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pmid:33889524

A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma.

Qianli Ma · Yang Chen · Fei Xiao · Yang Hao · Zhiyi Song · Jin Zhang · Katsuhiro Okuda · Sang-Won Um · Mario Silva · Yoshihisa Shimada · Chaozeng Si · Chaoyang Liang

Translational lung cancer research · 2021 doi:10.21037/tlcr-21-223

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Experiment

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Data

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title
A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma.
doi
10.21037/tlcr-21-223
pmid
33889524
authors array · 12 items
[0]
Qianli Ma
[1]
Yang Chen
[2]
Fei Xiao
[3]
Yang Hao
[4]
Zhiyi Song
[5]
Jin Zhang
[6]
Katsuhiro Okuda
[7]
Sang-Won Um
[8]
Mario Silva
[9]
Yoshihisa Shimada
[10]
Chaozeng Si
[11]
Chaoyang Liang
journal
Translational lung cancer research
year
2021
abstract
BACKGROUND: Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD. METHODS: The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094). RESULTS: Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts. CONCLUSIONS: The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.
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The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.
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sections[6] "Relationship of estimate-stromal-immune scores and prognosis" paragraph 2
quote
The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.
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❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
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sections[3] "Population" paragraphs 1-4
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❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
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❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
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❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
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❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
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[315]
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[318]
GSM1855115
[319]
GSM1855116
[320]
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[321]
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[322]
GSM1855119
[323]
GSM1855120
[324]
GSM1855121
[325]
GSM1855122
[326]
GSM1855123
[327]
GSM1855124
[328]
GSM1855125
[329]
GSM1855126
[330]
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[331]
GSM1855128
[332]
GSM1855129
[333]
GSM1855130
[334]
GSM1855131
[335]
GSM1855132
[336]
GSM1855133
[337]
GSM1855134
[338]
GSM1855135
[339]
GSM1855136
[340]
GSM1855137
[341]
GSM1855138
[342]
GSM1855139
[343]
GSM1855140
[344]
GSM1855141
[345]
GSM1855142
[346]
GSM1855143
[347]
GSM1855144
[348]
GSM1855145
[349]
GSM1855146
[350]
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[351]
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[352]
GSM1855149
[353]
GSM1855150
[354]
GSM1855151
[355]
GSM1855152
[356]
GSM1855153
[357]
GSM1855154
[358]
GSM1855155
[359]
GSM1855156
[360]
GSM1855157
[361]
GSM1855158
[362]
GSM1855159
[363]
GSM1855160
[364]
GSM1855161
[365]
GSM1855162
[366]
GSM1855163
[367]
GSM1855164
[368]
GSM1855165
[369]
GSM1855166
[370]
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[371]
GSM1855168
[372]
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[373]
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[374]
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[375]
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[377]
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[378]
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[379]
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[380]
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[381]
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[382]
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[383]
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[384]
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[386]
GSM1855183
[387]
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[388]
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[389]
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[390]
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[391]
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[393]
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[394]
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[397]
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[399]
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[400]
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[401]
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[402]
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[403]
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[404]
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[405]
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[406]
GSM1855203
[407]
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[408]
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[409]
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[410]
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[411]
GSM1855208
[412]
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[413]
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[414]
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[415]
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[416]
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[417]
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[418]
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[419]
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[420]
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[422]
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[423]
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[424]
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[425]
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[441]
GSM1855238
sample_count
442
group_sizes
null
group_sizes_basis
null
platform
15048
taxon
Homo sapiens
title
KRAS mutation-associated gene expression, p53 and STK11 mutations, proliferation and immune surveillance in lung adenocarcinoma
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 16 fields
id
asset:table:pmid:33889524:1
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table
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jats-table
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application/vnd.jats.table+json
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null
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sha256:f67647fc8e32045f62ddeed9685217814ce0f607f450f726966f1c5985994521
bytes
3043
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verified
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read_table
reader_status
available
identifier
section[9]/table-wrap[1]#t1
version
null
note
Clinicopathologic characteristics of patients in different risk groups
structure object · 5 fields
pmcid
PMC8044489
section_index
9
section_title
Gene signature and risk evaluation model
row_count
43
column_count
7
table object · 8 fields
label
Table 1
caption
Clinicopathologic characteristics of patients in different risk groups
rows array · 43 items
[0] array · 7 items
[0]
Characteristics
[1]
Whole cohort (n=514)
[2]
Risk score =0 (n=124)
[3]
Risk score =1 (n=130)
[4]
Risk score =2 (n=139)
[5]
Risk score =3 (n=121)
[6]
P value
[1] array · 7 items
[0]
Age [median (IQR)]
[1]
66.00 [59.00, 72.25]
[2]
68.50 [61.00, 74.00]
[3]
68.00 [60.00, 74.25]
[4]
65.00 [57.00, 71.50]
[5]
62.00 [56.50, 70.00]
[6]
<0.001
[2] array · 7 items
[0]
Gender (%)
[1]
[2]
[3]
[4]
[5]
[6]
0.006
[3] array · 7 items
[0]
Female
[1]
274 (53.6)
[2]
82 (66.1)
[3]
68 (52.7)
[4]
71 (51.4)
[5]
53 (44.2)
[6]
[4] array · 7 items
[0]
Male
[1]
237 (46.4)
[2]
42 (33.9)
[3]
61 (47.3)
[4]
67 (48.6)
[5]
67 (55.8)
[6]
[5] array · 7 items
[0]
Stage (%)
[1]
[2]
[3]
[4]
[5]
[6]
<0.001
[6] array · 7 items
[0]
I
[1]
275 (54.3)
[2]
85 (70.2)
[3]
73 (57.5)
[4]
60 (43.5)
[5]
57 (47.5)
[6]
[7] array · 7 items
[0]
II
[1]
121 (23.9)
[2]
29 (24.0)
[3]
28 (22.0)
[4]
29 (21.0)
[5]
35 (29.2)
[6]
[8] array · 7 items
[0]
III
[1]
84(16.6)
[2]
6 (5.0)
[3]
18 (14.2)
[4]
40 (29.0)
[5]
20 (16.7)
[6]
[9] array · 7 items
[0]
IV
[1]
26 (5.1)
[2]
1 (0.8)
[3]
8 (6.3)
[4]
9 (6.5)
[5]
8 (6.7)
[6]
[10] array · 7 items
[0]
Survival (%)
[1]
[2]
[3]
[4]
[5]
[6]
0.023
[11] array · 7 items
[0]
Alive
[1]
327 (63.6)
[2]
92 (74.2)
[3]
84 (64.6)
[4]
81 (58.3)
[5]
70 (57.9)
[6]
[12] array · 7 items
[0]
Dead
[1]
187 (36.4)
[2]
32 (25.8)
[3]
46 (35.4)
[4]
58 (41.7)
[5]
51 (42.1)
[6]
[13] array · 7 items
[0]
Stage (%)*
[1]
[2]
[3]
[4]
[5]
[6]
<0.001
[14] array · 7 items
[0]
i
[1]
274 (53.6)
[2]
85 (68.5)
[3]
73 (56.6)
[4]
60 (43.5)
[5]
56 (46.7)
[6]
[15] array · 7 items
[0]
ii
[1]
120 (23.5)
[2]
29 (23.4)
[3]
28 (21.7)
[4]
28 (20.3)
[5]
35 (29.2)
[6]
[16] array · 7 items
[0]
iii
[1]
84 (16.4)
[2]
6 (4.8)
[3]
18 (14.0)
[4]
40 (29.0)
[5]
20 (16.7)
[6]
[17] array · 7 items
[0]
iv
[1]
26 (5.1)
[2]
1 (0.8)
[3]
8 (6.2)
[4]
9 (6.5)
[5]
8 (6.7)
[6]
[18] array · 7 items
[0]
M (%)*
[1]
[2]
[3]
[4]
[5]
[6]
0.322
[19] array · 7 items
[0]
M0
[1]
343 (67.1)
[2]
83 (66.9)
[3]
83 (64.3)
[4]
93 (67.4)
[5]
84 (70.0)
[6]
[20] array · 7 items
[0]
M1
[1]
25 (4.9)
[2]
1 (0.8)
[3]
7 (5.4)
[4]
9 (6.5)
[5]
8 (6.7)
[6]
[21] array · 7 items
[0]
Mx
[1]
139 (27.2)
[2]
38 (30.6)
[3]
39 (30.2)
[4]
35 (25.4)
[5]
27 (22.5)
[6]
[22] array · 7 items
[0]
N (%)*
[1]
[2]
[3]
[4]
[5]
[6]
<0.001
[23] array · 7 items
[0]
N0
[1]
328 (64.2)
[2]
91 (73.4)
[3]
87 (67.4)
[4]
74 (53.6)
[5]
76 (63.3)
[6]
[24] array · 7 items
[0]
N1
[1]
95 (18.6)
[2]
24 (19.4)
[3]
21 (16.3)
[4]
22 (15.9)
[5]
28 (23.3)
[6]
[25] array · 7 items
[0]
N2
[1]
74 (14.5)
[2]
4 (3.2)
[3]
15 (11.6)
[4]
39 (28.3)
[5]
16 (13.3)
[6]
[26] array · 7 items
[0]
N3
[1]
2 (0.4)
[2]
1 (0.8)
[3]
0 (0.0)
[4]
1 (0.7)
[5]
0 (0.0)
[6]
[27] array · 7 items
[0]
Nx
[1]
11 (2.2)
[2]
3 (2.4)
[3]
6 (4.7)
[4]
2 [1–4]
[5]
0 (0.0)
[6]
[28] array · 7 items
[0]
T (%)
[1]
[2]
[3]
[4]
[5]
[6]
0.002
[29] array · 7 items
[0]
T1
[1]
168 (32.9)
[2]
55 (44.4)
[3]
51 (39.5)
[4]
35 (25.4)
[5]
27 (22.5)
[6]
[30] array · 7 items
[0]
T2
[1]
276 (54.0)
[2]
60 (48.4)
[3]
61 (47.3)
[4]
87 (63.0)
[5]
68 (56.7)
[6]
[31] array · 7 items
[0]
T3
[1]
45 (8.8)
[2]
6 (4.8)
[3]
11 (8.5)
[4]
9 (6.5)
[5]
19 (15.8)
[6]
[32] array · 7 items
[0]
T4
[1]
19 (3.7)
[2]
3 (2.4)
[3]
4 (3.1)
[4]
6 (4.3)
[5]
6 (5.0)
[6]
[33] array · 7 items
[0]
Tx
[1]
3 (0.6)
[2]
0 (0.0)
[3]
2 [1–6]
[4]
1 (0.7)
[5]
0 (0.0)
[6]
[34] array · 7 items
[0]
Estimate score (%)
[1]
[2]
[3]
[4]
[5]
[6]
<0.001
[35] array · 7 items
[0]
High
[1]
257 (50.0)
[2]
111 (89.5)
[3]
82 (63.1)
[4]
49 (35.3)
[5]
15 (12.4)
[6]
[36] array · 7 items
[0]
Low
[1]
257 (50.0)
[2]
13 (10.5)
[3]
48 (36.9)
[4]
90 (64.7)
[5]
106 (87.6)
[6]
[37] array · 7 items
[0]
Immune score (%)
[1]
[2]
[3]
[4]
[5]
[6]
<0.001
[38] array · 7 items
[0]
High
[1]
453 (88.1)
[2]
124 (100.0)
[3]
128 (98.5)
[4]
122 (87.8)
[5]
79 (65.3)
[6]
[39] array · 7 items
[0]
Low
[1]
61 (11.9)
[2]
0 (0.0)
[3]
2 [1–5]
[4]
17 (12.2)
[5]
42 (34.7)
[6]
[40] array · 7 items
[0]
Stromal score (%)
[1]
[2]
[3]
[4]
[5]
[6]
0.003
[41] array · 7 items
[0]
High
[1]
26 (5.1)
[2]
13 (10.5)
[3]
8 (6.2)
[4]
4 (2.9)
[5]
1 (0.8)
[6]
[42] array · 7 items
[0]
Low
[1]
488 (94.9)
[2]
111 (89.5)
[3]
122 (93.8)
[4]
135 (97.1)
[5]
120 (99.2)
[6]
locator
section[9]/table-wrap[1]#t1
[4] object · 16 fields
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asset:table:pmid:33889524:2
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table
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jats-table
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application/vnd.jats.table+json
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section[15]/table-wrap[1]#t2
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note
Prognostic gene signature selection based on a robust likelihood- based survival model
structure object · 5 fields
pmcid
PMC8044489
section_index
15
section_title
Gene signature based on the estimate, stromal, and immune scores and risk stratification model for LUAD
row_count
20
column_count
4
table object · 8 fields
label
Table 2
caption
Prognostic gene signature selection based on a robust likelihood- based survival model
rows array · 20 items
[0] array · 4 items
[0]
Gene Symbol
[1]
Negative log-likelihood
[2]
AIC
[3]
Forward selection
[1] array · 4 items
[0]
CD74
[1]
973.92
[2]
1,949.83
[3]
Y
[2] array · 4 items
[0]
JCHAIN
[1]
971
[2]
1,946
[3]
Y
[3] array · 4 items
[0]
PTGDS
[1]
969.98
[2]
1,945.96
[3]
Y
[4] array · 4 items
[0]
HLA-DRA
[1]
969.95
[2]
1,947.9
[3]
-
[5] array · 4 items
[0]
HLA-DRB5
[1]
969.07
[2]
1,948.13
[3]
-
[6] array · 4 items
[0]
HLA-DOA
[1]
968.91
[2]
1,949.82
[3]
-
[7] array · 4 items
[0]
C7
[1]
968.89
[2]
1,951.78
[3]
-
[8] array · 4 items
[0]
PTPRC
[1]
968.89
[2]
1,953.77
[3]
-
[9] array · 4 items
[0]
HLA-DQA1
[1]
968.7
[2]
1,955.39
[3]
-
[10] array · 4 items
[0]
HLA-DMB
[1]
966.91
[2]
1,953.81
[3]
-
[11] array · 4 items
[0]
CHIT1
[1]
966.85
[2]
1,955.7
[3]
-
[12] array · 4 items
[0]
CPA3
[1]
965.89
[2]
1,955.78
[3]
-
[13] array · 4 items
[0]
CSF2RB
[1]
965.89
[2]
1,957.78
[3]
-
[14] array · 4 items
[0]
HLA-DPB1
[1]
965.86
[2]
1,959.72
[3]
-
[15] array · 4 items
[0]
CD79A
[1]
965.06
[2]
1,960.13
[3]
-
[16] array · 4 items
[0]
S100P
[1]
963.13
[2]
1,958.25
[3]
-
[17] array · 4 items
[0]
HLA-DQB2
[1]
962.83
[2]
1,959.66
[3]
-
[18] array · 4 items
[0]
HLA-DRB1
[1]
962.72
[2]
1,961.45
[3]
-
[19] array · 4 items
[0]
CD52
[1]
962.65
[2]
1,963.31
[3]
-
locator
section[15]/table-wrap[1]#t2
[5] object · 16 fields
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asset:table:pmid:33889524:3
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table
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jats-table
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section[16]/table-wrap[1]#t3
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note
Multivariate Cox regression analyses of risk factors for overall survival
structure object · 5 fields
pmcid
PMC8044489
section_index
16
section_title
Relationship of estimate-stromal-immune score-based risk group with overall survival in patients with LUAD
row_count
7
column_count
5
table object · 8 fields
label
Table 3
caption
Multivariate Cox regression analyses of risk factors for overall survival
rows array · 7 items
[0] array · 5 items
[0]
Characteristics
[1]
[2]
Hazard ratio
[3]
95% Cl
[4]
P value
[1] array · 5 items
[0]
Risk Score
[1]
1 vs. 0
[2]
1.31
[3]
(0.82–2.11)
[4]
0.262
[2] array · 5 items
[0]
[1]
2 vs. 0
[2]
1.41
[3]
(0.88–2.24)
[4]
0.151
[3] array · 5 items
[0]
[1]
3 vs. 0
[2]
1.84
[3]
(1.16–2.91)
[4]
0.009
[4] array · 5 items
[0]
Stage
[1]
II vs. 1
[2]
2.42
[3]
(1.68–3.48)
[4]
<0.001
[5] array · 5 items
[0]
[1]
III vs. 1
[2]
3.42
[3]
(2.30–5.08)
[4]
<0.001
[6] array · 5 items
[0]
[1]
IV vs. 1
[2]
3.53
[3]
(2.02–6.18)
[4]
<0.001
locator
section[16]/table-wrap[1]#t3
gaps array · 4 items
[0]
asset:data:geo:GSE68571 not downloaded
[1]
asset:data:geo:GSE68571 licence not read
[2]
asset:data:geo:GSE72094 not downloaded
[3]
asset:data:geo:GSE72094 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
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
1
band
verbatim
quote
The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.
[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
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
0.7
band
medium
quote
cox proportional hazards
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
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capability_id
capability:computational_analysis
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[3]
statistical_regression
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[5]
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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complete
tier
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nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
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required_operations array · 1 item
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logrank_test
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: log_rank_test.
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
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true
accessions array · 2 items
[0]
GSE68571
[1]
GSE72094
resolved_accessions array · 2 items
[0]
GSE68571
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GSE72094
statements array · 1 item
[0]
❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.
basis
The paper names GSE68571, GSE72094 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
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logrank_test
assets array · 5 items
[0]
source:pmid:33889524
[1]
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asset:table:pmid:33889524:2
[4]
asset:table:pmid:33889524:3
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run_analysis
must_build object · 5 fields
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readers array · 0 items
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data_gaps array · 1 item
[0]
data:geo:GSE68571
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
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2026-10-31T18:29:31Z
independence
single_reader
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verbatim
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null
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3
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gaps array · 1 item
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pmid:33889524
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paper
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pmid:33889524
[1] object · 3 fields
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source:pmid:33889524
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manuscript
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PMID 33889524 full text
[2] object · 3 fields
id
data:geo:GSE68571
type
dataset
label
GEO GSE68571
activities array · 3 items
[0] object · 4 fields
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classification
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paper
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pmid:33889524
[1] object · 3 fields
id
source:pmid:33889524
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manuscript
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PMID 33889524 full text
[2] object · 3 fields
id
data:geo:GSE68571
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dataset
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GEO GSE68571
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agent:corpus-assay-engine
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activity-read-33889524
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extraction
used array · 1 item
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agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE68571
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used array · 1 item
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agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
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agent:corpus-assay-engine
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software
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agent:minimax-reader
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dsl_catalogue_fingerprint
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gaps array · 8 items
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[3]
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dsl_catalogue
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evidence array · 1 item
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The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33889524", "kind": "science-record", "revision": 7, "content_hash": "sha256:0db6af1624135c9af44934c07dac3685f359fdb1b1bec5ab9c9a5f86006a3052", "created_at": "2026-11-01T15:41:33Z", "updated_at": "2026-11-01T21:25:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma.", "doi": "10.21037/tlcr-21-223", "pmid": "33889524", "authors": ["Qianli Ma", "Yang Chen", "Fei Xiao", "Yang Hao", "Zhiyi Song", "Jin Zhang", "Katsuhiro Okuda", "Sang-Won Um", "Mario Silva", "Yoshihisa Shimada", "Chaozeng Si", "Chaoyang Liang"], "journal": "Translational lung cancer research", "year": 2021, "abstract": "BACKGROUND: Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD.\nMETHODS: The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094).\nRESULTS: Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts.\nCONCLUSIONS: The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.", "claims": [{"id": "claim-method", "statement": "The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33889524", "locator": "sections[6] \"Relationship of estimate-stromal-immune scores and prognosis\" paragraph 2", "quote": "The Kaplan–Meier estimator was used to estimate the patients’ overall survival. The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33889524", "locator": "sections[3] \"Population\" paragraphs 1-4", "quote": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33889524"], "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:GSE68571"], "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:GSE68571"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33889524", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tlcr-21-223", "sha256": "sha256:216ea8519ce015fcd86333ba8c716a43643e283cc06c253dfed69ff69c7d8364", "bytes": 46806, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tlcr-21-223", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8044489", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE68571", "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=GSE68571", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE68571", "version": null, "note": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. Clinicopathological data for the corresponding patients were obtained including age, sex, pathologic stage, and survival outcome.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "❖ Derivation cohort: 514 LUAD samples from The Cancer Genome Atlas (TCGA);❖ Validation cohorts: external validation was operated in two independent cohorts from the Gene Expression Omnibus (GEO) database:96 LUAD samples from GSE68571 database;442 LUAD samples from GSE72094 database. 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Log-rank tests were used to compare the survival outcomes of the 2 groups."}}, {"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": "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 proportional hazards"}}], "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": ["logrank_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The derivation cohort was divided into 2 groups based on their ESTIMATE, stromal, and immune scores: maximally selected rank statistics were employed to identify the optimal score cutoff for classifying patients by each score. The R package “maxstat” was used during identification (16). Log-rank tests were used to compare the survival outcomes of the 2 groups."], "order_basis": "analysis_only", "id": "assay:1"}]}