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pmid:31257520

Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA‑gene pairs in small‑cell lung cancer.

Yun Mao · Peng Xue · Linlu Li · Pengpeng Xu · Yafang Cai · Xuelei Chu · Pengyuan Jiang · Shijie Zhu

Molecular medicine reports · 2019 doi:10.3892/mmr.2019.10441

Paper

complete

Experiment

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Data

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Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:31257520
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science-record
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sha256:207a47b785fd068675200b24a260fd7f2031451e353288ea9fe43679a929c237
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2026-11-01T15:40:46Z
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2026-11-01T22:44:03Z
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paper object · 10 fields
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complete
title
Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA‑gene pairs in small‑cell lung cancer.
doi
10.3892/mmr.2019.10441
pmid
31257520
authors array · 8 items
[0]
Yun Mao
[1]
Peng Xue
[2]
Linlu Li
[3]
Pengpeng Xu
[4]
Yafang Cai
[5]
Xuelei Chu
[6]
Pengyuan Jiang
[7]
Shijie Zhu
journal
Molecular medicine reports
year
2019
abstract
Small‑cell lung cancer (SCLC) is a type of lung cancer with early metastasis, and high recurrence and mortality rates. The molecular mechanism is still unclear and further research is required. The aim of the present study was to examine the pathogenesis and potential molecular markers of SCLC by comparing the differential expression of mRNA and microRNA (miRNA) between SCLC tissue and normal lung tissue. A transcriptome sequencing dataset (GSE6044) and a non‑coding RNA sequence dataset (GSE19945) were downloaded from the Gene Expression Omnibus (GEO) database. In total, 451 differentially expressed genes (DEGs) and 134 differentially expressed miRNAs (DEMs) were identified using the R limma software package and the GEO2R tool of the GEO, respectively. The Gene Ontology function was significantly enriched for 28 terms, and the Kyoto Encyclopedia of Genes and Genomes database had 19 enrichment pathways, mainly related to 'cell cycle', 'DNA replication' and 'oocyte meiosis mismatch repair'. The protein‑protein interaction network was constructed using Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities in SCLC. The 1,402 miRNA‑gene pairs encompassed 602 target genes of the DEMs using miRNAWalk, which is a bioinformatics platform that predicts DEM target genes and miRNA‑gene pairs. There were 19 overlapping genes regulated by 32 miRNAs between target genes of the DEMs and DEGs. Bioinformatics analysis may help to better understand the role of DEGs, DEMs and miRNA‑gene pairs in cell proliferation and signal transduction. The related hub genes may be used as biomarkers for the diagnosis and prognosis of SCLC, and as potential drug targets.
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The DEGs in SCLC tissue compared with normal lung tissue were determined using limma package (18). DEGs were screened with a false discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1. The DEMs in SCLC tissue compared with normal lung tissue were confirmed using the GEO2R application from GEO. The false FDR corrected P<0.05 and | log FC|>1 were used as the screening thresholds.
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sections[5] "Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs)" paragraph 2
quote
The DEGs in SCLC tissue compared with normal lung tissue were determined using limma package (18). DEGs were screened with a false discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1. The DEMs in SCLC tissue compared with normal lung tissue were confirmed using the GEO2R application from GEO. The false FDR corrected P<0.05 and | log FC|>1 were used as the screening thresholds.
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sections[19] "Availability of data and materials" paragraph 2
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All data generated or analyzed during this study are included in this published article.
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A small-cell lung cancer subtype with good prognosis found by a three miRNA signature
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Homo sapiens
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Genetic programming and gene expression profiling for molecular discrimination and characterization of lung cancers
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Top 20 DEGs between SCLC tissues compared with normal lung tissues.
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DEGs and DEMs
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Table I.
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Top 20 DEGs between SCLC tissues compared with normal lung tissues.
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Adjusted P-value
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Log FC
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1.77×10−4
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DEGs and DEMs
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Table II.
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Top 20 DEMs between SCLC tissues compared with normal lung tissues.
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Adjusted P-value
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Table III.
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KEGG analysis of module 1 and module 2.
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[0]
Category
[1]
Term
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P-value
[3]
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Genes
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[0]
Module 1
[1]
hsa04110: Cell cycle
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[3]
18
[4]
CDC7, CDC6, CDK1, TTK, CDC20, ESPL1, PTTG1, MCM2, MCM3, CDK2, MCM6, CCNB1, MAD2L1, CCNB2, BUB1, PCNA, BUB1B, CCNA2
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[0]
[1]
hsa03030: DNA replication
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[3]
11
[4]
RFC5, PRIM1, RFC3, RFC4, POLE2, PCNA, MCM2, MCM3, RNASEH2A, FEN1, MCM6
[3] array · 5 items
[0]
[1]
hsa04114: Oocyte meiosis
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1.04×10−10
[3]
11
[4]
CCNB1, CDK1, MAD2L1, CCNB2, BUB1, FBXO5, AURKA, CDC20, ESPL1, PTTG1, CDK2
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[0]
[1]
hsa04914: Progesterone-mediated oocyte maturation
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[3]
7
[4]
CCNB1, CDK1, MAD2L1, CCNB2, BUB1, CCNA2, CDK2
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[0]
[1]
hsa03420: Nucleotide excision repair
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8.58×10−5
[3]
5
[4]
RFC5, RFC3, RFC4, POLE2, PCNA
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[0]
[1]
hsa03430: Mismatch repair
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1.99×10−4
[3]
4
[4]
RFC5, RFC3, RFC4, PCNA
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[0]
[1]
hsa04115: p53 signaling pathway
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3.43×10−4
[3]
5
[4]
CCNB1, CDK1, CCNB2, RRM2, CDK2
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[0]
[1]
hsa00240: Pyrimidine metabolism
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1.62×10−3
[3]
5
[4]
PRIM1, TYMS, POLE2, RRM2, RRM1
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hsa03410: Base excision repair
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1.20×10−2
[3]
3
[4]
POLE2, PCNA, FEN1
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[0]
Module 2
[1]
hsa04610: Complement and coagulation cascades
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6.19×10−4
[3]
5
[4]
A2M, C3, SERPINA1, CFD, PROS1
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[0]
[1]
hsa04062: Chemokine signaling pathway
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4.55×10−3
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4
[4]
CXCL1, CXCR4, GNB1, CCL19
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[1]
hsa00240: Pyrimidine metabolism
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1.50×10−2
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3
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NME5, CTPS1, ENTPD3
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asset:data:geo:GSE6044 licence not read
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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 · 1 field
fdr_threshold
expressed miRNAs, using FDR-corrected P<0
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
The DEGs in SCLC tissue compared with normal lung tissue were determined using limma package (18). DEGs were screened with a false discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1. The DEMs in SCLC tissue compared with normal lung tissue were confirmed using the GEO2R application from GEO. The false FDR corrected P<0.05 and | log FC|>1 were used as the screening thresholds.
targets array · 0 items
gaps array · 0 items
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
complete
tier
R1
blocked_on
nothing:replicable_now
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
R1
reachable
true
accessions array · 2 items
[0]
GSE6044
[1]
GSE19945
resolved_accessions array · 2 items
[0]
GSE6044
[1]
GSE19945
statements array · 1 item
[0]
All data generated or analyzed during this study are included in this published article.
basis
The paper names GSE6044, GSE19945 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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:31257520
[1]
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[2]
asset:table:pmid:31257520:1
[3]
asset:table:pmid:31257520:2
[4]
asset:table:pmid:31257520:3
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:GSE19945
gaps array · 0 items
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
2026-10-31T18:15:11Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
32486
completion_tokens
1287
cached_tokens
792
stages array · 1 item
[0] object · 4 fields
id
extraction
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complete
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MiniMax-M3.1-Flash
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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
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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:31257520
type
paper
label
pmid:31257520
[1] object · 3 fields
id
source:pmid:31257520
type
manuscript
label
PMID 31257520 full text
[2] object · 3 fields
id
data:geo:GSE19945
type
dataset
label
GEO GSE19945
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:31257520
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paper
label
pmid:31257520
[1] object · 3 fields
id
source:pmid:31257520
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manuscript
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PMID 31257520 full text
[2] object · 3 fields
id
data:geo:GSE19945
type
dataset
label
GEO GSE19945
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-31257520
type
extraction
used array · 1 item
[0]
source:pmid:31257520
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE19945
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE19945
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
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Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
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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:31257520
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sha256:bba0727dd270ef9c45d914f9da73a9877d588208b245de33f2b8cbdfc19c2d30
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null
gaps array · 8 items
[0]
asset:data:geo:GSE19945 licence not read
[1]
asset:data:geo:GSE19945 not downloaded
[2]
asset:data:geo:GSE6044 licence not read
[3]
asset:data:geo:GSE6044 not downloaded
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assays array · 2 items
[0] object · 11 fields
role
data_generation
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microarray
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99e1195c7d6f
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partial
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assay_lexicon
99e1195c7d6f
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0.93
evidence array · 1 item
[0]
Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA‑gene pairs in smal
order_basis
paper_text_order:0
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assay:1
[1] object · 11 fields
role
analysis
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differential_expression
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f2132e96ae6c7a4e
required_operations array · 3 items
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[1]
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[2]
fdr_bh
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supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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The DEGs in SCLC tissue compared with normal lung tissue were determined using limma package (18). DEGs were screened with a false discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1. The DEMs in SCLC tissue compared with normal lung tissue were confirmed using the GEO2R application from GEO. The false FDR corrected P<0.05 and | log FC|>1 were used as the screening thresholds.
order_basis
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Complete file
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The aim of the present study was to examine the pathogenesis and potential molecular markers of SCLC by comparing the differential expression of mRNA and microRNA (miRNA) between SCLC tissue and normal lung tissue. A transcriptome sequencing dataset (GSE6044) and a non‑coding RNA sequence dataset (GSE19945) were downloaded from the Gene Expression Omnibus (GEO) database. In total, 451 differentially expressed genes (DEGs) and 134 differentially expressed miRNAs (DEMs) were identified using the R limma software package and the GEO2R tool of the GEO, respectively. The Gene Ontology function was significantly enriched for 28 terms, and the Kyoto Encyclopedia of Genes and Genomes database had 19 enrichment pathways, mainly related to 'cell cycle', 'DNA replication' and 'oocyte meiosis mismatch repair'. The protein‑protein interaction network was constructed using Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities in SCLC. The 1,402 miRNA‑gene pairs encompassed 602 target genes of the DEMs using miRNAWalk, which is a bioinformatics platform that predicts DEM target genes and miRNA‑gene pairs. There were 19 overlapping genes regulated by 32 miRNAs between target genes of the DEMs and DEGs. Bioinformatics analysis may help to better understand the role of DEGs, DEMs and miRNA‑gene pairs in cell proliferation and signal transduction. The related hub genes may be used as biomarkers for the diagnosis and prognosis of SCLC, and as potential drug targets.", "claims": [{"id": "claim-method", "statement": "The DEGs in SCLC tissue compared with normal lung tissue were determined using limma package (18). DEGs were screened with a false discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1. The DEMs in SCLC tissue compared with normal lung tissue were confirmed using the GEO2R application from GEO. 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"hsa03430: Mismatch repair", "1.99×10−4", "4", "RFC5, RFC3, RFC4, PCNA"], ["", "hsa04115: p53 signaling pathway", "3.43×10−4", "5", "CCNB1, CDK1, CCNB2, RRM2, CDK2"], ["", "hsa00240: Pyrimidine metabolism", "1.62×10−3", "5", "PRIM1, TYMS, POLE2, RRM2, RRM1"], ["", "hsa03410: Base excision repair", "1.20×10−2", "3", "POLE2, PCNA, FEN1"], ["Module 2", "hsa04610: Complement and coagulation cascades", "6.19×10−4", "5", "A2M, C3, SERPINA1, CFD, PROS1"], ["", "hsa04062: Chemokine signaling pathway", "4.55×10−3", "4", "CXCL1, CXCR4, GNB1, CCL19"], ["", "hsa00240: Pyrimidine metabolism", "1.50×10−2", "3", "NME5, CTPS1, ENTPD3"]], "row_count": 13, "column_count": 5, "numeric_cells": 41, "header_rows": 1, "locator": "section[14]/table-wrap[1]#tIII-mmr-20-03-2199"}}], "gaps": ["asset:data:geo:GSE19945 not downloaded", "asset:data:geo:GSE19945 licence not read", "asset:data:geo:GSE6044 not downloaded", "asset:data:geo:GSE6044 licence not read"]}, "analysis": {"state": "complete", "methods": 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