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pmid:33447425

Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.

Rong Yang · Yuwei Zhou · Chengli Du · Yihe Wu

Journal of thoracic disease · 2020 doi:10.21037/jtd-20-3453

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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pmid:33447425
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science-record
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sha256:aa4551701055e619ea6c91289b26ec7d6c43d1ab84b06990b94ee5611bca4670
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2026-11-01T15:41:22Z
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2026-11-01T22:02:57Z
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paper object · 10 fields
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title
Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.
doi
10.21037/jtd-20-3453
pmid
33447425
authors array · 4 items
[0]
Rong Yang
[1]
Yuwei Zhou
[2]
Chengli Du
[3]
Yihe Wu
journal
Journal of thoracic disease
year
2020
abstract
BACKGROUND: Lung adenocarcinoma is the main pathological type of non-small cell lung cancer (NSCLC). In this study, we analyzed the gene expression profile of lung adenocarcinoma tumor and paracancerous tissues by bioinformatics to assess the genes and signal pathways related to lung adenocarcinoma. METHODS: The expression data of GSE7670, GSE27262, and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common differential expression genes of lung adenocarcinoma tumor and adjacent tissues. The STRING database was used to construct the protein-protein interaction (PPI) network of lung adenocarcinoma and mine the gene modules and core genes in the network, and the online tools, GEPIA and Kaplan-Meier plotter were used to further verify and analyze the core genes. RESULTS: There were 109 pairs of lung adenocarcinoma tissues and matched paracancerous normal lung tissues in the three data sets. Eighty-three differentially expressed genes were identified, including 16 up-regulated and 67 down-regulated genes, and 60 differentially expressed genes were successfully incorporated into the PPI network complex. Eleven core genes were identified in the PPI network complex, including three up-regulated (COMP, SPP1, COL1A1) and eight down-regulated genes (CDH5, CAV1, CLDN5, LYVE1, IL6, VWF, TEK, PECAM1). These core genes were verified by the GEPIA tumor database. Survival analysis showed that expression of the core genes was significantly related to the prognosis of lung adenocarcinoma. KEGG pathway analysis of core genes showed six genes (COMP, SPP1, COL1A1, IL6, VWF, TEK) were significantly enriched in the PI3K-Akt signaling-pathway (P=1.62E-06). CONCLUSIONS: By analyzing the differential expression genes of lung adenocarcinoma and paracancerous normal tissues with bioinformatics, 11 genes with significant differential expression and significant influence on prognosis were identified. The findings may provide new concepts for developing diagnosis and treatment targets and prognosis markers for lung adenocarcinoma.
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Differentially expressed genes with an adjusted P value <0.05 and |logFC| >2 were considered statistically significant by using t-test.
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sections[8] "Statistical analysis" paragraph 2
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Differentially expressed genes with an adjusted P value <0.05 and |logFC| >2 were considered statistically significant by using t-test.
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The data of three gene expression profiles (GSE7670, GSE27262, GSE32863) were downloaded from the database and included 26, 25, and 58 pairs of lung adenocarcinoma and matched paracancerous normal lung tissue samples, respectively.
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sections[3] "Microarray data information" paragraph 2
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The data of three gene expression profiles (GSE7670, GSE27262, GSE32863) were downloaded from the database and included 26, 25, and 58 pairs of lung adenocarcinoma and matched paracancerous normal lung tissue samples, respectively.
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Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue
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Expression data from Lung cancer
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Eighty-three common differentially expressed genes
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Identification of DEGs in lung adenocarcinoma
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Table 1
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Eighty-three common differentially expressed genes
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GO enrichment analysis of differentially expressed genes
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GO and KEGG pathway analysis
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Table 2
caption
GO enrichment analysis of differentially expressed genes
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Term
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Count
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%
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0.18545
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0.035697
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33.54157
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0.022598
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23.71515
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0.00188
[5]
2.203801
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0.007307
[5]
8.318523
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4
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[4]
0.025677
[5]
26.51256
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KEGG pathway enrichment analysis of differentially expressed genes
structure object · 5 fields
pmcid
PMC7797856
section_index
11
section_title
GO and KEGG pathway analysis
row_count
7
column_count
6
table object · 8 fields
label
Table 3
caption
KEGG pathway enrichment analysis of differentially expressed genes
rows array · 7 items
[0] array · 6 items
[0]
Pathway ID
[1]
Name
[2]
Count
[3]
%
[4]
P value
[5]
Genes
[1] array · 6 items
[0]
hsa04151
[1]
PI3K-Akt signaling pathway
[2]
7
[3]
5.9
[4]
0.021716
[5]
VWF, IL6, COMP, TEK, COL1A1, SPP1
[2] array · 6 items
[0]
hsa05144
[1]
Malaria
[2]
6
[3]
5.0
[4]
1.92E-05
[5]
IL6, CD36, COMP, PECAM1, ACKR1, HBB
[3] array · 6 items
[0]
hsa04510
[1]
Focal adhesion
[2]
6
[3]
5.0
[4]
0.009898
[5]
VWF, CAV2, CAV1, COMP, COL1A1, SPP1
[4] array · 6 items
[0]
hsa04512
[1]
ECM-receptor interaction
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5
[3]
4.2
[4]
0.002175
[5]
VWF, CD36, COMP, COL1A1, SPP1
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[0]
hsa04514
[1]
Cell adhesion molecules (CAMs)
[2]
5
[3]
4.2
[4]
0.016157
[5]
CLDN18, CLDN5, PECAM1, CDH3, CDH5
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[0]
hsa04670
[1]
Leukocyte transendothelial migration
[2]
4
[3]
3.3
[4]
0.037066
[5]
CLDN18, CLDN5, PECAM1, CDH5
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section[11]/table-wrap[2]#t3
[7] object · 16 fields
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asset:table:pmid:33447425:4
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jats-table
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application/vnd.jats.table+json
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read_table
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section[15]/table-wrap[1]#t4
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KEGG pathway enrichment analysis of 11 core genes
structure object · 5 fields
pmcid
PMC7797856
section_index
15
section_title
KEGG pathway analysis of core genes
row_count
8
column_count
6
table object · 8 fields
label
Table 4
caption
KEGG pathway enrichment analysis of 11 core genes
rows array · 8 items
[0] array · 6 items
[0]
Pathway ID
[1]
Name
[2]
Count
[3]
%
[4]
P value
[5]
Genes
[1] array · 6 items
[0]
hsa04151
[1]
PI3K-Akt signaling pathway
[2]
7
[3]
37.9
[4]
1.62E-06
[5]
VWF, IL6, COMP, TEK, COL1A1, SPP1
[2] array · 6 items
[0]
hsa04510
[1]
Focal adhesion
[2]
5
[3]
27.1
[4]
1.03E-04
[5]
VWF, CAV1, COMP, COL1A1, SPP1
[3] array · 6 items
[0]
hsa04512
[1]
ECM-receptor interaction
[2]
4
[3]
21.7
[4]
1.67E-04
[5]
VWF, COMP, COL1A1, SPP1
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[0]
hsa05144
[1]
Malaria
[2]
3
[3]
16.3
[4]
0.002099
[5]
IL6, COMP, PECAM1
[5] array · 6 items
[0]
hsa04620
[1]
Toll-like receptor signaling pathway
[2]
3
[3]
16.3
[4]
0.008016
[5]
IL6, SPP1
[6] array · 6 items
[0]
hsa04670
[1]
Leukocyte transendothelial migration
[2]
3
[3]
16.3
[4]
0.009717
[5]
CLDN5, PECAM1, CDH5
[7] array · 6 items
[0]
hsa04514
[1]
Cell adhesion molecules (CAMs)
[2]
3
[3]
16.3
[4]
0.016707
[5]
CLDN5, PECAM1, CDH5
locator
section[15]/table-wrap[1]#t4
gaps array · 6 items
[0]
asset:data:geo:GSE27262 not downloaded
[1]
asset:data:geo:GSE27262 licence not read
[2]
asset:data:geo:GSE32863 not downloaded
[3]
asset:data:geo:GSE32863 licence not read
[4]
asset:data:geo:GSE7670 not downloaded
[5]
asset:data:geo:GSE7670 licence not read
analysis object · 4 fields
state
partial
methods array · 4 items
[0] object · 10 fields
id
method-1
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
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band
medium
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
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network_analysis
specific
ppi_network
label
Network / pathway analysis
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[0]
build_graph
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centrality_scores
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pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
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edge_list
evidence object · 3 fields
score
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protein-protein interaction
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id
method-3
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network_analysis
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pathway_enrich
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parameters object · 0 fields
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edge_list
evidence object · 3 fields
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quote
pathway enrichment
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method-4
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null
specific
null
label
t-test
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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
Differentially expressed genes with an adjusted P value <0.05 and |logFC| >2 were considered statistically significant by using t-test.
targets array · 0 items
gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
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complete
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capability:computational_analysis
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meta_analysis
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machine_learning
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survival_analysis
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statistical_regression
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pca
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candidate
Automated Assays Laravel application and simulated adapters
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nothing:replicable_now
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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.
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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.
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tier
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[0]
GSE7670
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GSE27262
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GSE32863
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GSE7670
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GSE27262
[2]
GSE32863
statements array · 1 item
[0]
The data of three gene expression profiles (GSE7670, GSE27262, GSE32863) were downloaded from the database and included 26, 25, and 58 pairs of lung adenocarcinoma and matched paracancerous normal lung tissue samples, respectively.
basis
The paper names GSE7670, GSE27262, GSE32863 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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survival_km
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source:pmid:33447425
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asset:table:pmid:33447425:2
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asset:table:pmid:33447425:3
[5]
asset:table:pmid:33447425:4
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run_analysis
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data_gaps array · 1 item
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data:geo:GSE27262
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minimax
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minimax
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single_reader
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pmid:33447425
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source:pmid:33447425
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PMID 33447425 full text
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GEO GSE27262
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agent:corpus-assay-engine
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extraction
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[3]
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Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common d
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urvival analysis data of EGA, TCGA, and GEO (Affymetrix microarrays only) databases. In this study,
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33447425", "kind": "science-record", "revision": 8, "content_hash": "sha256:aa4551701055e619ea6c91289b26ec7d6c43d1ab84b06990b94ee5611bca4670", "created_at": "2026-11-01T15:41:22Z", "updated_at": "2026-11-01T22:02:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.", "doi": "10.21037/jtd-20-3453", "pmid": "33447425", "authors": ["Rong Yang", "Yuwei Zhou", "Chengli Du", "Yihe Wu"], "journal": "Journal of thoracic disease", "year": 2020, "abstract": "BACKGROUND: Lung adenocarcinoma is the main pathological type of non-small cell lung cancer (NSCLC). In this study, we analyzed the gene expression profile of lung adenocarcinoma tumor and paracancerous tissues by bioinformatics to assess the genes and signal pathways related to lung adenocarcinoma.\nMETHODS: The expression data of GSE7670, GSE27262, and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common differential expression genes of lung adenocarcinoma tumor and adjacent tissues. The STRING database was used to construct the protein-protein interaction (PPI) network of lung adenocarcinoma and mine the gene modules and core genes in the network, and the online tools, GEPIA and Kaplan-Meier plotter were used to further verify and analyze the core genes.\nRESULTS: There were 109 pairs of lung adenocarcinoma tissues and matched paracancerous normal lung tissues in the three data sets. Eighty-three differentially expressed genes were identified, including 16 up-regulated and 67 down-regulated genes, and 60 differentially expressed genes were successfully incorporated into the PPI network complex. Eleven core genes were identified in the PPI network complex, including three up-regulated (COMP, SPP1, COL1A1) and eight down-regulated genes (CDH5, CAV1, CLDN5, LYVE1, IL6, VWF, TEK, PECAM1). These core genes were verified by the GEPIA tumor database. Survival analysis showed that expression of the core genes was significantly related to the prognosis of lung adenocarcinoma. KEGG pathway analysis of core genes showed six genes (COMP, SPP1, COL1A1, IL6, VWF, TEK) were significantly enriched in the PI3K-Akt signaling-pathway (P=1.62E-06).\nCONCLUSIONS: By analyzing the differential expression genes of lung adenocarcinoma and paracancerous normal tissues with bioinformatics, 11 genes with significant differential expression and significant influence on prognosis were identified. The findings may provide new concepts for developing diagnosis and treatment targets and prognosis markers for lung adenocarcinoma.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes with an adjusted P value <0.05 and |logFC| >2 were considered statistically significant by using t-test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33447425", "locator": "sections[8] \"Statistical analysis\" paragraph 2", "quote": "Differentially expressed genes with an adjusted P value <0.05 and |logFC| >2 were considered statistically significant by using t-test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data of three gene expression profiles (GSE7670, GSE27262, GSE32863) were downloaded from the database and included 26, 25, and 58 pairs of lung adenocarcinoma and matched paracancerous normal lung tissue samples, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33447425", "locator": "sections[3] \"Microarray data information\" paragraph 2", "quote": "The data of three gene expression profiles (GSE7670, GSE27262, GSE32863) were downloaded from the database and included 26, 25, and 58 pairs of lung adenocarcinoma and matched paracancerous normal lung tissue samples, respectively."}], "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:33447425"], "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:GSE27262"], "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:GSE27262"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33447425", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-20-3453", "sha256": 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