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pmid:33081731

Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma.

Fujun Zhang · Yu Liu · Yixin Yang · Kai Yang

BMC cancer · 2020 doi:10.1186/s12885-020-07489-7

Paper

complete

Experiment

complete

Data

partial

Analysis

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pmid:33081731
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2026-11-01T15:41:13Z
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title
Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma.
doi
10.1186/s12885-020-07489-7
pmid
33081731
authors array · 4 items
[0]
Fujun Zhang
[1]
Yu Liu
[2]
Yixin Yang
[3]
Kai Yang
journal
BMC cancer
year
2020
abstract
BACKGROUND: Immune-related genes is closely related to the occurrence and prognosis of head and neck squamous cell carcinoma (HNSCC). At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. The prognosis of HNSCC is still poor currently, and it may be effective to predict the clinical outcome of HNSCC by immunogenic analysis. METHODS: RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), the MINiML format GSE65858 chip expression data was downloaded from NCBI, and immune-related genes was downloaded from the InnateDB database. Immune-related genes in 519 HNSC patients were integrated from TCGA dataset. By using multivariate COX analysis and Lasso regression, robust immune-related gene pairs (IRGPs) that predict clinical outcomes of HNSCC were identified. Finally, a risk prognostic model related to immune gene pair was established and verified by clinical features, test sets and GEO external validation set. RESULTS: A total of 699 IRGPs were significantly correlated with the prognosis of HNSCC patients. Fourteen robust IRGPs were finally obtained by Lasso regression and a prognostic risk prediction model was constructed. Risk score of each sample were calculated based on Risk models and divided into the high-risk group (Risk-H) and low Risk group (Risk-L). Risk models were able to stratify the risk in patients with TNM Stage, Age, gender, and smoking history, and the AUC > 0.65 in training set and test set, shows that 14-IRGPs signature in patients with HNSCC has excellent classification performance. In addition, 14-IRGPs had the highest average C index compared with the prognostic characteristics and T, N, and Age of the 3 previously reported HNSCC. CONCLUSION: This study constructed 14-IRGPs as a novel prognostic marker for predicting survival in HNSCC patients.
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title
Gene expression patterns and TP53 mutations are associated with HPV RNA status, lymph node metastasis, and survival in head and neck cancer
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Clinical information of data sets
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[0] array · 3 items
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[1]
TCGA dataset (n = 517)
[2]
GSE65858 (n = 270)
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[1]
<=60
[2]
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[3]
41
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[2]
229
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[2]
297
[3]
176
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94
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–
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[2]
37
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[2]
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[0]
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[3]
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Sample statistics for training set and validation set
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[0]
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[1]
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[2]
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[3]
132
[4]
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[2]
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[3]
149
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0.878
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113
[4]
0.004
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50
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33
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[1]
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[1]
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3
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[2]
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[3]
247
[4]
0.1
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[1]
16
[2]
7
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16
[3]
11
[4]
0.444
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[1]
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[2]
41
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[1]
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[2]
41
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[1]
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[2]
164
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[0]
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[1]
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55
[3]
62
[4]
0.511
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[2]
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section[4]/table-wrap[1]#Tab2
[4] object · 16 fields
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asset:table:pmid:33081731:3
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jats-table
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note
14 IRGPs associated with prognosis
structure object · 5 fields
pmcid
PMC7574345
section_index
13
section_title
Identification of IRGPs in patients with HNSCC
row_count
15
column_count
6
table object · 8 fields
label
Table 3
caption
14 IRGPs associated with prognosis
rows array · 15 items
[0] array · 6 items
[0]
IRGPs
[1]
Coef
[2]
P value
[3]
HR
[4]
Low.95.CI.
[5]
High.95.CI.
[1] array · 6 items
[0]
ARHGAP15_VS_CTSG
[1]
−0.0415
[2]
0.001833936
[3]
0.507
[4]
0.331
[5]
0.778
[2] array · 6 items
[0]
BTK_VS_CCR7
[1]
−0.0015
[2]
0.000471643
[3]
0.512
[4]
0.352
[5]
0.745
[3] array · 6 items
[0]
BTK_VS_CTSG
[1]
−0.0371
[2]
0.00101336
[3]
0.512
[4]
0.343
[5]
0.763
[4] array · 6 items
[0]
CAMK2A_VS_CLEC6A
[1]
−0.0728
[2]
0.001000155
[3]
0.335
[4]
0.175
[5]
0.642
[5] array · 6 items
[0]
CAMK2A_VS_MASP1
[1]
−0.1535
[2]
2.82E-05
[3]
0.452
[4]
0.312
[5]
0.656
[6] array · 6 items
[0]
CCL17_VS_SEMA3A
[1]
0.0242
[2]
0.00023524
[3]
2.019
[4]
1.388
[5]
2.935
[7] array · 6 items
[0]
CDKN2A_VS_CLTC
[1]
0.1432
[2]
0.000924265
[3]
2.866
[4]
1.537
[5]
5.344
[8] array · 6 items
[0]
DUSP16_VS_TRIM6
[1]
0.1202
[2]
7.15E-05
[3]
4.076
[4]
2.037
[5]
8.154
[9] array · 6 items
[0]
IKBKB_VS_TRIB3
[1]
0.007
[2]
0.001015171
[3]
1.879
[4]
1.290
[5]
2.738
[10] array · 6 items
[0]
MASP1_VS_SEMA3A
[1]
0.0033
[2]
0.000200249
[3]
2.122
[4]
1.427
[5]
3.154
[11] array · 6 items
[0]
MASP1_VS_TRIM6
[1]
0.0728
[2]
0.000288898
[3]
2.307
[4]
1.468
[5]
3.626
[12] array · 6 items
[0]
ORAI1_VS_SUGT1
[1]
0.0751
[2]
0.000457145
[3]
2.617
[4]
1.528
[5]
4.481
[13] array · 6 items
[0]
SEMA3A_VS_SLAMF1
[1]
−0.0663
[2]
0.000190953
[3]
0.459
[4]
0.305
[5]
0.691
[14] array · 6 items
[0]
STAP2_VS_TRIB3
[1]
0.1549
[2]
1.47E-05
[3]
5.593
[4]
2.567
[5]
12.186
locator
section[13]/table-wrap[1]#Tab3
[5] object · 16 fields
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asset:table:pmid:33081731:4
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table
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jats-table
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application/vnd.jats.table+json
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sha256:0ae3f2b08e487d394ec42c9f6ae208b68fa00dae9cfde95ffa56425a79033ce2
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read_table
reader_status
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section[16]/table-wrap[1]#Tab4
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null
note
Univariate and multivariate COX regression analysis identified clinical factors associated with prognosis
structure object · 5 fields
pmcid
PMC7574345
section_index
16
section_title
Univariate and multivariate analysis of 14-IRGPs signature
row_count
41
column_count
7
table object · 8 fields
label
Table 4
caption
Univariate and multivariate COX regression analysis identified clinical factors associated with prognosis
rows array · 41 items
[0] array · 3 items
[0]
Variables
[1]
Univariate analysis
[2]
Multivariable analysis
[1] array · 6 items
[0]
HR
[1]
95%CI of HR
[2]
P value
[3]
HR
[4]
95%CI of HR
[5]
P value
[2] array · 1 item
[0]
TCGA training datasets
[3] array · 1 item
[0]
14-IRGPs signature
[4] array · 7 items
[0]
Risk score (High/Low)
[1]
2.97
[2]
1.99–4.43
[3]
7.79E-08
[4]
2.53
[5]
1.54–4.13
[6]
0.0002
[5] array · 7 items
[0]
Age
[1]
1.02
[2]
1.01–1.04
[3]
0.009
[4]
1.02
[5]
0.99–1.04
[6]
0.0550
[6] array · 7 items
[0]
Gender (Male vs Female)
[1]
0.70
[2]
0.47–1.04
[3]
0.077
[4]
0.94
[5]
0.56–1.5
[6]
0.8236
[7] array · 7 items
[0]
T3/T4 vs T1/T2
[1]
0.97
[2]
0.66–1.43
[3]
0.895
[4]
0.43
[5]
0.24–0.74
[6]
0.0025
[8] array · 7 items
[0]
N1/N2/N3 VS N0
[1]
1.21
[2]
0.78–1.85
[3]
0.39
[4]
1.00
[5]
0.59–1.68
[6]
0.9985
[9] array · 7 items
[0]
Stage IV vs Stage I/ II/III
[1]
1.97
[2]
1.31–2.95
[3]
0.001
[4]
2.65
[5]
1.46–4.78
[6]
0.0012
[10] array · 7 items
[0]
G3/G4 vs G1/G2
[1]
1.16
[2]
0.76–1.77
[3]
0.491
[4]
0.94
[5]
0.57–1.52
[6]
0.7969
[11] array · 7 items
[0]
Smoking vs Non-smoking
[1]
0.95
[2]
0.59–1.52
[3]
8.35E-01
[4]
0.85
[5]
0.48–1.49
[6]
0.5707
[12] array · 1 item
[0]
TCGA validation datasets
[13] array · 1 item
[0]
14-IRGPs signature
[14] array · 7 items
[0]
Risk score (High/Low)
[1]
1.96
[2]
1.32–2.91
[3]
7.93E-04
[4]
1.72
[5]
1.12–2.62
[6]
0.0123
[15] array · 7 items
[0]
Age
[1]
1.02
[2]
0.99–1.04
[3]
0.064
[4]
1.02
[5]
0.99–1.03
[6]
0.1549
[16] array · 7 items
[0]
Gender (Male vs Female)
[1]
0.77
[2]
0.51–1.17
[3]
0.223
[4]
0.79
[5]
0.47–1.31
[6]
0.3577
[17] array · 7 items
[0]
T3/T4 vs T1/T2
[1]
1.75
[2]
1.13–2.71
[3]
0.011
[4]
2.05
[5]
1.17–3.59
[6]
0.0120
[18] array · 7 items
[0]
N1/N2/N3 VS N0
[1]
1.29
[2]
0.87–1.91
[3]
0.209
[4]
1.39
[5]
0.86–2.23
[6]
0.1674
[19] array · 7 items
[0]
Stage IV vs Stage I/ II/III
[1]
1.36
[2]
0.90–2.05
[3]
0.142
[4]
0.87
[5]
0.49–1.53
[6]
0.6379
[20] array · 7 items
[0]
G3/G4 vs G1/G2
[1]
1.22
[2]
0.77–1.93
[3]
0.393
[4]
1.20
[5]
0.74–1.93
[6]
0.4439
[21] array · 7 items
[0]
Smoking vs Non-smoking
[1]
1.33
[2]
0.82–2.15
[3]
0.247
[4]
1.34
[5]
0.77–2.31
[6]
0.2987
[22] array · 1 item
[0]
TCGA entire datasets
[23] array · 1 item
[0]
14-IRGPs signature
[24] array · 7 items
[0]
Risk score (High/Low)
[1]
2.54
[2]
1.92–3.35
[3]
6.51E-11
[4]
2.01
[5]
1.41–2.84
[6]
9.36E-05
[25] array · 7 items
[0]
Age
[1]
1.02
[2]
1.01–1.03
[3]
0.0013
[4]
1.02
[5]
1.01–1.04
[6]
0.0087
[26] array · 7 items
[0]
Gender (Male vs Female)
[1]
0.74
[2]
0.55–0.97
[3]
0.034
[4]
0.97
[5]
0.66–1.41
[6]
0.8830
[27] array · 7 items
[0]
T3/T4 vs T1/T2
[1]
1.28
[2]
0.96–1.70
[3]
0.093
[4]
0.72
[5]
0.47–1.09
[6]
0.1107
[28] array · 7 items
[0]
N1/N2/N3 VS N0
[1]
1.20
[2]
0.87–1.66
[3]
0.271
[4]
1.07
[5]
0.73–1.54
[6]
0.7276
[29] array · 7 items
[0]
Stage IV vs Stage I/ II/III
[1]
1.65
[2]
1.24–2.20
[3]
6.20E-04
[4]
1.85
[5]
1.21–2.82
[6]
0.0046
[30] array · 7 items
[0]
G3/G4 vs G1/G2
[1]
1.15
[2]
0.84–1.57
[3]
0.365
[4]
0.83
[5]
0.58–1.18
[6]
0.3067
[31] array · 7 items
[0]
Smoking vs Non-smoking
[1]
1.14
[2]
0.81–1.59
[3]
0.45
[4]
1.07
[5]
0.7–1.63
[6]
0.7539
[32] array · 1 item
[0]
GSE65858
[33] array · 1 item
[0]
14-IRGPs signature
[34] array · 7 items
[0]
Risk score (High/Low)
[1]
1.94
[2]
1.27–2.97
[3]
0.0021
[4]
1.90
[5]
1.23–2.92
[6]
0.0035
[35] array · 7 items
[0]
Age
[1]
1.03
[2]
1.01–1.05
[3]
0.0130
[4]
1.04
[5]
1.01–1.06
[6]
0.0036
[36] array · 7 items
[0]
Gender (Male vs Female)
[1]
1.05
[2]
0.62–1.77
[3]
0.8680
[4]
1.06
[5]
0.62–1.82
[6]
0.8378
[37] array · 7 items
[0]
T3/T4 vs T1/T2
[1]
2.92
[2]
1.81–4.72
[3]
1.23E-05
[4]
2.16
[5]
1.26–3.69
[6]
0.0049
[38] array · 7 items
[0]
N1/N2/N3 VS N0
[1]
2.14
[2]
1.38–3.32
[3]
0.0007
[4]
1.39
[5]
0.73–2.65
[6]
0.3216
[39] array · 7 items
[0]
Stage IV vs Stage I/ II/III
[1]
2.92
[2]
1.72–4.95
[3]
7.22E-05
[4]
1.55
[5]
0.68–3.55
[6]
0.2987
[40] array · 7 items
[0]
Smoking vs Non-smoking
[1]
0.94
[2]
0.55–1.59
[3]
0.8210
[4]
1.14
[5]
0.64–2.03
[6]
0.6493
locator
section[16]/table-wrap[1]#Tab4
[6] object · 16 fields
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asset:table:pmid:33081731:5
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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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sha256:7390049a08fb53dee72bd2d3cf7efb77e28249917daa127b0aa5ec41091eff63
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read_table
reader_status
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section[19]/table-wrap[1]#Tab5
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null
note
Comparision for 4 models and clinical features
structure object · 5 fields
pmcid
PMC7574345
section_index
19
section_title
14-IRGPs was compared with other signatures and clinical features
row_count
9
column_count
5
table object · 8 fields
label
Table 5
caption
Comparision for 4 models and clinical features
rows array · 9 items
[0] array · 5 items
[0]
Characteristics
[1]
C-index (95%CI)
[2]
1-year AUC (95%CI)
[3]
3-year AUC (95%CI)
[4]
5-year AUC (95%CI)
[1] array · 5 items
[0]
T
[1]
0.50 (0.426–0.583, 0.571)
[2]
0.53 (0.47–0.6)
[3]
0.52 (0.46–0.58)
[4]
0.6 (0.52–0.67)
[2] array · 5 items
[0]
N
[1]
0.53 (0.452–0.610,0.835)
[2]
0.52 (0.47–0.59)
[3]
0.53 (0.47–0.59)
[4]
0.45 (0.46–0.54)
[3] array · 5 items
[0]
Age
[1]
0.56 (0.490–0.626,0.0096)
[2]
0.62 (0.53–0.71)
[3]
0.55 (0.48–0.62)
[4]
0.51 (0.42–0.6)
[4] array · 5 items
[0]
AJCC stage
[1]
0.52 (0.434–0.600,0.412)
[2]
0.56 (0.5–0.63)
[3]
0.58 (0.52–0.64)
[4]
0.5 (0.41–0.59)
[5] array · 5 items
[0]
14-IGPS signature
[1]
0.78 (0.693–0.859,3.65E-08)
[2]
0.73 (0.66–0.8)
[3]
0.82 (0.77–0.87)
[4]
0.75 (0.66–0.82)
[6] array · 5 items
[0]
3-gene signature
[1]
0.67 (0.579–0.767, 0.002)
[2]
0.56 (0.48–0.63)
[3]
0.64 (0.57–0.71)
[4]
0.63 (0.52–0.74)
[7] array · 5 items
[0]
6-gene signature
[1]
0.65 (0.560–0.750, 7.07E-5)
[2]
0.56 (0.48–0.63)
[3]
0.63 (0.56–0.7)
[4]
0.71 (0.62–0.81)
[8] array · 5 items
[0]
3-gene signature
[1]
0.62 (0.519–0.715,0.043
[2]
0.59 (0.50–0.67)
[3]
0.58 (0.51–0.66)
[4]
0.51 (0.40–0.61)
locator
section[19]/table-wrap[1]#Tab5
gaps array · 2 items
[0]
asset:data:geo:GSE65858 not downloaded
[1]
asset:data:geo:GSE65858 licence not read
analysis object · 4 fields
state
complete
methods array · 4 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
reordered
quote
“The log-rank test and Cox regression analysis were conducted to evaluate overall survival difference between the low risk and high risk 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
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log rank
[3] object · 10 fields
id
method-4
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.8
band
high
quote
pathway enrichment
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 1 item
[0]
GSE65858
resolved_accessions array · 1 item
[0]
GSE65858
statements array · 1 item
[0]
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
basis
The paper names GSE65858 in NCBI Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 7 items
[0]
source:pmid:33081731
[1]
data:geo:GSE65858
[2]
asset:table:pmid:33081731:1
[3]
asset:table:pmid:33081731:2
[4]
asset:table:pmid:33081731:3
[5]
asset:table:pmid:33081731:4
[6]
asset:table:pmid:33081731:5
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[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE65858
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
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extraction_review
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MiniMax-M3.1-Flash
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minimax
provider_family
minimax
read_at
2026-10-31T18:23:36Z
independence
single_reader
evidence_check object · 2 fields
method
reordered
availability
verbatim
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3
usage object · 3 fields
prompt_tokens
32660
completion_tokens
1298
cached_tokens
784
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
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MiniMax-M3.1-Flash
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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
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null
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[0]
execution.not_run
provenance object · 7 fields
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[0] object · 3 fields
id
pmid:33081731
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paper
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pmid:33081731
[1] object · 3 fields
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source:pmid:33081731
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manuscript
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PMID 33081731 full text
[2] object · 3 fields
id
data:geo:GSE65858
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dataset
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GEO GSE65858
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[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
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[0] object · 3 fields
id
pmid:33081731
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paper
label
pmid:33081731
[1] object · 3 fields
id
source:pmid:33081731
type
manuscript
label
PMID 33081731 full text
[2] object · 3 fields
id
data:geo:GSE65858
type
dataset
label
GEO GSE65858
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-33081731
type
extraction
used array · 1 item
[0]
source:pmid:33081731
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE65858
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE65858
agent
agent:corpus-assay-engine
agents array · 2 items
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id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
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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:33081731
parent_hash
sha256:2a505f671aee14e1b1a797f86867e4d894ccc51689f1dce1c204f7cb2d64c1d6
fork_of
null
gaps array · 6 items
[0]
asset:data:geo:GSE65858 licence not read
[1]
asset:data:geo:GSE65858 not downloaded
[2]
execution.not_run
[3]
review.human_critique
[4]
review.independent_reader
[5]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
rna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
e of HNSCC by immunogenic analysis. METHODS: RNASeq and clinical follow-up information were down
order_basis
paper_text_order:494
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
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cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
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evidence array · 1 item
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“The log-rank test and Cox regression analysis were conducted to evaluate overall survival difference between the low risk and high risk groups.”
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33081731", "kind": "science-record", "revision": 7, "content_hash": "sha256:b9ad6acf7e70c11b5d1b3c9b432aefce7509d9c643871c22a6925520924f0a77", "created_at": "2026-11-01T15:41:13Z", "updated_at": "2026-11-01T22:45:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma.", "doi": "10.1186/s12885-020-07489-7", "pmid": "33081731", "authors": ["Fujun Zhang", "Yu Liu", "Yixin Yang", "Kai Yang"], "journal": "BMC cancer", "year": 2020, "abstract": "BACKGROUND: Immune-related genes is closely related to the occurrence and prognosis of head and neck squamous cell carcinoma (HNSCC). At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. The prognosis of HNSCC is still poor currently, and it may be effective to predict the clinical outcome of HNSCC by immunogenic analysis.\nMETHODS: RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), the MINiML format GSE65858 chip expression data was downloaded from NCBI, and immune-related genes was downloaded from the InnateDB database. Immune-related genes in 519 HNSC patients were integrated from TCGA dataset. By using multivariate COX analysis and Lasso regression, robust immune-related gene pairs (IRGPs) that predict clinical outcomes of HNSCC were identified. Finally, a risk prognostic model related to immune gene pair was established and verified by clinical features, test sets and GEO external validation set.\nRESULTS: A total of 699 IRGPs were significantly correlated with the prognosis of HNSCC patients. Fourteen robust IRGPs were finally obtained by Lasso regression and a prognostic risk prediction model was constructed. Risk score of each sample were calculated based on Risk models and divided into the high-risk group (Risk-H) and low Risk group (Risk-L). Risk models were able to stratify the risk in patients with TNM Stage, Age, gender, and smoking history, and the AUC > 0.65 in training set and test set, shows that 14-IRGPs signature in patients with HNSCC has excellent classification performance. In addition, 14-IRGPs had the highest average C index compared with the prognostic characteristics and T, N, and Age of the 3 previously reported HNSCC.\nCONCLUSION: This study constructed 14-IRGPs as a novel prognostic marker for predicting survival in HNSCC patients.", "claims": [{"id": "claim-data-access", "statement": "The datasets used and analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33081731", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The datasets used and analysed during the current study are available from the corresponding author on reasonable request."}], "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:33081731"], "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:GSE65858"], "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:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33081731", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-020-07489-7", "sha256": "sha256:60a3769e318a1f49bf50020823b2f76660acca63720946ffd993e944d5427d4a", "bytes": 37537, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-020-07489-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7574345", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE65858", "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=GSE65858", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65858", "version": null, "note": "The datasets used and analysed during the current study are available from the corresponding author on reasonable 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