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pmid:35966313

Development and validation of endoplasmic reticulum stress-related eight-gene signature for predicting the overall survival of lung adenocarcinoma.

Lin Lin · Wei Zhang

Translational cancer research · 2022 doi:10.21037/tcr-22-106

Paper

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Development and validation of endoplasmic reticulum stress-related eight-gene signature for predicting the overall survival of lung adenocarcinoma.
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10.21037/tcr-22-106
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35966313
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Lin Lin
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Wei Zhang
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Translational cancer research
year
2022
abstract
Background: The high case-fatality rate of patients with lung adenocarcinoma (LUAD) emphasizes the importance of identifying a robust and reliable prognostic signature for LUAD patients. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD. Methods: The mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms. Results: A prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways. Conclusions: The 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.
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sections[4] "Construction overall survival (OS)-related gene signature in TCGA-LUAD" paragraph 2
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The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting.
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Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
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sections[25] "Footnotes" paragraph 2
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Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
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The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.
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sections[13] "The internal validation data sets in TCGA verifies the robustness of the prognostic model" paragraph 2
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In time-dependent ROC curve analyses, the AUC for the eight -mRNA signature prognostic model was 0.749 at one year, 0.737 at two years and 0.707 at four years of OS, this suggests that the model had good discrimination (AUCmax =0.749) (Figure 3B).
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The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.
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[245]
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sample_count
246
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression data for pathological stage I-II lung adenocarcinomas
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 15 fields
id
data:geo:GSE37745
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=GSE37745
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE37745
version
null
note
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
access_statement_kind
data_availability_statement
sample_accessions array · 196 items
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[67]
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[130]
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[159]
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[160]
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[162]
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[165]
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[180]
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[184]
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[185]
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[188]
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[190]
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[191]
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[192]
GSM1019330
[193]
GSM1019331
[194]
GSM1019332
[195]
GSM1019333
sample_count
196
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis and tissue microarray validation
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 15 fields
id
data:geo:GSE50081
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=GSE50081
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE50081
version
null
note
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
access_statement_kind
data_availability_statement
sample_accessions array · 181 items
[0]
GSM1213669
[1]
GSM1213670
[2]
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[3]
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[14]
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[16]
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[17]
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[19]
GSM1213688
[20]
GSM1213689
[21]
GSM1213690
[22]
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[23]
GSM1213692
[24]
GSM1213693
[25]
GSM1213694
[26]
GSM1213695
[27]
GSM1213696
[28]
GSM1213697
[29]
GSM1213698
[30]
GSM1213699
[31]
GSM1213700
[32]
GSM1213701
[33]
GSM1213702
[34]
GSM1213703
[35]
GSM1213704
[36]
GSM1213705
[37]
GSM1213706
[38]
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[39]
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[40]
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[41]
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[42]
GSM1213711
[43]
GSM1213712
[44]
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[45]
GSM1213714
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[47]
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GSM1213717
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GSM1213719
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GSM1213724
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GSM1213725
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GSM1213727
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GSM1213738
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GSM1213739
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[85]
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[87]
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[99]
GSM1213768
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[103]
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[104]
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[105]
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[106]
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[109]
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[111]
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[112]
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[113]
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[114]
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[115]
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[119]
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[124]
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[125]
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[130]
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[139]
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[148]
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[149]
GSM1213818
[150]
GSM1213819
[151]
GSM1213820
[152]
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[153]
GSM1213822
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[155]
GSM1213824
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GSM1213825
[157]
GSM1213826
[158]
GSM1213827
[159]
GSM1213828
[160]
GSM1213829
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GSM1213830
[162]
GSM1213831
[163]
GSM1213832
[164]
GSM1213833
[165]
GSM1213834
[166]
GSM1213835
[167]
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[168]
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[169]
GSM1213838
[170]
GSM1213839
[171]
GSM1213840
[172]
GSM1213841
[173]
GSM1213842
[174]
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[176]
GSM1213845
[177]
GSM1213846
[178]
GSM1213847
[179]
GSM1213848
[180]
GSM1213849
sample_count
181
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Validation of a histology-independent prognostic gene signature for early stage, non-small cell lung cancer including stage IA patients
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 8 items
[0]
asset:data:geo:GSE30219 not downloaded
[1]
asset:data:geo:GSE30219 licence not read
[2]
asset:data:geo:GSE31210 not downloaded
[3]
asset:data:geo:GSE31210 licence not read
[4]
asset:data:geo:GSE37745 not downloaded
[5]
asset:data:geo:GSE37745 licence not read
[6]
asset:data:geo:GSE50081 not downloaded
[7]
asset:data:geo:GSE50081 licence not read
analysis object · 4 fields
state
partial
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
The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting.
[1] object · 10 fields
id
method-2
family
group_comparison
specific
wilcoxon
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
group_comparison_wilcoxon
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
Wilcoxon
[2] object · 10 fields
id
method-3
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
[3] object · 10 fields
id
method-4
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.7
band
medium
quote
ROC curve
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
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 · 4 items
[0]
GSE30219
[1]
GSE31210
[2]
GSE50081
[3]
GSE37745
resolved_accessions array · 4 items
[0]
GSE30219
[1]
GSE31210
[2]
GSE50081
[3]
GSE37745
statements array · 1 item
[0]
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss
basis
The paper names GSE30219, GSE31210, GSE50081, GSE37745 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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cox_ph
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data:geo:GSE30219
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review object · 5 fields
state
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machine_critique object · 5 fields
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verbatim
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data:geo:GSE30219
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GEO GSE30219
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GEO GSE30219
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agent:corpus-assay-engine
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agent:minimax-reader
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asset:data:geo:GSE30219 licence not read
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asset:data:geo:GSE30219 not downloaded
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asset:data:geo:GSE31210 licence not read
[3]
asset:data:geo:GSE31210 not downloaded
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asset:data:geo:GSE37745 licence not read
[5]
asset:data:geo:GSE37745 not downloaded
[6]
asset:data:geo:GSE50081 licence not read
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asset:data:geo:GSE50081 not downloaded
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execution.not_run
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The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35966313", "kind": "science-record", "revision": 4, "content_hash": "sha256:a9299531b47a92a0a1a499faa57a0ca78f5dfc8dd57a0336993fe070b5461cd1", "created_at": "2026-11-01T18:15:05Z", "updated_at": "2026-11-01T22:07:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of endoplasmic reticulum stress-related eight-gene signature for predicting the overall survival of lung adenocarcinoma.", "doi": "10.21037/tcr-22-106", "pmid": "35966313", "authors": ["Lin Lin", "Wei Zhang"], "journal": "Translational cancer research", "year": 2022, "abstract": "Background: The high case-fatality rate of patients with lung adenocarcinoma (LUAD) emphasizes the importance of identifying a robust and reliable prognostic signature for LUAD patients. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD.\nMethods: The mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms.\nResults: A prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways.\nConclusions: The 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.", "claims": [{"id": "claim-method", "statement": "The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[4] \"Construction overall survival (OS)-related gene signature in TCGA-LUAD\" paragraph 2", "quote": "The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[25] \"Footnotes\" paragraph 2", "quote": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[13] \"The internal validation data sets in TCGA verifies the robustness of the prognostic model\" paragraph 2", "quote": "In time-dependent ROC curve analyses, the AUC for the eight -mRNA signature prognostic model was 0.749 at one year, 0.737 at two years and 0.707 at four years of OS, this suggests that the model had good discrimination (AUCmax =0.749) (Figure 3B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.", "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:35966313"], "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:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35966313", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-22-106", "sha256": 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