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pmid:32323816

Application of weighted gene co‑expression network analysis to explore the potential diagnostic biomarkers for colorectal cancer.

Liping Qin · Jianping Zeng · Nannan Shi · Liu Chen · Li Wang

Molecular medicine reports · 2020 doi:10.3892/mmr.2020.11047

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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title
Application of weighted gene co‑expression network analysis to explore the potential diagnostic biomarkers for colorectal cancer.
doi
10.3892/mmr.2020.11047
pmid
32323816
authors array · 5 items
[0]
Liping Qin
[1]
Jianping Zeng
[2]
Nannan Shi
[3]
Liu Chen
[4]
Li Wang
journal
Molecular medicine reports
year
2020
abstract
Colorectal cancer (CRC) is one of the most common malignant diseases in the world. Although mechanistic studies have been conducted on the pathogenesis of CRC, the molecular mechanism of CRC tumorigenesis remains unclear. In the present study, the weighted gene co‑expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC. Next, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the key module genes to analyze the functional pathways involved. The hub genes were screened using the Cytoscape platform and verified by a second GEO dataset, GSE21510. Finally, 10 hub genes were identified in 2 key modules (the green and brown modules) as the genes most significantly associated with the tumorigenesis of CRC. The 5 hub genes from the green module included collagen type I α1 chain, collagen type XII α1 chain, collagen triple helix repeat containing 1, inhibin subunit βa (INHBA) and chromobox 2 (CBX2), while the 5 hub genes from the brown module included bestrophin 2 (BEST2), carbonic anhydrase 2, glucagon, solute carrier family 4 member 4 and gliomedin. The 2 key modules with the 10 hub genes identified may regulate the occurrence and development of CRC through the extracellular matrix pathway, PI3K‑Akt and chemokine signaling pathways, thus providing a reference for understanding the complex mechanism of tumorigenesis in CRC. Of note, few studies have reported the pathogenesis of CRC with the 3 identified hub genes, INHBA, CBX2 and BEST2. Further investigation of the molecular mechanism of these genes in CRC is recommended.
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In the present study, the weighted gene co-expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC.
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In the present study, the weighted gene co-expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC.
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Clinical Significance of Osteoprotegerin Expression in Human Colorectal Cancer
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13497
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Homo sapiens
title
Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery
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2026-11-01T15:39:55Z
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identifier_resolution_only_no_download_no_licence_read
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GO enrichment analysis of green module and brown modules (only biological processes).
structure object · 5 fields
pmcid
PMC7185270
section_index
15
section_title
GO enrichment and KEGG pathway analyses of key modules
row_count
35
column_count
5
table object · 8 fields
label
Table I.
caption
GO enrichment analysis of green module and brown modules (only biological processes).
rows array · 35 items
[0] array · 5 items
[0]
Term
[1]
Description
[2]
Count
[3]
%
[4]
FDR
[1] array · 1 item
[0]
Green module
[2] array · 5 items
[0]
GO:0030574
[1]
Collagen catabolic process
[2]
25
[3]
3.205128
[4]
1.50×10−14
[3] array · 5 items
[0]
GO:0006954
[1]
Inflammatory response
[2]
55
[3]
7.051282
[4]
2.00×10−13
[4] array · 5 items
[0]
GO:0007155
[1]
Cell adhesion
[2]
58
[3]
7.435897
[4]
1.81×10−11
[5] array · 5 items
[0]
GO:0030198
[1]
Extracellular matrix organization
[2]
35
[3]
4.487179
[4]
4.26×10−10
[6] array · 5 items
[0]
GO:0030593
[1]
Neutrophil chemotaxis
[2]
20
[3]
2.564103
[4]
8.23×10−9
[7] array · 5 items
[0]
GO:0007267
[1]
Cell-cell signaling
[2]
38
[3]
4.871795
[4]
9.10×10−9
[8] array · 5 items
[0]
GO:0030199
[1]
Collagen fibril organization
[2]
16
[3]
2.051282
[4]
1.31×10−9
[9] array · 5 items
[0]
GO:0042060
[1]
Wound healing
[2]
21
[3]
2.692308
[4]
3.96×10−8
[10] array · 5 items
[0]
GO:0070098
[1]
Chemokine-mediated signaling pathway
[2]
19
[3]
2.435897
[4]
3.11×10−7
[11] array · 5 items
[0]
GO:0006935
[1]
Chemotaxis
[2]
24
[3]
3.076923
[4]
5.95×10−7
[12] array · 5 items
[0]
GO:0060326
[1]
Cell chemotaxis
[2]
17
[3]
2.179487
[4]
5.16×10−6
[13] array · 5 items
[0]
GO:0002548
[1]
Monocyte chemotaxis
[2]
13
[3]
1.666667
[4]
8.20×10−5
[14] array · 5 items
[0]
GO:0008284
[1]
Positive regulation of cell proliferation
[2]
45
[3]
5.769231
[4]
1.24×10−4
[15] array · 5 items
[0]
GO:0006955
[1]
Immune response
[2]
42
[3]
5.384615
[4]
1.51×10−4
[16] array · 5 items
[0]
GO:0001501
[1]
Skeletal system development
[2]
22
[3]
2.820513
[4]
1.56×10−4
[17] array · 5 items
[0]
GO:0071346
[1]
Cellular response to interferon-γ
[2]
14
[3]
1.794872
[4]
4.19×10−4
[18] array · 5 items
[0]
GO:0071347
[1]
Cellular response to interleukin-1
[2]
15
[3]
1.923077
[4]
9.83×10−4
[19] array · 5 items
[0]
GO:0050900
[1]
Leukocyte migration
[2]
19
[3]
2.435897
[4]
2.31×10−3
[20] array · 5 items
[0]
GO:0022617
[1]
Extracellular matrix disassembly
[2]
15
[3]
1.923077
[4]
2.34×10−3
[21] array · 5 items
[0]
GO:0010628
[1]
Positive regulation of gene expression
[2]
29
[3]
3.717949
[4]
2.87×10−3
[22] array · 1 item
[0]
Brown module
[23] array · 5 items
[0]
GO:0007586
[1]
Digestion
[2]
20
[3]
1.380262
[4]
3.25×10−5
[24] array · 5 items
[0]
GO:0034765
[1]
Regulation of ion transmembrane transport
[2]
25
[3]
1.725328
[4]
5.43×10−4
[25] array · 5 items
[0]
GO:0071294
[1]
Cellular response to zinc ion
[2]
10
[3]
0.690131
[4]
2.60×10-3
[26] array · 5 items
[0]
GO:0001764
[1]
Neuron migration
[2]
23
[3]
1.587302
[4]
2.94×10−3
[27] array · 5 items
[0]
GO:0006730
[1]
One-carbon metabolic process
[2]
12
[3]
0.828157
[4]
3.87×10−3
[28] array · 5 items
[0]
GO:1902476
[1]
Chloride transmembrane transport
[2]
21
[3]
1.449275
[4]
5.68×10−3
[29] array · 5 items
[0]
GO:0015701
[1]
Bicarbonate transport
[2]
14
[3]
0.966184
[4]
7.57×10−3
[30] array · 5 items
[0]
GO:0007218
[1]
Neuropeptide signaling pathway
[2]
21
[3]
1.449275
[4]
2.13×10−2
[31] array · 5 items
[0]
GO:0070098
[1]
Chemokine-mediated signaling pathway
[2]
17
[3]
1.173223
[4]
2.90×10−2
[32] array · 5 items
[0]
GO:0045926
[1]
Negative regulation of growth
[2]
9
[3]
0.621118
[4]
3.00×10−2
[33] array · 5 items
[0]
GO:0008202
[1]
Steroid metabolic process
[2]
13
[3]
0.89717
[4]
3.38×10−2
[34] array · 5 items
[0]
GO:0007267
[1]
Cell-cell signaling
[2]
37
[3]
2.553485
[4]
3.52×10−2
locator
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KEGG pathway enrichment analysis of green module and brown modules.
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PMC7185270
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GO enrichment and KEGG pathway analyses of key modules
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Table II.
caption
KEGG pathway enrichment analysis of green module and brown modules.
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[0] array · 6 items
[0]
Category
[1]
Term
[2]
Description
[3]
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[4]
%
[5]
FDR
[1] array · 1 item
[0]
Green module
[2] array · 6 items
[0]
KEGG
[1]
hsa04060
[2]
Cytokine-cytokine receptor interaction
[3]
40
[4]
5.128205
[5]
3.32×10−10
[3] array · 6 items
[0]
KEGG
[1]
hsa04974
[2]
Protein digestion and absorption
[3]
22
[4]
2.820513
[5]
7.29×10−8
[4] array · 6 items
[0]
KEGG
[1]
hsa04512
[2]
ECM-receptor interaction
[3]
18
[4]
2.307692
[5]
1.27×10−4
[5] array · 6 items
[0]
KEGG
[1]
hsa05146
[2]
Amoebiasis
[3]
19
[4]
2.435897
[5]
4.96×10−4
[6] array · 6 items
[0]
KEGG
[1]
hsa05323
[2]
Rheumatoid arthritis
[3]
16
[4]
2.051282
[5]
4.50×10−3
[7] array · 6 items
[0]
KEGG
[1]
hsa04151
[2]
PI3K-Akt signaling pathway
[3]
33
[4]
4.230769
[5]
2.73×10−2
[8] array · 6 items
[0]
KEGG
[1]
hsa04062
[2]
Chemokine signaling pathway
[3]
22
[4]
2.820513
[5]
4.61×10−2
[9] array · 6 items
[0]
KEGG
[1]
hsa04668
[2]
TNF signaling pathway
[3]
14
[4]
1.794872
[5]
7.10×10−1
[10] array · 6 items
[0]
KEGG
[1]
hsa04310
[2]
Wnt signaling pathway
[3]
16
[4]
2.05128
[5]
9.10×10−1
[11] array · 1 item
[0]
Brown module
[12] array · 6 items
[0]
KEGG
[1]
hsa00830
[2]
Retinol metabolism
[3]
24
[4]
1.656315
[5]
1.39×10−8
[13] array · 6 items
[0]
KEGG
[1]
hsa05204
[2]
Chemical carcinogenesis
[3]
23
[4]
1.587302
[5]
1.30×10−5
[14] array · 6 items
[0]
KEGG
[1]
hsa00982
[2]
Drug metabolism-cytochrome P450
[3]
20
[4]
1.380262
[5]
1.02×10−4
[15] array · 6 items
[0]
KEGG
[1]
hsa00910
[2]
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[3]
9
[4]
0.621118
[5]
8.46×10−3
[16] array · 6 items
[0]
KEGG
[1]
hsa00980
[2]
Metabolism of xenobiotics by cytochrome P450
[3]
18
[4]
1.242236
[5]
9.57×10−3
[17] array · 6 items
[0]
KEGG
[1]
hsa00140
[2]
Steroid hormone biosynthesis
[3]
15
[4]
1.035197
[5]
3.38×10−2
[18] array · 6 items
[0]
KEGG
[1]
hsa04978
[2]
Mineral absorption
[3]
13
[4]
0.89717
[5]
3.44×10−2
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In the present study, the weighted gene co-expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC.
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Automated Assays Laravel application and simulated adapters
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All data generated or analyzed during this study are included in this published article.
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The paper names GSE87211, GSE21510 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32323816", "kind": "science-record", "revision": 6, "content_hash": "sha256:11ec8918d5555024ecd0b3f93b3b09830ce6b3328fd138632a5f1f188d44b6b1", "created_at": "2026-11-01T15:41:00Z", "updated_at": "2026-11-01T21:23:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Application of weighted gene co‑expression network analysis to explore the potential diagnostic biomarkers for colorectal cancer.", "doi": "10.3892/mmr.2020.11047", "pmid": "32323816", "authors": ["Liping Qin", "Jianping Zeng", "Nannan Shi", "Liu Chen", "Li Wang"], "journal": "Molecular medicine reports", "year": 2020, "abstract": "Colorectal cancer (CRC) is one of the most common malignant diseases in the world. Although mechanistic studies have been conducted on the pathogenesis of CRC, the molecular mechanism of CRC tumorigenesis remains unclear. In the present study, the weighted gene co‑expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC. Next, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the key module genes to analyze the functional pathways involved. The hub genes were screened using the Cytoscape platform and verified by a second GEO dataset, GSE21510. Finally, 10 hub genes were identified in 2 key modules (the green and brown modules) as the genes most significantly associated with the tumorigenesis of CRC. The 5 hub genes from the green module included collagen type I α1 chain, collagen type XII α1 chain, collagen triple helix repeat containing 1, inhibin subunit βa (INHBA) and chromobox 2 (CBX2), while the 5 hub genes from the brown module included bestrophin 2 (BEST2), carbonic anhydrase 2, glucagon, solute carrier family 4 member 4 and gliomedin. The 2 key modules with the 10 hub genes identified may regulate the occurrence and development of CRC through the extracellular matrix pathway, PI3K‑Akt and chemokine signaling pathways, thus providing a reference for understanding the complex mechanism of tumorigenesis in CRC. Of note, few studies have reported the pathogenesis of CRC with the 3 identified hub genes, INHBA, CBX2 and BEST2. Further investigation of the molecular mechanism of these genes in CRC is recommended.", "claims": [{"id": "claim-method", "statement": "In the present study, the weighted gene co-expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32323816", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In the present study, the weighted gene co-expression network analysis was performed for the Gene Expression Omnibus (GEO) dataset GSE87211, in order to analyze the key modules involved in the pathogenesis of CRC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32323816", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this published article."}], "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:32323816"], "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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