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pmid:30002985

Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis.

Ling Cao · Yan Chen · Miao Zhang · De-Quan Xu · Yan Liu · Tonglin Liu · Shi-Xin Liu · Ping Wang

PeerJ · 2018 doi:10.7717/peerj.5180

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Experiment

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:30002985
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science-record
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2026-11-01T15:40:35Z
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2026-11-01T22:43:27Z
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title
Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis.
doi
10.7717/peerj.5180
pmid
30002985
authors array · 8 items
[0]
Ling Cao
[1]
Yan Chen
[2]
Miao Zhang
[3]
De-Quan Xu
[4]
Yan Liu
[5]
Tonglin Liu
[6]
Shi-Xin Liu
[7]
Ping Wang
journal
PeerJ
year
2018
abstract
OBJECTIVE: Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC. METHODS: The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples. RESULTS: A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan-Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted. CONCLUSION: In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.
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By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen.
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The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database.
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The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R.
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The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network.
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By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen.
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By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen.
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MicroRNA profiling of gastric cancer
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dataset
label
GEO GSE13911
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:30002985
type
paper
label
pmid:30002985
[1] object · 3 fields
id
source:pmid:30002985
type
manuscript
label
PMID 30002985 full text
[2] object · 3 fields
id
data:geo:GSE13911
type
dataset
label
GEO GSE13911
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-30002985
type
extraction
used array · 1 item
[0]
source:pmid:30002985
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE13911
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE13911
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
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agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
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MiniMax literature reader
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
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lineage object · 4 fields
parent_id
pmid:30002985
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sha256:0c64228385d98f988686854d3329071c6e933c561f209516f7e1362d7f5f5928
fork_of
null
gaps array · 12 items
[0]
asset:data:geo:GSE13911 licence not read
[1]
asset:data:geo:GSE13911 not downloaded
[2]
asset:data:geo:GSE19826 licence not read
[3]
asset:data:geo:GSE19826 not downloaded
[4]
asset:data:geo:GSE79973 licence not read
[5]
asset:data:geo:GSE79973 not downloaded
[6]
asset:data:geo:GSE93415 licence not read
[7]
asset:data:geo:GSE93415 not downloaded
[8]
execution.not_run
[9]
review.human_critique
[10]
review.independent_reader
[11]
review.machine_critique
assays array · 3 items
[0] object · 11 fields
role
data_generation
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
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source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
plan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples. RESULTS: A t
order_basis
paper_text_order:960
id
assay:1
[1] object · 11 fields
role
data_generation
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clinical_cohort
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99e1195c7d6f
required_operations array · 0 items
support_state
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source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.85
evidence array · 1 item
[0]
sue specimens We analyzed samples from 20 GC patients who underwent tumor resection at the Department
order_basis
paper_text_order:2519
id
assay:2
[2] object · 11 fields
role
analysis
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group_comparison
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required_operations array · 4 items
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[1]
welch_t
[2]
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[3]
group_comparison_anova
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source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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As an R programming language-based dataset analysis tool, GEO2R is based on the analysis of variance or t-test.
order_basis
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assay:3
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30002985", "kind": "science-record", "revision": 8, "content_hash": "sha256:f6cb097141fbb6b38937cc59ef689e8d5901a01666b0bbd4c6fc0f59973f68da", "created_at": "2026-11-01T15:40:35Z", "updated_at": "2026-11-01T22:43:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis.", "doi": "10.7717/peerj.5180", "pmid": "30002985", "authors": ["Ling Cao", "Yan Chen", "Miao Zhang", "De-Quan Xu", "Yan Liu", "Tonglin Liu", "Shi-Xin Liu", "Ping Wang"], "journal": "PeerJ", "year": 2018, "abstract": "OBJECTIVE: Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.\nMETHODS: The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.\nRESULTS: A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan-Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.\nCONCLUSION: In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.", "claims": [{"id": "claim-method", "statement": "As an R programming language-based dataset analysis tool, GEO2R is based on the analysis of variance or t-test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30002985", "locator": "sections[4] \"DEG identification\" paragraph 2", "quote": "As an R programming language-based dataset analysis tool, GEO2R is based on the analysis of variance or t-test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data are provided in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30002985", "locator": "sections[20] \"Additional Information and Declarations\" paragraph 5", "quote": "The raw data are provided in the Supplemental Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:30002985", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database."}, {"asset": "source:pmid:30002985", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R."}, {"asset": "source:pmid:30002985", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network."}, {"asset": "source:pmid:30002985", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen.", "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:30002985"], "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:GSE13911"], "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:GSE13911"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30002985", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.5180", "sha256": 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