSM1844248
[354]
GSM1844249
[355]
GSM1844250
[356]
GSM1844251
sample_count
357
group_sizes
null
group_sizes_basis
null
platform
20769
taxon
Homo sapiens
title
Virtual Microdissection of Pancreatic Ductal Adenocarcinoma Reveals Tumor and Stroma Subtypes.
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[6] object · 15 fields
id
data:geo:GSE78229
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=GSE78229
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE78229
version
null
note
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 50 items
[0]
GSM1527105
[1]
GSM1527109
[2]
GSM1527137
[3]
GSM1527139
[4]
GSM1527141
[5]
GSM1527145
[6]
GSM1527147
[7]
GSM1527149
[8]
GSM1527151
[9]
GSM1527153
[10]
GSM1527155
[11]
GSM1527157
[12]
GSM1527159
[13]
GSM1527161
[14]
GSM1527163
[15]
GSM1527165
[16]
GSM1527167
[17]
GSM1527169
[18]
GSM1527171
[19]
GSM1527175
[20]
GSM1527177
[21]
GSM1527179
[22]
GSM1527181
[23]
GSM1527183
[24]
GSM1527185
[25]
GSM1527189
[26]
GSM1527191
[27]
GSM1527193
[28]
GSM1527196
[29]
GSM1527198
[30]
GSM1527199
[31]
GSM1527200
[32]
GSM1527202
[33]
GSM1527204
[34]
GSM1527205
[35]
GSM1527207
[36]
GSM1527209
[37]
GSM1527212
[38]
GSM1527213
[39]
GSM1527215
[40]
GSM1527216
[41]
GSM1527218
[42]
GSM1527219
[43]
GSM1527220
[44]
GSM1527223
[45]
GSM1527225
[46]
GSM1527227
[47]
GSM1527228
[48]
GSM1527230
[49]
GSM1527232
sample_count
50
group_sizes
null
group_sizes_basis
null
platform
6244
taxon
Homo sapiens
title
Microarray gene-expression profiles of 50 pancreatic tumors tissue from patients with pancreatic ductal adenocarcinoma
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 12 items
[0]
asset:data:geo:GSE15471 not downloaded
[1]
asset:data:geo:GSE15471 licence not read
[2]
asset:data:geo:GSE28735 not downloaded
[3]
asset:data:geo:GSE28735 licence not read
[4]
asset:data:geo:GSE62165 not downloaded
[5]
asset:data:geo:GSE62165 licence not read
[6]
asset:data:geo:GSE62452 not downloaded
[7]
asset:data:geo:GSE62452 licence not read
[8]
asset:data:geo:GSE71729 not downloaded
[9]
asset:data:geo:GSE71729 licence not read
[10]
asset:data:geo:GSE78229 not downloaded
[11]
asset:data:geo:GSE78229 licence not read
analysis object · 4 fields
state
partial
methods array · 5 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
0.8
band
high
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
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
0.8
band
high
quote
Cox regression
[2] object · 10 fields
id
method-3
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
0.8
band
high
quote
Limma
[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
0.8
band
high
quote
ROC curve
[4] object · 10 fields
id
method-5
family
null
specific
null
label
Least absolute shrinkage and selection operator (LASSO) regression analysis
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Subsequently, Least absolute shrinkage and selection operator (LASSO) regression analysis was further used to screen out the optimal gene combination for constructing the risk signature.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 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 · 6 items
[0]
GSE15471
[1]
GSE28735
[2]
GSE62165
[3]
GSE62452
[4]
GSE78229
[5]
GSE71729
resolved_accessions array · 6 items
[0]
GSE15471
[1]
GSE28735
[2]
GSE62165
[3]
GSE62452
[4]
GSE78229
[5]
GSE71729
statements array · 1 item
[0]
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
basis
The paper names GSE15471, GSE28735, GSE62165, GSE62452, GSE78229, GSE71729 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]
survival_km
assets array · 2 items
[0]
source:pmid:34887861
[1]
data:geo:GSE15471
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
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tification. HPNE, PANC-1, T3M4 and MIACaPa-2 cell lines were cultured in high glucose Dulbecco’s modified Eagle’s
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sas, USA), BxPC-3, AsPC-1, SW1990, PATU 8988 cell lines were cultured in RPMI-1640 medium (CORNING, Manassas, USA)
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ING, Manassas, USA), and Capan-1 and CFPAC-1 cell lines were cultured in Iscove’s Modified Dulbecco Medium (IMDM;
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, PANC-1, T3M4 and MIACaPa-2 cell lines were cultured in high glucose Dulbecco’s modified Eagle’s med
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images of S100A2 and CD68/CD4 expression by immunohistochemistry in HPA database.Click here for additional da
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Complete file
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It is of great significance to construct an effective prognostic signature of PC and find the novel biomarker for the optimization of the clinical decision-making. Due to the crucial role of immunity in tumor development, a prognostic model based on nine immune-related genes was constructed, which was proved to be effective in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set, GSE78229 set, and GSE62452 set. Furthermore, S100A2 (S100 Calcium Binding Protein A2) was identified as the gene occupying the most paramount position in risk model. Gene set enrichment analysis (GSEA), ESTIMATE and CIBERSORT algorithm revealed that S100A2 was closely associated with the immune status in PC microenvironment, mainly related to lower proportion of CD8+T cells and activated NK cells and higher proportion of M0 macrophages. Meanwhile, patients with high S100A2 expression might get more benefit from immunotherapy according to immunophenoscore algorithm. Afterwards, our independent cohort was also used to demonstrate S100A2 was an unfavorable marker of PC, as well as its remarkably positive correlation with the expression of PD-L1. In conclusion, our results demonstrate S100A2 might be responsible for the preservation of immune-suppressive status in PC microenvironment, which was identified with significant potentiality in predicting prognosis and immunotherapy response in PC patients.", "claims": [{"id": "claim-method", "statement": "Subsequently, Least absolute shrinkage and selection operator (LASSO) regression analysis was further used to screen out the optimal gene combination for constructing the risk signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34887861", "locator": "sections[4] \"Construction and Validation of a Risk Signature Associated With Survival of PC Patients\" paragraph 2", "quote": "Subsequently, Least absolute shrinkage and selection operator (LASSO) regression analysis was further used to screen out the optimal gene combination for constructing the risk signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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