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pmid:36714024

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.

Kaier Cai · Zhilong Xie · Yingao Liu · Junfeng Wu · Hao Song · Wang Liu · Xinyi Wang · Yinghuan Xiong · Siyuan Gan · Yanqin Sun

BioMed research international · 2023 doi:10.1155/2023/2152432

Paper

complete

Experiment

complete

Data

partial

Analysis

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title
Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.
doi
10.1155/2023/2152432
pmid
36714024
authors array · 10 items
[0]
Kaier Cai
[1]
Zhilong Xie
[2]
Yingao Liu
[3]
Junfeng Wu
[4]
Hao Song
[5]
Wang Liu
[6]
Xinyi Wang
[7]
Yinghuan Xiong
[8]
Siyuan Gan
[9]
Yanqin Sun
journal
BioMed research international
year
2023
abstract
Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.
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After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.
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quote
After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.
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The data used to support the findings of this study are available from the corresponding authors upon request.
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Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database.
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[109]
GSM1062874
sample_count
110
group_sizes
null
group_sizes_basis
null
platform
6244
taxon
Homo sapiens
title
Gene expression profiling of lung adenocarcinomas and normal lung tissue
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE7670
role
raw
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geo-series
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application/vnd.ncbi.geo-series+json
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7670
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null
reader_status
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GSE7670
version
null
note
The data used to support the findings of this study are available from the corresponding authors upon request.
structure object · 15 fields
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GEO
access_route
GEO accession URL
access_statement_verbatim
The data used to support the findings of this study are available from the corresponding authors upon request.
access_statement_kind
data_availability_statement
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GSM185811
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[52]
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[65]
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sample_count
66
group_sizes
null
group_sizes_basis
null
platform
96
taxon
Homo sapiens
title
Expression data from Lung cancer
resolution_checked_at
2026-11-01T15:14:33Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 10 items
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asset:data:geo:GSE151102 not downloaded
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[3]
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[4]
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[5]
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[6]
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[7]
asset:data:geo:GSE43458 licence not read
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[9]
asset:data:geo:GSE7670 licence not read
analysis object · 4 fields
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partial
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id
method-1
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survival_analysis
specific
kaplan_meier
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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
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medium
quote
Kaplan-Meier
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id
method-2
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network_analysis
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ppi_network
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Network / pathway analysis
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[0]
build_graph
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centrality_scores
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pathway_enrich
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data_shapes array · 1 item
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edge_list
evidence object · 3 fields
score
0.7
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protein-protein interaction
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method-3
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network_analysis
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Network / pathway analysis
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pathway_enrich
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edge_list
evidence object · 3 fields
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pathway enrichment
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id
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null
specific
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label
GEO2R
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missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
label
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assay_families array · 9 items
[0]
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[5]
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[7]
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
tier
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blocked_on
nothing:replicable_now
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method object · 5 fields
tier
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true
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survival_km
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier.
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
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true
accessions array · 5 items
[0]
GSE7670
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GSE33532
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GSE43458
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GSE19804
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[0]
GSE7670
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GSE151102
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GSE33532
[3]
GSE43458
[4]
GSE19804
statements array · 1 item
[0]
The data used to support the findings of this study are available from the corresponding authors upon request.
basis
The paper names GSE7670, GSE151102, GSE33532, GSE43458, GSE19804 in Gene Expression Omnibus (GEO) database in the National Center for Biotechnology Information (NCBI) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
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survival_km
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[0]
source:pmid:36714024
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data:geo:GSE151102
steps array · 1 item
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run_analysis
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[0]
data:geo:GSE151102
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review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
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model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:25:25Z
independence
single_reader
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method
verbatim
availability
verbatim
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5
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8414
completion_tokens
2809
cached_tokens
256
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extraction
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complete
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MiniMax-M3.1-Flash
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absent
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not_run
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pmid:36714024
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paper
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pmid:36714024
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source:pmid:36714024
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manuscript
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PMID 36714024 full text
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id
data:geo:GSE151102
type
dataset
label
GEO GSE151102
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id
activity-reading-sci-2026-11-01
type
classification
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type
paper
label
pmid:36714024
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id
source:pmid:36714024
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manuscript
label
PMID 36714024 full text
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id
data:geo:GSE151102
type
dataset
label
GEO GSE151102
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agent:corpus-assay-engine
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id
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extraction
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agent:minimax-reader
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id
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[5]
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[0]
and Methods 3.4. Accuracy and Reliability of Microarray Analysis of the Core Genes The microarray da
[1]
of Microarray Analysis of the Core Genes The microarray datasets GSE7670, GSE151102, GSE33532, GSE43
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36714024", "kind": "science-record", "revision": 3, "content_hash": "sha256:0cf1035bdce844f576b92921b3b5372ba248eee5503fc0efa73678b3d88af072", "created_at": "2026-11-01T19:01:16Z", "updated_at": "2026-11-01T22:08:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.", "doi": "10.1155/2023/2152432", "pmid": "36714024", "authors": ["Kaier Cai", "Zhilong Xie", "Yingao Liu", "Junfeng Wu", "Hao Song", "Wang Liu", "Xinyi Wang", "Yinghuan Xiong", "Siyuan Gan", "Yanqin Sun"], "journal": "BioMed research international", "year": 2023, "abstract": "Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.\nMethods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.\nResults: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.\nConclusion: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.", "claims": [{"id": "claim-method", "statement": "After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36714024", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding authors upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36714024", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding authors upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36714024", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database.", "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:36714024"], "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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