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pmid:37404760

Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer.

Qi Wang · Biyuan Zhang · Haiji Wang · Mingming Hu · Hui Feng · Wen Gao · Haijun Lu · Ye Tan · Yinying Dong · Mingjin Xu · Tianhui Guo · Xiaomeng Ji

Frontiers in oncology · 2023 doi:10.3389/fonc.2023.1210994

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Experiment

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Data

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title
Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer.
doi
10.3389/fonc.2023.1210994
pmid
37404760
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[0]
Qi Wang
[1]
Biyuan Zhang
[2]
Haiji Wang
[3]
Mingming Hu
[4]
Hui Feng
[5]
Wen Gao
[6]
Haijun Lu
[7]
Ye Tan
[8]
Yinying Dong
[9]
Mingjin Xu
[10]
Tianhui Guo
[11]
Xiaomeng Ji
journal
Frontiers in oncology
year
2023
abstract
Background: Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients. Methods: We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model. Results: There were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4+ memory T cells, CD8+ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model. Conclusion: In conclusion, we discovered a 6 gene signature to forecast GC patients' OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.
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Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort.
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sections[5] "Prognostic model construction and validation" paragraph 2
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Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort.
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
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sections[16] "Data availability statement" paragraph 2
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
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Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69.
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sections[11] "Prognostic model construction and validation" paragraph 2
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Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69.
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Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69.
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Global gene expression analysis of gastric cancer by oligonucleotide microarrays
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sample_count
300
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study [gastric tumors]
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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licence_read
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[3] object · 15 fields
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note
Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
structure object · 15 fields
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GEO
access_route
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
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GSM1584930
sample_count
94
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Discovered salivary transcriptomic biomarkers for the detection of gastric cancer
resolution_checked_at
2026-11-01T18:46:34Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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licence_read
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[4] object · 15 fields
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data:geo:GSE66229
role
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bytes
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note
Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
access_statement_kind
data_availability_statement
sample_accessions array · 400 items
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[48]
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[51]
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[52]
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[53]
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[54]
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[55]
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[56]
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[57]
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[58]
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[61]
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[65]
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[67]
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[70]
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[71]
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[79]
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[80]
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[106]
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[110]
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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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[116]
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[117]
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[118]
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[119]
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[244]
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[245]
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[246]
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[247]
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[248]
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[249]
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[250]
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GSM1524126
sample_count
400
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 8 items
[0]
asset:data:geo:GSE54129 not downloaded
[1]
asset:data:geo:GSE54129 licence not read
[2]
asset:data:geo:GSE62254 not downloaded
[3]
asset:data:geo:GSE62254 licence not read
[4]
asset:data:geo:GSE64951 not downloaded
[5]
asset:data:geo:GSE64951 licence not read
[6]
asset:data:geo:GSE66229 not downloaded
[7]
asset:data:geo:GSE66229 licence not read
analysis object · 4 fields
state
complete
methods array · 3 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
Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort.
[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
0.7
band
medium
quote
Kaplan-Meier
[2] object · 10 fields
id
method-3
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
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basis
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tier
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data object · 6 fields
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GSE54129
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GSE66229
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statements array · 1 item
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).
basis
The paper names GSE54129, GSE66229, GSE64951, GSE62254 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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kind
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provider
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minimax
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independence
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method
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GEO GSE54129
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Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37404760", "kind": "science-record", "revision": 3, "content_hash": "sha256:6c06e16f17de65541c6f877cbf260b7039bc62101d264fa2bc079c39485d5ecf", "created_at": "2026-11-01T19:02:10Z", "updated_at": "2026-11-01T22:09:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer.", "doi": "10.3389/fonc.2023.1210994", "pmid": "37404760", "authors": ["Qi Wang", "Biyuan Zhang", "Haiji Wang", "Mingming Hu", "Hui Feng", "Wen Gao", "Haijun Lu", "Ye Tan", "Yinying Dong", "Mingjin Xu", "Tianhui Guo", "Xiaomeng Ji"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Background: Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.\nMethods: We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.\nResults: There were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4+ memory T cells, CD8+ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.\nConclusion: In conclusion, we discovered a 6 gene signature to forecast GC patients' OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.", "claims": [{"id": "claim-method", "statement": "Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37404760", "locator": "sections[5] \"Prognostic model construction and validation\" paragraph 2", "quote": "Subsequently, we proceeded to generate a prognostic signature by means of the least absolute shrinkage and selection operator (LASSO) Cox regression method utilizing R “glmnet” package (17) in TCGA cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37404760", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37404760", "locator": "sections[11] \"Prognostic model construction and validation\" paragraph 2", "quote": "Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Figure 5E shows that the prognostic signature was well established at 1-, 3-, and 5-year AUCs of 0.62, 0.67, and 0.69.", "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:37404760"], "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:GSE54129"], "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:GSE54129"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37404760", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2023.1210994", "sha256": "sha256:74bbd9b69705a2f33dc8d9f153ea259f4fd7f108cf7be36fa480e30151aaff7b", "bytes": 44709, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2023.1210994", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10316024", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE54129", "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=GSE54129", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE54129", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1308392", "GSM1308393", "GSM1308394", "GSM1308395", "GSM1308396", "GSM1308397", "GSM1308398", "GSM1308399", "GSM1308400", "GSM1308401", "GSM1308402", "GSM1308403", "GSM1308404", "GSM1308405", "GSM1308406", "GSM1308407", "GSM1308408", "GSM1308409", "GSM1308410", "GSM1308411", "GSM1308412", "GSM1308413", "GSM1308414", "GSM1308415", "GSM1308416", "GSM1308417", "GSM1308418", "GSM1308419", "GSM1308420", "GSM1308421", "GSM1308422", "GSM1308423", "GSM1308424", "GSM1308425", "GSM1308426", "GSM1308427", "GSM1308428", "GSM1308429", "GSM1308430", "GSM1308431", "GSM1308432", "GSM1308433", "GSM1308434", "GSM1308435", "GSM1308436", "GSM1308437", "GSM1308438", "GSM1308439", "GSM1308440", "GSM1308441", "GSM1308442", "GSM1308443", "GSM1308444", "GSM1308445", "GSM1308446", "GSM1308447", "GSM1308448", "GSM1308449", "GSM1308450", "GSM1308451", "GSM1308452", "GSM1308453", "GSM1308454", "GSM1308455", "GSM1308456", "GSM1308457", "GSM1308458", "GSM1308459", "GSM1308460", "GSM1308461", "GSM1308462", "GSM1308463", "GSM1308464", "GSM1308465", "GSM1308466", "GSM1308467", "GSM1308468", "GSM1308469", "GSM1308470", "GSM1308471", "GSM1308472", "GSM1308473", "GSM1308474", "GSM1308475", "GSM1308476", "GSM1308477", "GSM1308478", "GSM1308479", "GSM1308480", "GSM1308481", "GSM1308482", "GSM1308483", "GSM1308484", "GSM1308485", "GSM1308486", "GSM1308487", "GSM1308488", "GSM1308489", "GSM1308490", "GSM1308491", "GSM1308492", "GSM1308493", "GSM1308494", "GSM1308495", "GSM1308496", "GSM1308497", "GSM1308498", "GSM1308499", "GSM1308500", "GSM1308501", "GSM1308502", "GSM1308503", "GSM1308504", "GSM1308505", "GSM1308506", "GSM1308507", "GSM1308508", "GSM1308509", "GSM1308510", "GSM1308511", "GSM1308512", "GSM1308513", "GSM1308514", "GSM1308515", "GSM1308516", "GSM1308517", "GSM1308518", "GSM1308519", "GSM1308520", "GSM1308521", "GSM1308522", "GSM1308523"], "sample_count": 132, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Global gene expression analysis of gastric cancer by oligonucleotide microarrays", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA repository (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), and IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1523727", "GSM1523728", "GSM1523729", "GSM1523744", "GSM1523745", "GSM1523746", "GSM1523747", "GSM1523748", "GSM1523765", "GSM1523768", "GSM1523769", "GSM1523770", "GSM1523771", "GSM1523772", "GSM1523773", "GSM1523774", "GSM1523775", "GSM1523776", "GSM1523777", "GSM1523778", "GSM1523779", "GSM1523780", "GSM1523781", "GSM1523782", "GSM1523783", "GSM1523784", "GSM1523785", "GSM1523786", "GSM1523787", "GSM1523788", "GSM1523789", "GSM1523790", "GSM1523791", "GSM1523792", "GSM1523793", "GSM1523794", "GSM1523795", "GSM1523796", "GSM1523797", "GSM1523798", "GSM1523799", "GSM1523800", "GSM1523801", "GSM1523802", "GSM1523803", "GSM1523804", "GSM1523805", "GSM1523806", "GSM1523807", "GSM1523808", 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