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pmid:31921296

Identification and Validation of an Immune-Related RNA Signature to Predict Survival of Patients With Head and Neck Squamous Cell Carcinoma.

Shuo Wu · Xinyi Dai · Dielai Xie

Frontiers in genetics · 2019 doi:10.3389/fgene.2019.01252

Paper

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Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:31921296
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science-record
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2026-11-01T15:40:54Z
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2026-11-01T21:22:48Z
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paper object · 10 fields
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complete
title
Identification and Validation of an Immune-Related RNA Signature to Predict Survival of Patients With Head and Neck Squamous Cell Carcinoma.
doi
10.3389/fgene.2019.01252
pmid
31921296
authors array · 3 items
[0]
Shuo Wu
[1]
Xinyi Dai
[2]
Dielai Xie
journal
Frontiers in genetics
year
2019
abstract
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease characterized by different molecular subgroups and clinical features. Therefore, it is important to uncover reliable molecular biomarkers for distinguishing different risk patient subgroup. Here, we conducted a multi-omics analysis to examine the joint predictive power of a multi-type RNA signature in the prognosis of HNSCC patients through integration analysis of mRNA, miRNA, and lncRNA expression profiles and clinical data in a large number of HNSCC patients. A multi-type RNA signature (15SigRS) was constructed which can classify patients into the high-risk group and low-risk group with the significantly different outcome [hazard ratio (HR) = 2.718, 95% confidence interval (CI), 2.258-3.272, p < 0.001] in the discovery data set, and subsequently validated in the Cancer Genome Atlas (TCGA) testing data set (HR = 1.299, 95% CI, 1.170-1.442, p < 0.001) and another independent GSE65858 data set (HR = 1.077, 95% CI, 1.016-1.143, p = 0.013). Further multivariate Cox regression analysis and stratification analysis demonstrated the independence of predictive performance of the 15SigRS relative to conventional clinicopathological factors. Furthermore, the 15SigRS has a prior performance in prognostic prediction than other single RNA type-based signatures. Functional analysis suggested that the 15SigRS are involved in immune- or metabolism-related KEGG pathways. In summary, our study demonstrated the potential application of mixed RNA types as molecular markers for predicting the outcome of cancer patients.
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Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers.
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methodological
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sections[8] "Identification of Independent Survival-Related mRNAs, miRNAs, and lncRNAs" paragraph 2
quote
Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers.
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The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).
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methodological
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sections[16] "Data Availability Statement" paragraph 2
quote
The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).
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The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).
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The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).
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sample_count
270
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null
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10558
taxon
Homo sapiens
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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2026-11-01T15:39:55Z
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[2] object · 16 fields
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asset:table:pmid:31921296:1
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Summary of clinical characteristics of three HNSCC patient data sets in the study.
structure object · 5 fields
pmcid
PMC6915042
section_index
3
section_title
Patient Data Set
row_count
32
column_count
6
table object · 8 fields
label
Table 1
caption
Summary of clinical characteristics of three HNSCC patient data sets in the study.
rows array · 32 items
[0] array · 6 items
[0]
Characteristic
[1]
[2]
Discovery dataset (N = 245)
[3]
Validation dataset (N = 244)
[4]
TCGA dataset (N = 489)
[5]
GSE65858 dataset (N = 270)
[1] array · 6 items
[0]
Vital status, n (%)
[1]
Alive
[2]
150 (61.2)
[3]
128 (52.5)
[4]
278 (56.9)
[5]
176 (65.2)
[2] array · 6 items
[0]
[1]
Dead
[2]
95 (38.8)
[3]
116 (47.5)
[4]
211 (43.1)
[5]
94 (34.8)
[3] array · 6 items
[0]
Age (years), n (%)
[1]
> = 60
[2]
132 (53.9)
[3]
141 (57.8)
[4]
273 (55.8)
[5]
117 (49.3)
[4] array · 6 items
[0]
[1]
<60
[2]
113 (46.1)
[3]
103 (42.2)
[4]
216 (44.2)
[5]
153 (56.7)
[5] array · 6 items
[0]
Gender, n (%)
[1]
Female
[2]
64 (26.1)
[3]
66 (27.0)
[4]
130 (26.6)
[5]
47 (17.4)
[6] array · 6 items
[0]
[1]
Male
[2]
181 (73.9)
[3]
178 (73.0)
[4]
359 (73.4)
[5]
223 (82.6)
[7] array · 6 items
[0]
Stage, n (%)
[1]
Stage I/II
[2]
57 (23.3)
[3]
37 (15.2)
[4]
94 (19.2)
[5]
55 (20.4)
[8] array · 6 items
[0]
[1]
Stage III/IV
[2]
157 (64.1)
[3]
171 (70.1)
[4]
328 (67.1)
[5]
215 (79.6)
[9] array · 6 items
[0]
[1]
NA
[2]
31 (12.6)
[3]
36 (14.7)
[4]
67 (13.7)
[5]
[10] array · 6 items
[0]
Grade, n (%)
[1]
G1
[2]
29 (11.8)
[3]
32 (13.1)
[4]
61 (12.5)
[5]
[11] array · 6 items
[0]
[1]
G2
[2]
147 (60)
[3]
144 (59.0)
[4]
291 (59.5)
[5]
[12] array · 6 items
[0]
[1]
G3
[2]
59 (24.1)
[3]
58 (23.8)
[4]
117 (23.9)
[5]
[13] array · 6 items
[0]
[1]
NA
[2]
10 (4.1)
[3]
10 (4.1)
[4]
20 (4.1)
[5]
[14] array · 6 items
[0]
Race, n (%)
[1]
White
[2]
201 (82)
[3]
216 (88.5)
[4]
417 (85.3)
[5]
[15] array · 5 items
[0]
[1]
Other_race
[2]
34 (13.9)
[3]
24 (9.8)
[4]
58 (11.9)
[16] array · 6 items
[0]
[1]
NA
[2]
10 (4.1)
[3]
4 (1.7)
[4]
14 (2.9)
[5]
[17] array · 6 items
[0]
ANGIOLYMPHATIC_INVASION, n (%)
[1]
Yes
[2]
54 (22)
[3]
61 (25)
[4]
115 (23.5)
[5]
[18] array · 6 items
[0]
[1]
No
[2]
112 (45.7)
[3]
104 (42.6)
[4]
216 (44.2)
[5]
[19] array · 6 items
[0]
[1]
NA
[2]
79 (32.2)
[3]
79 (32.4)
[4]
158 (32.3)
[5]
[20] array · 6 items
[0]
PERINEURAL_INVASION, n (%)
[1]
Yes
[2]
72 (29.4)
[3]
86 (35.2)
[4]
158 (32.3)
[5]
[21] array · 6 items
[0]
[1]
No
[2]
98 (40)
[3]
86 (35.2)
[4]
184 (37.6)
[5]
[22] array · 6 items
[0]
[1]
NA
[2]
75 (30.6)
[3]
72 (29.5)
[4]
147 (30.1)
[5]
[23] array · 6 items
[0]
Smoking_pack_years, n (%)
[1]
> = 40
[2]
82 (33.5)
[3]
78 (32.0)
[4]
160 (32.7)
[5]
222 (YES, 82.2)
[24] array · 6 items
[0]
[1]
<40
[2]
61 (24.9)
[3]
58 (23.8)
[4]
119 (24.3)
[5]
48 (NO, 17.8)
[25] array · 6 items
[0]
[1]
NA
[2]
102 (41.6)
[3]
108 (44.2)
[4]
210 (42.9)
[5]
[26] array · 6 items
[0]
ALCOHOL_HISTORY_DOCUMENTED, n (%)
[1]
Yes
[2]
159 (64.9)
[3]
165 (67.6)
[4]
324 (66.3)
[5]
[27] array · 6 items
[0]
[1]
No
[2]
81 (33.1)
[3]
73 (29.9)
[4]
154 (31.5)
[5]
[28] array · 6 items
[0]
[1]
NA
[2]
5 (2)
[3]
6 (2.5)
[4]
11 (2.2)
[5]
[29] array · 6 items
[0]
HPV_STATUS_P16, n (%)
[1]
Negative
[2]
37 (15.1)
[3]
32 (13.1)
[4]
69 (14.1)
[5]
[30] array · 6 items
[0]
[1]
Positive
[2]
15 (6.1)
[3]
15 (6.1)
[4]
30 (6.1)
[5]
[31] array · 6 items
[0]
[1]
NA
[2]
193 (78.8)
[3]
197 (80.8)
[4]
390 (79.8)
[5]
locator
section[3]/table-wrap[1]#T1
[3] object · 16 fields
id
asset:table:pmid:31921296:2
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:187e5b55b458844557f7b9cd03f407ef62a79cea7ac301d4a8f31e6c9dcc4e78
bytes
3828
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[10]/table-wrap[1]#T2
version
null
note
Univariate and multivariate Cox regression analysis of OS in each data set.
structure object · 5 fields
pmcid
PMC6915042
section_index
10
section_title
Independent Confirmation of the 15SigRs for Survival Prediction in the Validation Data Set and TCGA Data Set
row_count
47
column_count
7
table object · 8 fields
label
Table 2
caption
Univariate and multivariate Cox regression analysis of OS in each data set.
rows array · 47 items
[0] array · 3 items
[0]
Variable
[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]
Discovery data set (n = 245)
[3] array · 7 items
[0]
15SigRS
[1]
2.718
[2]
2.258–3.272
[3]
<0.001
[4]
2.562
[5]
1.999–3.284
[6]
<0.001
[4] array · 7 items
[0]
Age
[1]
1.043
[2]
1.024–1.063
[3]
0.000
[4]
1.016
[5]
0.991–1.042
[6]
0.218
[5] array · 7 items
[0]
Gender (male/female)
[1]
0.643
[2]
0.42–0.984
[3]
0.042
[4]
0.499
[5]
0.272–0.916
[6]
0.025
[6] array · 7 items
[0]
Stage (III&IV/I&II)
[1]
1.254
[2]
0.753–2.088
[3]
0.385
[4]
1.187
[5]
0.352–4.001
[6]
0.783
[7] array · 7 items
[0]
Grade (G2/G1)
[1]
1.362
[2]
0.703–2.640
[3]
0.360
[4]
1.685
[5]
0.584–4.861
[6]
0.334
[8] array · 7 items
[0]
Grade (G3/G1)
[1]
1.139
[2]
0.554–2.343
[3]
0.723
[4]
1.829
[5]
0.599–5.582
[6]
0.289
[9] array · 7 items
[0]
Race (White/other race)
[1]
0.578
[2]
0.329–1.014
[3]
0.056
[4]
1.623
[5]
0.679–3.880
[6]
0.276
[10] array · 7 items
[0]
ALCOHOL_HISTORY_DOCUMENTED (yes/no)
[1]
0.734
[2]
0.483–1.113
[3]
0.145
[4]
1.010
[5]
0.562–1.815
[6]
0.974
[11] array · 7 items
[0]
ANGIOLYMPHATIC_INVASION (yes/no)
[1]
1.687
[2]
0.988–2.881
[3]
0.056
[4]
[5]
[6]
[12] array · 7 items
[0]
PERINEURAL_INVASION (yes/no)
[1]
2.879
[2]
1.689–4.907
[3]
0.000
[4]
[5]
[6]
[13] array · 7 items
[0]
SMOKING_PACK_YEARS
[1]
1.001
[2]
0.995–1.008
[3]
0.737
[4]
[5]
[6]
[14] array · 1 item
[0]
Validation data set (n = 244)
[15] array · 7 items
[0]
15SigRS
[1]
1.299
[2]
1.170–1.442
[3]
<0.001
[4]
1.311
[5]
1.158–1.484
[6]
<0.001
[16] array · 7 items
[0]
Age
[1]
1.003
[2]
0.986–1.02
[3]
0.721
[4]
1.020
[5]
0.996–1.045
[6]
0.111
[17] array · 7 items
[0]
Gender (male/female)
[1]
0.904
[2]
0.605–1.352
[3]
0.624
[4]
1.063
[5]
0.606–1.866
[6]
0.832
[18] array · 7 items
[0]
Stage (III & IV/I & II)
[1]
3.294
[2]
1.595–6.805
[3]
0.001
[4]
3.102
[5]
0.569–16.905
[6]
0.191
[19] array · 7 items
[0]
Grade (G2/G1)
[1]
2.264
[2]
1.157–4.430
[3]
0.017
[4]
1.326
[5]
0.593–2.967
[6]
0.492
[20] array · 7 items
[0]
Grade (G3/G1)
[1]
1.927
[2]
0.936–3.964
[3]
0.075
[4]
1.248
[5]
0.520–2.993
[6]
0.620
[21] array · 7 items
[0]
Race (White/other race)
[1]
0.871
[2]
0.477–1.587
[3]
0.651
[4]
0.900
[5]
0.452–1.790
[6]
0.763
[22] array · 7 items
[0]
ALCOHOL_HISTORY_DOCUMENTED (yes/no)
[1]
1.185
[2]
0.788–1.78
[3]
0.415
[4]
1.536
[5]
0.882–2.675
[6]
0.130
[23] array · 7 items
[0]
ANGIOLYMPHATIC_INVASION (yes/no)
[1]
1.809
[2]
1.138–2.874
[3]
0.012
[4]
[5]
[6]
[24] array · 7 items
[0]
PERINEURAL_INVASION (yes/no)
[1]
1.698
[2]
1.061–2.716
[3]
0.027
[4]
[5]
[6]
[25] array · 7 items
[0]
SMOKING_PACK_YEARS
[1]
1.001
[2]
0.992–1.011
[3]
0.801
[4]
[5]
[6]
[26] array · 1 item
[0]
TCGA data set (n = 489)
[27] array · 7 items
[0]
15SigRS
[1]
1.496
[2]
1.393–1.606
[3]
<0.001
[4]
1.482
[5]
1.348–1.629
[6]
<0.001
[28] array · 7 items
[0]
Age
[1]
1.022
[2]
1.009–1.035
[3]
0.001
[4]
1.027
[5]
1.010–1.044
[6]
0.002
[29] array · 7 items
[0]
Gender (male/female)
[1]
0.759
[2]
0.568–1.016
[3]
0.064
[4]
0.786
[5]
0.526–1.174
[6]
0.239
[30] array · 7 items
[0]
Stage (III & IV/I & II)
[1]
1.812
[2]
1.216–2.701
[3]
0.003
[4]
1.846
[5]
0.768–4.437
[6]
0.171
[31] array · 7 items
[0]
Grade (G2/G1)
[1]
1.749
[2]
1.102–2.777
[3]
0.018
[4]
1.240
[5]
0.679–2.264
[6]
0.483
[32] array · 7 items
[0]
Grade (G3/G1)
[1]
1.507
[2]
0.913–2.487
[3]
0.109
[4]
1.441
[5]
0.754–2.754
[6]
0.269
[33] array · 7 items
[0]
Race (White/other race)
[1]
0.710
[2]
0.473–1.065
[3]
0.098
[4]
0.811
[5]
0.492–1.335
[6]
0.410
[34] array · 7 items
[0]
ALCOHOL_HISTORY_DOCUMENTED (yes/no)
[1]
0.951
[2]
0.712–1.27
[3]
0.734
[4]
1.165
[5]
0.792–1.714
[6]
0.437
[35] array · 7 items
[0]
ANGIOLYMPHATIC_INVASION (yes/no)
[1]
1.750
[2]
1.239–2.473
[3]
0.001
[4]
[5]
[6]
[36] array · 7 items
[0]
PERINEURAL_INVASION (yes/no)
[1]
2.222
[2]
1.563–3.16
[3]
0.000
[4]
[5]
[6]
[37] array · 7 items
[0]
SMOKING_PACK_YEARS
[1]
1.001
[2]
0.995–1.006
[3]
0.765
[4]
[5]
[6]
[38] array · 7 items
[0]
HPV_STATUS_P16 (yes/no)
[1]
0.504
[2]
0.172–1.477
[3]
0.212
[4]
[5]
[6]
[39] array · 1 item
[0]
GSE65858 data set (n = 270)
[40] array · 7 items
[0]
15SigRS
[1]
1.077
[2]
1.016–1.143
[3]
0.013
[4]
1.073
[5]
1.012–1.137
[6]
0.019
[41] array · 7 items
[0]
Age
[1]
1.037
[2]
1.006–1.048
[3]
0.012
[4]
1.03
[5]
1.007–1.053
[6]
0.01
[42] array · 7 items
[0]
Gender (male/female)
[1]
1.046
[2]
0.6174–1.771
[3]
0.868
[4]
1.026
[5]
0.602–1.749
[6]
0.923
[43] array · 7 items
[0]
Stage (II/I)
[1]
0.386
[2]
0.112–1.333
[3]
0.132
[4]
0.306
[5]
0.088–1.071
[6]
0.064
[44] array · 7 items
[0]
Stage (III/I & II)
[1]
0.459
[2]
0.1447–1.454
[3]
0.185
[4]
0.423
[5]
0.133–1.343
[6]
0.144
[45] array · 7 items
[0]
Stage (IV/I&)
[1]
1.495
[2]
0.603–3.705
[3]
0.385
[4]
1.339
[5]
0.537–3.336
[6]
0.531
[46] array · 7 items
[0]
SMOKING (yes/no)
[1]
0.941
[2]
0.555–1.595
[3]
0.821
[4]
1.294
[5]
0.733–2.284
[6]
0.373
locator
section[10]/table-wrap[1]#T2
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
verbatim
quote
Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers.
[1] object · 10 fields
id
method-2
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
[2] object · 10 fields
id
method-3
family
clustering
specific
hierarchical
label
Clustering
dsl_operations array · 1 item
[0]
hierarchical_cluster
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
feature_matrix
evidence object · 3 fields
score
0.8
band
high
quote
Hierarchical clustering
[3] object · 10 fields
id
method-4
family
contingency_analysis
specific
chi_square
label
Categorical (contingency table) analysis
dsl_operations array · 1 item
[0]
chi_square_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
categorical_cross_tabulation
evidence object · 3 fields
score
0.8
band
high
quote
chi-square
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 data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).
basis
The paper names GSE65858 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]
cox_ph
assets array · 4 items
[0]
source:pmid:31921296
[1]
data:geo:GSE65858
[2]
asset:table:pmid:31921296:1
[3]
asset:table:pmid:31921296:2
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:GSE65858
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
2026-10-31T18:17:43Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
19692
completion_tokens
1313
cached_tokens
785
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:31921296
type
paper
label
pmid:31921296
[1] object · 3 fields
id
source:pmid:31921296
type
manuscript
label
PMID 31921296 full text
[2] object · 3 fields
id
data:geo:GSE65858
type
dataset
label
GEO GSE65858
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:31921296
type
paper
label
pmid:31921296
[1] object · 3 fields
id
source:pmid:31921296
type
manuscript
label
PMID 31921296 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-31921296
type
extraction
used array · 1 item
[0]
source:pmid:31921296
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
[0] object · 3 fields
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agent:corpus-assay-engine
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software
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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:31921296
parent_hash
sha256:0f2abd5b25d10d70b86a2667d2e60a72b1fd18787074588de6b5f45e8495244e
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
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[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]
Materials and Methods Patient Data Set RNA-Seq data (HTSeq), miRNA expression data (Illumin
order_basis
paper_text_order:1739
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
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f2132e96ae6c7a4e
required_operations array · 1 item
[0]
cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers.
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:31921296", "kind": "science-record", "revision": 6, "content_hash": "sha256:a1c0610e474268a7ab4f03bd5822edb722fb070074065dbf16b134d8451c07aa", "created_at": "2026-11-01T15:40:54Z", "updated_at": "2026-11-01T21:22:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of an Immune-Related RNA Signature to Predict Survival of Patients With Head and Neck Squamous Cell Carcinoma.", "doi": "10.3389/fgene.2019.01252", "pmid": "31921296", "authors": ["Shuo Wu", "Xinyi Dai", "Dielai Xie"], "journal": "Frontiers in genetics", "year": 2019, "abstract": "Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease characterized by different molecular subgroups and clinical features. Therefore, it is important to uncover reliable molecular biomarkers for distinguishing different risk patient subgroup. Here, we conducted a multi-omics analysis to examine the joint predictive power of a multi-type RNA signature in the prognosis of HNSCC patients through integration analysis of mRNA, miRNA, and lncRNA expression profiles and clinical data in a large number of HNSCC patients. A multi-type RNA signature (15SigRS) was constructed which can classify patients into the high-risk group and low-risk group with the significantly different outcome [hazard ratio (HR) = 2.718, 95% confidence interval (CI), 2.258-3.272, p < 0.001] in the discovery data set, and subsequently validated in the Cancer Genome Atlas (TCGA) testing data set (HR = 1.299, 95% CI, 1.170-1.442, p < 0.001) and another independent GSE65858 data set (HR = 1.077, 95% CI, 1.016-1.143, p = 0.013). Further multivariate Cox regression analysis and stratification analysis demonstrated the independence of predictive performance of the 15SigRS relative to conventional clinicopathological factors. Furthermore, the 15SigRS has a prior performance in prognostic prediction than other single RNA type-based signatures. Functional analysis suggested that the 15SigRS are involved in immune- or metabolism-related KEGG pathways. In summary, our study demonstrated the potential application of mixed RNA types as molecular markers for predicting the outcome of cancer patients.", "claims": [{"id": "claim-method", "statement": "Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31921296", "locator": "sections[8] \"Identification of Independent Survival-Related mRNAs, miRNAs, and lncRNAs\" paragraph 2", "quote": "Then all these candidate prognostic mRNAs, miRNAs, and lncRNAs were fitted into multivariate Cox regression analysis, 15 of 39 genes were identified as independent prognostic gene biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31921296", "locator": "sections[16] \"Data Availability Statement\" paragraph 2", "quote": "The data analyzed in this study was obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858)."}], "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:31921296"], "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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["", "<60", "113 (46.1)", "103 (42.2)", "216 (44.2)", "153 (56.7)"], ["Gender, n (%)", "Female", "64 (26.1)", "66 (27.0)", "130 (26.6)", "47 (17.4)"], ["", "Male", "181 (73.9)", "178 (73.0)", "359 (73.4)", "223 (82.6)"], ["Stage, n (%)", "Stage I/II", "57 (23.3)", "37 (15.2)", "94 (19.2)", "55 (20.4)"], ["", "Stage III/IV", "157 (64.1)", "171 (70.1)", "328 (67.1)", "215 (79.6)"], ["", "NA", "31 (12.6)", "36 (14.7)", "67 (13.7)", ""], ["Grade, n (%)", "G1", "29 (11.8)", "32 (13.1)", "61 (12.5)", ""], ["", "G2", "147 (60)", "144 (59.0)", "291 (59.5)", ""], ["", "G3", "59 (24.1)", "58 (23.8)", "117 (23.9)", ""], ["", "NA", "10 (4.1)", "10 (4.1)", "20 (4.1)", ""], ["Race, n (%)", "White", "201 (82)", "216 (88.5)", "417 (85.3)", ""], ["", "Other_race", "34 (13.9)", "24 (9.8)", "58 (11.9)"], ["", "NA", "10 (4.1)", "4 (1.7)", "14 (2.9)", ""], ["ANGIOLYMPHATIC_INVASION, n (%)", "Yes", "54 (22)", "61 (25)", "115 (23.5)", ""], ["", "No", "112 (45.7)", "104 (42.6)", "216 (44.2)", ""], ["", "NA", "79 (32.2)", "79 (32.4)", "158 (32.3)", ""], ["PERINEURAL_INVASION, n (%)", "Yes", "72 (29.4)", "86 (35.2)", "158 (32.3)", ""], ["", "No", "98 (40)", "86 (35.2)", "184 (37.6)", ""], ["", "NA", "75 (30.6)", "72 (29.5)", "147 (30.1)", ""], ["Smoking_pack_years, n (%)", "> = 40", "82 (33.5)", "78 (32.0)", "160 (32.7)", "222 (YES, 82.2)"], ["", "<40", "61 (24.9)", "58 (23.8)", "119 (24.3)", "48 (NO, 17.8)"], ["", "NA", "102 (41.6)", "108 (44.2)", "210 (42.9)", ""], ["ALCOHOL_HISTORY_DOCUMENTED, n (%)", "Yes", "159 (64.9)", "165 (67.6)", "324 (66.3)", ""], ["", "No", "81 (33.1)", "73 (29.9)", "154 (31.5)", ""], ["", "NA", "5 (2)", "6 (2.5)", "11 (2.2)", ""], ["HPV_STATUS_P16, n (%)", "Negative", "37 (15.1)", "32 (13.1)", "69 (14.1)", ""], ["", "Positive", "15 (6.1)", "15 (6.1)", "30 (6.1)", ""], ["", "NA", "193 (78.8)", "197 (80.8)", "390 (79.8)", ""]], "row_count": 32, "column_count": 6, "numeric_cells": 112, "header_rows": 1, "locator": "section[3]/table-wrap[1]#T1"}}, {"id": 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["15SigRS", "2.718", "2.258–3.272", "<0.001", "2.562", "1.999–3.284", "<0.001"], ["Age", "1.043", "1.024–1.063", "0.000", "1.016", "0.991–1.042", "0.218"], ["Gender (male/female)", "0.643", "0.42–0.984", "0.042", "0.499", "0.272–0.916", "0.025"], ["Stage (III&IV/I&II)", "1.254", "0.753–2.088", "0.385", "1.187", "0.352–4.001", "0.783"], ["Grade (G2/G1)", "1.362", "0.703–2.640", "0.360", "1.685", "0.584–4.861", "0.334"], ["Grade (G3/G1)", "1.139", "0.554–2.343", "0.723", "1.829", "0.599–5.582", "0.289"], ["Race (White/other race)", "0.578", "0.329–1.014", "0.056", "1.623", "0.679–3.880", "0.276"], ["ALCOHOL_HISTORY_DOCUMENTED (yes/no)", "0.734", "0.483–1.113", "0.145", "1.010", "0.562–1.815", "0.974"], ["ANGIOLYMPHATIC_INVASION (yes/no)", "1.687", "0.988–2.881", "0.056", "", "", ""], ["PERINEURAL_INVASION (yes/no)", "2.879", "1.689–4.907", "0.000", "", "", ""], ["SMOKING_PACK_YEARS", "1.001", "0.995–1.008", "0.737", "", "", ""], ["Validation data set (n = 244)"], ["15SigRS", "1.299", "1.170–1.442", "<0.001", "1.311", "1.158–1.484", "<0.001"], ["Age", "1.003", "0.986–1.02", "0.721", "1.020", "0.996–1.045", "0.111"], ["Gender (male/female)", "0.904", "0.605–1.352", "0.624", "1.063", "0.606–1.866", "0.832"], ["Stage (III & IV/I & II)", "3.294", "1.595–6.805", "0.001", "3.102", "0.569–16.905", "0.191"], ["Grade (G2/G1)", "2.264", "1.157–4.430", "0.017", "1.326", "0.593–2.967", "0.492"], ["Grade (G3/G1)", "1.927", "0.936–3.964", "0.075", "1.248", "0.520–2.993", "0.620"], ["Race (White/other race)", "0.871", "0.477–1.587", "0.651", "0.900", "0.452–1.790", "0.763"], ["ALCOHOL_HISTORY_DOCUMENTED (yes/no)", "1.185", "0.788–1.78", "0.415", "1.536", "0.882–2.675", "0.130"], ["ANGIOLYMPHATIC_INVASION (yes/no)", "1.809", "1.138–2.874", "0.012", "", "", ""], ["PERINEURAL_INVASION (yes/no)", "1.698", "1.061–2.716", "0.027", "", "", ""], ["SMOKING_PACK_YEARS", "1.001", "0.992–1.011", "0.801", "", "", ""], ["TCGA data set (n = 489)"], ["15SigRS", "1.496", "1.393–1.606", "<0.001", "1.482", "1.348–1.629", "<0.001"], ["Age", "1.022", "1.009–1.035", "0.001", "1.027", "1.010–1.044", "0.002"], ["Gender (male/female)", "0.759", "0.568–1.016", "0.064", "0.786", "0.526–1.174", "0.239"], ["Stage (III & IV/I & II)", "1.812", "1.216–2.701", "0.003", "1.846", "0.768–4.437", "0.171"], ["Grade (G2/G1)", "1.749", "1.102–2.777", "0.018", "1.240", "0.679–2.264", "0.483"], ["Grade (G3/G1)", "1.507", "0.913–2.487", "0.109", "1.441", "0.754–2.754", "0.269"], ["Race (White/other race)", "0.710", "0.473–1.065", "0.098", "0.811", "0.492–1.335", "0.410"], ["ALCOHOL_HISTORY_DOCUMENTED (yes/no)", "0.951", "0.712–1.27", "0.734", "1.165", "0.792–1.714", "0.437"], ["ANGIOLYMPHATIC_INVASION (yes/no)", "1.750", "1.239–2.473", "0.001", "", "", ""], ["PERINEURAL_INVASION (yes/no)", "2.222", "1.563–3.16", "0.000", "", "", ""], ["SMOKING_PACK_YEARS", "1.001", "0.995–1.006", "0.765", "", "", ""], ["HPV_STATUS_P16 (yes/no)", "0.504", "0.172–1.477", "0.212", "", "", ""], ["GSE65858 data set (n = 270)"], ["15SigRS", "1.077", "1.016–1.143", "0.013", "1.073", "1.012–1.137", "0.019"], ["Age", "1.037", "1.006–1.048", "0.012", "1.03", "1.007–1.053", "0.01"], ["Gender (male/female)", "1.046", "0.6174–1.771", "0.868", "1.026", "0.602–1.749", "0.923"], ["Stage (II/I)", "0.386", "0.112–1.333", "0.132", "0.306", "0.088–1.071", "0.064"], ["Stage (III/I & II)", "0.459", "0.1447–1.454", "0.185", "0.423", "0.133–1.343", "0.144"], ["Stage (IV/I&)", "1.495", "0.603–3.705", "0.385", "1.339", "0.537–3.336", "0.531"], ["SMOKING (yes/no)", "0.941", "0.555–1.595", "0.821", "1.294", "0.733–2.284", "0.373"]], "row_count": 47, "column_count": 7, "numeric_cells": 227, "header_rows": 2, "locator": "section[10]/table-wrap[1]#T2"}}], "gaps": ["asset:data:geo:GSE65858 not downloaded", "asset:data:geo:GSE65858 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", 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