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pmid:30138363

Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer.

Yun Ye · Su-Liang Li · Sheng-Yu Wang

PloS one · 2018 doi:10.1371/journal.pone.0198055

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Experiment

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Data

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Analysis

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pmid:30138363
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science-record
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sha256:9cd94873b1041a498575097e1f4f5e8e77f00537583fffee021045c13269d9cc
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2026-11-01T15:40:36Z
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2026-11-01T22:00:00Z
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title
Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer.
doi
10.1371/journal.pone.0198055
pmid
30138363
authors array · 3 items
[0]
Yun Ye
[1]
Su-Liang Li
[2]
Sheng-Yu Wang
journal
PloS one
year
2018
abstract
PURPOSE: Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa are related to the abnormal regulation of various signalling pathways. An increasing number of studies have suggested that mRNAs, miRNAs, lncRNAs, and TFs could play important roles in various biological processes that are associated with cancer pathogenesis. This study aims to reveal functional genes and investigate the underlying molecular mechanisms of PCa with bioinformatics. METHODS: Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the online tool GEO2R based on the R software limma package. Interactions between differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of miRNAs, lncRNAs and mRNAs was selected to construct mRNA-miRNA-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. Protein-protein interaction (PPI) networks were constructed, and transcription factors were annotated. Expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis. RESULTS: The results demonstrate that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue. CONCLUSION: Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs established a new prostate cancer network. In addition, we conducted functional module analysis within the network. In conclusion, this study provides new insight for exploration of the molecular mechanisms of PCa and valuable clues for further research into the process of tumourigenesis and its development in PCa.
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The multiple t test was used to detect statistically significant genes at the same time with FDR correction.
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sections[4] "Identification of differentially expressed genes" paragraph 2
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The multiple t test was used to detect statistically significant genes at the same time with FDR correction.
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All relevant data are within the paper and its Supporting Information files.
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Identification of differentially expressed mRNAs, miRNAs and lncRNAs
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Top 10 significantly up-regulated and down-regulated mRNAs in the GSE46602 dataset.
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Top 10 significantly up-regulated and down-regulated lncRNAs in the GSE46602 dataset.
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Identification of differentially expressed mRNAs, miRNAs and lncRNAs
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Top 10 significantly up-regulated and down-regulated lncRNAs in the GSE46602 dataset.
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1.16E-01
[3]
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[4]
1.132616
[5]
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9.06E-04
[3]
2.61E-05
[4]
1.13526
[5]
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4.49E-02
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1.15E-02
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1.284279
[5]
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[1]
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[2]
6.32E-05
[3]
8.40E-07
[4]
1.310892
[5]
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[17] array · 6 items
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[1]
chr15:50354174–50358312
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8.11E-04
[3]
2.26E-05
[4]
1.367447
[5]
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[18] array · 6 items
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RP11-391M1.4
[1]
chr3:40466466–40468587
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[3]
5.87E-08
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1.489649
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7.32E-04
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1.96E-05
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1.673082
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3.44E-06
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Protein-protein interaction network (PPI) analysis
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Topology parameters of hub genes (degree >5) in the miRNA-mRNA-TF regulatory network.
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hsa-let-7a
[1]
0.001582278
[2]
1383.644882
[3]
14
[7] array · 4 items
[0]
hsa-let-7d
[1]
0.00174216
[2]
2553.049636
[3]
13
[8] array · 4 items
[0]
PIK3R1
[1]
0.002057613
[2]
2045.442867
[3]
12
[9] array · 4 items
[0]
DLG4
[1]
0.001869159
[2]
3167.065846
[3]
12
[10] array · 4 items
[0]
TGFBR1
[1]
0.001976285
[2]
3691.770395
[3]
11
[11] array · 4 items
[0]
KIT
[1]
0.001904762
[2]
1517.550434
[3]
11
[12] array · 4 items
[0]
hsa-miR-23b
[1]
0.001587302
[2]
1375.820161
[3]
10
[13] array · 4 items
[0]
PTEN
[1]
0.001923077
[2]
1345.754047
[3]
10
[14] array · 4 items
[0]
hsa-miR-301a
[1]
0.001610306
[2]
1995.023424
[3]
9
[15] array · 4 items
[0]
LDLR
[1]
0.001960784
[2]
1888.001784
[3]
9
[16] array · 4 items
[0]
FLT1
[1]
0.001930502
[2]
1179.255452
[3]
9
[17] array · 4 items
[0]
hsa-miR-200b
[1]
0.001445087
[2]
1353.57487
[3]
8
[18] array · 4 items
[0]
hsa-miR-148a
[1]
0.00140647
[2]
1235.527073
[3]
8
[19] array · 4 items
[0]
PPARGC1A
[1]
0.001801802
[2]
1571.190602
[3]
8
[20] array · 4 items
[0]
BCL2L11
[1]
0.001834862
[2]
1606.829419
[3]
8
[21] array · 4 items
[0]
NR3C1*
[1]
0.00177305
[2]
733.3472529
[3]
7
[22] array · 4 items
[0]
NRG1*
[1]
0.001872659
[2]
1359.250724
[3]
7
[23] array · 4 items
[0]
hsa-miR-98
[1]
0.001618123
[2]
802.5761652
[3]
7
[24] array · 4 items
[0]
hsa-miR-92b
[1]
0.001506024
[2]
1655.33632
[3]
7
[25] array · 4 items
[0]
GJA1
[1]
0.001745201
[2]
1149.048086
[3]
7
[26] array · 4 items
[0]
hsa-miR-96
[1]
0.001335113
[2]
1416.966025
[3]
6
[27] array · 4 items
[0]
hsa-miR-454
[1]
0.001420455
[2]
1854.243032
[3]
6
[28] array · 4 items
[0]
hsa-miR-145
[1]
0.001461988
[2]
1257.608267
[3]
6
[29] array · 4 items
[0]
TFRC
[1]
0.001821494
[2]
445.8511305
[3]
6
[30] array · 4 items
[0]
ITGB8
[1]
0.001808318
[2]
2026.300029
[3]
6
[31] array · 4 items
[0]
EPHA5
[1]
0.001689189
[2]
845.4402231
[3]
6
[32] array · 4 items
[0]
CAMK2D
[1]
0.001620746
[2]
1045.62274
[3]
6
locator
section[13]/table-wrap[1]#pone.0198055.t004
gaps array · 0 items
analysis object · 4 fields
state
partial
methods array · 4 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
0.7
band
medium
quote
limma
[1] object · 10 fields
id
method-2
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
Protein-protein interaction
[2] object · 10 fields
id
method-3
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
pathway enrichment
[3] object · 10 fields
id
method-4
family
null
specific
null
label
Multiple t-test with false discovery rate (FDR) correction
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
The multiple t test was used to detect statistically significant genes at the same time with FDR correction.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
blocked
tier
R4
blocked_on
data:accession_absent
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
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
R4
reachable
false
accessions array · 0 items
resolved_accessions array · 0 items
statements array · 1 item
[0]
All relevant data are within the paper and its Supporting Information files.
basis
The paper names no accession to its source data, so the data has to come from the authors.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 5 items
[0]
source:pmid:30138363
[1]
asset:table:pmid:30138363:1
[2]
asset:table:pmid:30138363:2
[3]
asset:table:pmid:30138363:3
[4]
asset:table:pmid:30138363:4
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:None
gaps array · 1 item
[0]
data:accession_absent
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:35:43Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
30170
completion_tokens
1340
cached_tokens
792
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:30138363
type
paper
label
pmid:30138363
[1] object · 3 fields
id
source:pmid:30138363
type
manuscript
label
PMID 30138363 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
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:30138363
type
paper
label
pmid:30138363
[1] object · 3 fields
id
source:pmid:30138363
type
manuscript
label
PMID 30138363 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-30138363
type
extraction
used array · 1 item
[0]
source:pmid:30138363
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-None
type
accession_resolution
used array · 1 item
[0]
data:geo:None
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:30138363
parent_hash
sha256:7e296dbec564b689be4683fa90734785d99dfd7335f2e2f85f50f2780aeed309
fork_of
null
gaps array · 6 items
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execution.not_run
[1]
method.reader_named_unmapped
[2]
review.human_critique
[3]
review.independent_reader
[4]
review.machine_critique
[5]
step:obtain_source_data blocked on data:accession_absent
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
differential_expression
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
limma
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30138363", "kind": "science-record", "revision": 7, "content_hash": "sha256:9cd94873b1041a498575097e1f4f5e8e77f00537583fffee021045c13269d9cc", "created_at": "2026-11-01T15:40:36Z", "updated_at": "2026-11-01T22:00:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer.", "doi": "10.1371/journal.pone.0198055", "pmid": "30138363", "authors": ["Yun Ye", "Su-Liang Li", "Sheng-Yu Wang"], "journal": "PloS one", "year": 2018, "abstract": "PURPOSE: Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa are related to the abnormal regulation of various signalling pathways. An increasing number of studies have suggested that mRNAs, miRNAs, lncRNAs, and TFs could play important roles in various biological processes that are associated with cancer pathogenesis. This study aims to reveal functional genes and investigate the underlying molecular mechanisms of PCa with bioinformatics.\nMETHODS: Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the online tool GEO2R based on the R software limma package. Interactions between differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of miRNAs, lncRNAs and mRNAs was selected to construct mRNA-miRNA-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. Protein-protein interaction (PPI) networks were constructed, and transcription factors were annotated. Expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis.\nRESULTS: The results demonstrate that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue.\nCONCLUSION: Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs established a new prostate cancer network. In addition, we conducted functional module analysis within the network. In conclusion, this study provides new insight for exploration of the molecular mechanisms of PCa and valuable clues for further research into the process of tumourigenesis and its development in PCa.", "claims": [{"id": "claim-method", "statement": "The multiple t test was used to detect statistically significant genes at the same time with FDR correction.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30138363", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The multiple t test was used to detect statistically significant genes at the same time with FDR correction."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data are within the paper and its Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30138363", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the paper and its Supporting Information 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:30138363"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30138363", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0198055", "sha256": "sha256:f56c916af3852eeaff206cdb19e4f115cefa8fa98856e330c9f4db49ca7743d7", "bytes": 35089, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0198055", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6107126", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:30138363:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:2e217ed4b8c3fd2d4f7007cce96d107c6b2e18b388a9de2610ef65066c3536be", "bytes": 1329, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#pone.0198055.t001", "version": null, "note": "Top 10 significantly up-regulated and down-regulated miRNAs in the GSE64318 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