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pmid:33552715

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis.

Qian Zhao · Yan Zhang · Shichun Shao · Yeqing Sun · Zhengkui Lin

PeerJ · 2021 doi:10.7717/peerj.10594

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title
Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis.
doi
10.7717/peerj.10594
pmid
33552715
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[0]
Qian Zhao
[1]
Yan Zhang
[2]
Shichun Shao
[3]
Yeqing Sun
[4]
Zhengkui Lin
journal
PeerJ
year
2021
abstract
BACKGROUND: Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. METHODS: A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). RESULTS: Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). CONCLUSION: MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.
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We found the methylation changes of the hub genes in GSE73003, and then used t-test to identify the genes whose methylation changed significantly.
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We found the methylation changes of the hub genes in GSE73003, and then used t-test to identify the genes whose methylation changed significantly.
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Construction of GCN and identification of gene modules
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The modularities of 3 algorithms.
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Construction of GCN and identification of gene modules
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Construction of GCN and identification of gene modules
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The 20 signaling pathways with the smallest P-value of genes in the key gene module (m1).
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Calculation results of nine algorithms.The row corresponds to gene, and the column corresponds to algorithm. Each value represents a score.
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Construction of PPI network and identification of hub genes
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Calculation results of nine algorithms.The row corresponds to gene, and the column corresponds to algorithm. Each value represents a score.
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[6]
Closeness
[7]
Radiality
[8]
Betweenness
[9]
Stress
[1] array · 10 items
[0]
NUSAP1
[1]
1.0893
[2]
51
[3]
51
[4]
19.988
[5]
9
[6]
101.33333
[7]
5.09091
[8]
96.90787
[9]
1902
[2] array · 10 items
[0]
MCM3
[1]
0.91716
[2]
69
[3]
69
[4]
22.824
[5]
23
[6]
111.5
[7]
5.24242
[8]
372.309
[9]
7082
[3] array · 10 items
[0]
TRMT6
[1]
0.47733
[2]
9
[3]
11
[4]
2.093
[5]
6
[6]
69.78333
[7]
4.4
[8]
1264.09432
[9]
10640
[4] array · 10 items
[0]
RFC3
[1]
0.88611
[2]
60
[3]
60
[4]
20.772
[5]
10
[6]
107.25
[7]
5.19394
[8]
542.48026
[9]
7874
[5] array · 10 items
[0]
POLA2
[1]
1.16211
[2]
36
[3]
36
[4]
16.597
[5]
1
[6]
93.33333
[7]
4.97576
[8]
27.46845
[9]
786
[6] array · 10 items
[0]
AURKA
[1]
0.83591
[2]
71
[3]
71
[4]
23.302
[5]
7
[6]
112.5
[7]
5.25455
[8]
944.81247
[9]
9646
[7] array · 10 items
[0]
CDC20
[1]
0.75746
[2]
80
[3]
80
[4]
23.462
[5]
5
[6]
117.66667
[7]
5.33939
[8]
1556.30666
[9]
16426
[8] array · 10 items
[0]
TOP2A
[1]
0.81632
[2]
76
[3]
76
[4]
23.363
[5]
2
[6]
115.66667
[7]
5.30909
[8]
1108.53404
[9]
13048
[9] array · 10 items
[0]
ECT2
[1]
1.16516
[2]
30
[3]
30
[4]
14.084
[5]
1
[6]
89.33333
[7]
4.90303
[8]
9.41338
[9]
276
[10] array · 10 items
[0]
TK1
[1]
1.14366
[2]
41
[3]
41
[4]
17.127
[5]
1
[6]
95.75
[7]
5
[8]
60.29409
[9]
1056
[11] array · 10 items
[0]
MCM2
[1]
0.93961
[2]
69
[3]
69
[4]
22.807
[5]
5
[6]
111.58333
[7]
5.25455
[8]
827.49161
[9]
8772
[12] array · 10 items
[0]
FEN1
[1]
0.82386
[2]
73
[3]
73
[4]
22.298
[5]
4
[6]
114.25
[7]
5.29697
[8]
1278.36825
[9]
12488
[13] array · 10 items
[0]
NOP58
[1]
0.2704
[2]
15
[3]
16
[4]
2.45
[5]
10
[6]
76.91667
[7]
4.61818
[8]
2059.91502
[9]
14078
[14] array · 10 items
[0]
GINS2
[1]
1.15247
[2]
50
[3]
50
[4]
19.731
[5]
1
[6]
101
[7]
5.09091
[8]
105.79312
[9]
1852
[15] array · 10 items
[0]
NCAPD2
[1]
1.19618
[2]
38
[3]
39
[4]
17.413
[5]
7
[6]
96.25
[7]
5.05455
[8]
1216.19414
[9]
12076
[16] array · 10 items
[0]
KPNA2
[1]
0.97174
[2]
51
[3]
51
[4]
19.066
[5]
8
[6]
104.41667
[7]
5.21212
[8]
1504.41719
[9]
14138
locator
section[14]/table-wrap[1]#table-4
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Methylation of hub genes.The P-value was obtained by t-test. Methylation status was obtained by analyzing GSE73003, and expression status was obtained by analyzing the TCGA data set.
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DNA methylation analysis of the hub genes
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Methylation of hub genes.The P-value was obtained by t-test. Methylation status was obtained by analyzing GSE73003, and expression status was obtained by analyzing the TCGA data set.
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[0] array · 5 items
[0]
Hub gene
[1]
Methylation status
[2]
P-value
[3]
Expression status
[4]
P-value
[1] array · 5 items
[0]
CDC20
[1]
Hypomethylation
[2]
8.94E−05
[3]
High expression
[4]
2.11E−32
[2] array · 5 items
[0]
TOP2A
[1]
Hypomethylation
[2]
4.41E−02
[3]
High expression
[4]
2.70E−41
[3] array · 5 items
[0]
TK1
[1]
Hypomethylation
[2]
4.78E−04
[3]
High expression
[4]
1.02E−49
[4] array · 5 items
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FEN1
[1]
Hypomethylation
[2]
4.06E−02
[3]
High expression
[4]
8.23E−62
locator
section[16]/table-wrap[1]#table-5
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asset:data:geo:GSE138485 not downloaded
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We found the methylation changes of the hub genes in GSE73003, and then used t-test to identify the genes whose methylation changed significantly.
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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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GSE73003
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The following information was supplied regarding data availability:Data is available at GEO (GSE138485, GSE73003) and in the Supplementary Files.
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The paper names GSE138485, GSE73003 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33552715", "kind": "science-record", "revision": 7, "content_hash": "sha256:ecaf7466dd79967b2669dbbc4f751e01534f7f42a5c30b5192308ee03dd36cdb", "created_at": "2026-11-01T15:41:24Z", "updated_at": "2026-11-01T22:03:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis.", "doi": "10.7717/peerj.10594", "pmid": "33552715", "authors": ["Qian Zhao", "Yan Zhang", "Shichun Shao", "Yeqing Sun", "Zhengkui Lin"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.\nMETHODS: A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).\nRESULTS: Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).\nCONCLUSION: MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.", "claims": [{"id": "claim-method", "statement": "We found the methylation changes of the hub genes in GSE73003, and then used t-test to identify the genes whose methylation changed significantly.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33552715", "locator": "sections[9] \"DNA methylation analysis\" paragraph 2", "quote": "We found the methylation changes of the hub genes in GSE73003, and then used t-test to identify the genes whose methylation changed significantly."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:Data is available at GEO (GSE138485, GSE73003) and in the Supplementary Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33552715", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:Data is available at GEO (GSE138485, GSE73003) and in the Supplementary Files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:33552715"], "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:GSE138485"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE138485"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33552715", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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