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pmid:33589575

Development and validation of RNA binding protein-applied prediction model for gastric cancer.

Shuang Dai · Yan Huang · Ting Liu · Zi-Han Xu · Tao Liu · Lan Chen · Zhi-Wu Wang · Feng Luo

Aging · 2021 doi:10.18632/aging.202483

Paper

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Experiment

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Data

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title
Development and validation of RNA binding protein-applied prediction model for gastric cancer.
doi
10.18632/aging.202483
pmid
33589575
authors array · 8 items
[0]
Shuang Dai
[1]
Yan Huang
[2]
Ting Liu
[3]
Zi-Han Xu
[4]
Tao Liu
[5]
Lan Chen
[6]
Zhi-Wu Wang
[7]
Feng Luo
journal
Aging
year
2021
abstract
RNA-binding proteins (RBPs) have been reported to be associated with the occurrence and progression of multiple cancers, but the role in gastric adenocarcinoma remains poorly understood. The present study aims to uncover potential RBPs associated with the survival of gastric adenocarcinoma, as well as corresponding biologic properties and signaling pathways of these RBPs. RNA sequencing and clinical data of GC were obtained from The Cancer Genome Atlas (n=373) and the Gene Expression Omnibus (GSE84437, n=433) database. Tumor samples in TCGA were randomly divided into the training and internal testing group by R software. A total of 238 DERBPs were selected for univariate and multivariate Cox regression analyses. Five pivotal RBP genes (RNASE2, METTL1, ANG, YBX2 and LARP6) were screened out and were used to construct a new prognostic model. Survival relevance and prediction accuracy of model were tested via Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in internal and external testing groups. Further analysis has also showed that this model could serve as an independent prognosis-related parameter. A prognostic nomogram has been eventually developed, and presents a good performance of prediction.
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Differentially expressed genes (DEGs) with false discovery rate (FDR) cutoff of 0.05 and |log2(fold-change)|>0.5 were identified using the Limma package.
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Gene expression information (FPKM, n=373) and corresponding survival data of patients with gastric adenocarcinoma were downloaded from TCGA database in May 2020, containing 343 tumor samples with GC and 30 adjacent non-tumor samples.
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Univariate and multivariate Cox regression analyses of the entire TCGA cohort.
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Independent prediction capacity of prognostic model
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Table 1
caption
Univariate and multivariate Cox regression analyses of the entire TCGA cohort.
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[0] array · 4 items
[0]
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[1]
Univariate analysis
[2]
[3]
Multivariate analysis
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[0]
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[1]
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[2]
[3]
HR (95% CI)
[4]
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[0]
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[1]
1.024 (1.001-1.042)
[2]
0.007
[3]
[4]
1.029 (1.010-1.049)
[5]
0.003
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[0]
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[1]
0.761 (0.527-1.098)
[2]
0.1439
[3]
[4]
——
[5]
——
[4] array · 6 items
[0]
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[1]
1.184 (0.849-1.651)
[2]
0.317
[3]
[4]
——
[5]
——
[5] array · 6 items
[0]
Stage
[1]
1.550 (1.250-1.924)
[2]
<0.001
[3]
[4]
1.323 (0.862-2.030)
[5]
0.200
[6] array · 6 items
[0]
T
[1]
1.281 (1.029-1.594)
[2]
0.027
[3]
[4]
1.082 (0.795-1.473)
[5]
0.617
[7] array · 6 items
[0]
M
[1]
2.016 (1.109-3.662)
[2]
0.021
[3]
[4]
1.579 (0.700-3.563)
[5]
0.271
[8] array · 6 items
[0]
N
[1]
1.335 (1.140-1.564)
[2]
<0.001
[3]
[4]
1.172 (0.921-1.492)
[5]
0.196
[9] array · 6 items
[0]
Risk Score
[1]
1.216 (1.086-1.362)
[2]
<0.001
[3]
[4]
1.157 (1.005-1.333)
[5]
0.042
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section[7]/table-wrap[1]#t1
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Univariate and multivariate Cox regression analyses of the GEO cohort.
structure object · 5 fields
pmcid
PMC7950299
section_index
7
section_title
Independent prediction capacity of prognostic model
row_count
7
column_count
6
table object · 8 fields
label
Table 2
caption
Univariate and multivariate Cox regression analyses of the GEO cohort.
rows array · 7 items
[0] array · 4 items
[0]
Variables
[1]
Univariate analysis
[2]
[3]
Multivariate analysis
[1] array · 5 items
[0]
HR (95% CI)
[1]
P-value
[2]
[3]
HR (95% CI)
[4]
P-value
[2] array · 6 items
[0]
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[1]
1.020 (1.007-1.032)
[2]
0.002
[3]
[4]
1.025 (1.012-1.037)
[5]
<0.001
[3] array · 6 items
[0]
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[1]
0.796 (0.588-1.078)
[2]
0.141
[3]
[4]
——
[5]
——
[4] array · 6 items
[0]
T
[1]
1.740 (1.378-2.198)
[2]
<0.001
[3]
[4]
1.649 (1.290-2.110)
[5]
<0.001
[5] array · 6 items
[0]
N
[1]
1.676 (1.429-1.967)
[2]
<0.001
[3]
[4]
1.505 (1.281-1.768)
[5]
<0.001
[6] array · 6 items
[0]
Risk Score
[1]
2.436 (1.526-3.891)
[2]
<0.001
[3]
[4]
2.695 (1.621-4.481)
[5]
<0.001
locator
section[7]/table-wrap[2]#t2
gaps array · 2 items
[0]
asset:data:geo:GSE84437 not downloaded
[1]
asset:data:geo:GSE84437 licence not read
analysis object · 4 fields
state
partial
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[0] object · 10 fields
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method-1
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limma_voom
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Differential expression analysis
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[0]
normalise_counts
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differential_expression_fit
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fdr_bh
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count_matrix_with_condition
evidence object · 3 fields
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quote
Differentially expressed genes (DEGs) with false discovery rate (FDR) cutoff of 0.05 and |log2(fold-change)|>0.5 were identified using the Limma package.
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method-2
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Survival analysis
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time_to_event_with_censoring
evidence object · 3 fields
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Kaplan-Meier
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method-3
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survival_analysis
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cox_proportional_hazards
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Survival analysis
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[0]
cox_ph
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parameters object · 0 fields
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[0]
time_to_event_with_censoring
evidence object · 3 fields
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0.7
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Cox regression
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method.reader_differs_from_extractor
capability object · 3 fields
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complete
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[0] object · 6 fields
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capability:computational_analysis
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Computational analysis platform
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candidate
Automated Assays Laravel application and simulated adapters
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complete
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nothing:replicable_now
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normalise_counts
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differential_expression_fit
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fdr_bh
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
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capability:computational_analysis
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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
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true
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[0]
GSE84437
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[0]
GSE84437
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Gene expression information (FPKM, n=373) and corresponding survival data of patients with gastric adenocarcinoma were downloaded from TCGA database in May 2020, containing 343 tumor samples with GC and 30 adjacent non-tumor samples.
basis
The paper names GSE84437 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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minimax
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minimax
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extraction
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minimax
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erties and signaling pathways of these RBPs. RNA sequencing and clinical data of GC were obtained from T
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Differentially expressed genes (DEGs) with false discovery rate (FDR) cutoff of 0.05 and |log2(fold-change)|>0.5 were identified using the Limma package.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33589575", "kind": "science-record", "revision": 9, "content_hash": "sha256:b8535207bd7d86692ec574e4e674168f0f0a087ed7f571377df95ad93b19fc15", "created_at": "2026-11-01T15:41:25Z", "updated_at": "2026-11-01T22:46:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of RNA binding protein-applied prediction model for gastric cancer.", "doi": "10.18632/aging.202483", "pmid": "33589575", "authors": ["Shuang Dai", "Yan Huang", "Ting Liu", "Zi-Han Xu", "Tao Liu", "Lan Chen", "Zhi-Wu Wang", "Feng Luo"], "journal": "Aging", "year": 2021, "abstract": "RNA-binding proteins (RBPs) have been reported to be associated with the occurrence and progression of multiple cancers, but the role in gastric adenocarcinoma remains poorly understood. The present study aims to uncover potential RBPs associated with the survival of gastric adenocarcinoma, as well as corresponding biologic properties and signaling pathways of these RBPs. RNA sequencing and clinical data of GC were obtained from The Cancer Genome Atlas (n=373) and the Gene Expression Omnibus (GSE84437, n=433) database. Tumor samples in TCGA were randomly divided into the training and internal testing group by R software. A total of 238 DERBPs were selected for univariate and multivariate Cox regression analyses. Five pivotal RBP genes (RNASE2, METTL1, ANG, YBX2 and LARP6) were screened out and were used to construct a new prognostic model. Survival relevance and prediction accuracy of model were tested via Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in internal and external testing groups. Further analysis has also showed that this model could serve as an independent prognosis-related parameter. 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