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pmid:32547947

Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas.

Shuang Liu · Wenxin Wang · Yan Zhao · Kaige Liang · Yaojiang Huang

Frontiers in oncology · 2020 doi:10.3389/fonc.2020.00809

Paper

complete

Experiment

complete

Data

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Analysis

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Execution

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pmid:32547947
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2026-11-01T15:41:03Z
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title
Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas.
doi
10.3389/fonc.2020.00809
pmid
32547947
authors array · 5 items
[0]
Shuang Liu
[1]
Wenxin Wang
[2]
Yan Zhao
[3]
Kaige Liang
[4]
Yaojiang Huang
journal
Frontiers in oncology
year
2020
abstract
Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis. Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis. Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.
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The DEGs of PCa and normal tissues were detected by limma package of R software (5), P-Val < 0.05 and absolute logFC > 1. Using edgeR package of R software to normalize TCGA datasets and screen DEGs (6), P-Val < 0.05 and absolute logFC >1.
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sections[4] "Data Preprocessing and Differentially Expressed Genes (DEGs) Screening" paragraph 2
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The DEGs of PCa and normal tissues were detected by limma package of R software (5), P-Val < 0.05 and absolute logFC > 1. Using edgeR package of R software to normalize TCGA datasets and screen DEGs (6), P-Val < 0.05 and absolute logFC >1.
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Publicly available datasets were analyzed in this study. This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/.
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sections[17] "Data Availability Statement" paragraph 2
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Publicly available datasets were analyzed in this study. This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/.
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Integrative Genomic and Proteomic Analysis of Prostate Cancer Reveals Signatures of Metastatic Progression
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Gene Expression Profiling of Prostate Benign and Malignant Tissue
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Genome-wide analysis of gene expression patterns in human prostate cancer (PCa)
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Prognostic value of the seven genes in the PCa patients of the TCGA cohort.
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Prognostic value of the seven genes in the PCa patients of the TCGA cohort.
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Information of the three GEO datasets.
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GSE69223
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Meller et al. (17)
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The overlapping upregulated DEGs between GSE69223, GSE3325, GSE55945, and TCGA PRAD dataset.
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The overlapping upregulated DEGs between GSE69223, GSE3325, GSE55945, and TCGA PRAD dataset.
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The overlapping downregulated DEGs between GSE69223, GSE3325, GSE55945, and TCGA PRAD dataset.
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The overlapping downregulated DEGs between GSE69223, GSE3325, GSE55945, and TCGA PRAD dataset.
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Downregulated DEGs names
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SNX7,KLHL13,TRHDE,CYP3A5,GJA1,MAL,WFDC1,FLNC,B3GALT2,FOXF2,
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RRAS,CPNE5,MEIS1,CXCL13,PABPC4L,IGSF1,FILIP1L,MYOF,EPB41L3,
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GPRASP1,VIT,PDGFC,C2orf40,DSC3,C12orf75,EPHA2,GPX8,SLC24A3,
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CD200,C7,CRYAB,ZNF204P,SLC47A1,SCGB3A1,EPHB1,RCAN2,FHL1,ASB2,
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SLITRK6,CHRDL1,BCL11A,IL33,PRIMA1,GATA3,NEXN,SNHG18,PPP1R3C,
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[0]
LSAMP,UBXN10AS1,TMEM158,TENM2,PTGDS,FOXQ1,GPM6A,FOXF1,TGFB1I1,
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ACOX2,PARM1,HOXD10,RGN,KRT23,SYNDIG1,PGMAS1,WIF1,HSPB6,KCNJ8,
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HSPB3,EFS,PRTFDC1,SCGB1A1,AOC3,GSTP1,ZNF536,EDNRA,MIR100HG,
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SOWAHA,GSTM2,NSG1,MYH11,ROR2,SMR3B,NEURL1B,LINC00844,
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TMEM100,ID3,ACTC1,LAMB3,OGN,MYL9,LRCH2,IER3,SERPINF1,SYNPO2,
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DDIT4L,KIAA1210,PLN,PDZRN4,HSD11B1,IGDCC4,MEIS2,PYGM,PLA2G4A,
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DKK1,IP6K3,SMTNL2,SLC18A2,KRT14,CNN1,MYH6,JAKMIP1,CCK,ID1,
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KRT5,FLRT3,MME,SNX31,CYP4B1,LINC00261,MIR205HG,SLC39A2,UPK1A,
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BEX1,PTGS2,KRT13,TRG-AS1,TGM4,CD177,NEFH,SERPINB11
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section[10]/table-wrap[3]#T3
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asset:table:pmid:32547947:5
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jats-table
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application/vnd.jats.table+json
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Prognostic value of the seven genes in the PCa patients of the TCGA cohort.
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pmcid
PMC7277826
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14
section_title
Survival Analysis
row_count
9
column_count
6
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Table 4
caption
Prognostic value of the seven genes in the PCa patients of the TCGA cohort.
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[0]
Gene symbol
[1]
Univariate analysis
[2]
Multivariate analysis
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[1] array · 6 items
[0]
[1]
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[2]
p-value
[3]
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[4]
p-value
[5]
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[0]
BCO1
[1]
2.619(1.438–4.768)
[2]
0.002
[3]
1.023(1.006–1.040)
[4]
0.007
[5]
0.022
[3] array · 6 items
[0]
BAIAP2L2
[1]
1.644(1.187–2.278)
[2]
0.003
[3]
0.999 (0.997–1.000)
[4]
0.037
[5]
−0.002
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[0]
C7
[1]
0.657(0.491–0.879)
[2]
0.005
[3]
0.999 (0.999–1.000)
[4]
0.074
[5]
−0.001
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[0]
AP000844.2
[1]
1.743(1.2151–2.499)
[2]
0.003
[3]
1.004 (1.002–1.004)
[4]
<0.001
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0.004
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[0]
ASB9
[1]
1.828(1.315–2.541)
[2]
<0.001
[3]
1.002(1.002–1.006)
[4]
0.039
[5]
0.001
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[0]
MKI67P1
[1]
5.033(1.977–12.804)
[2]
0.001
[3]
2.288(1.307–4.005)
[4]
0.004
[5]
0.828
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[0]
TMEM272
[1]
2.545(1.479–4.380)
[2]
0.001
[3]
1.05(0.989–1.123)
[4]
0.106
[5]
0.053
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section[14]/table-wrap[1]#T4
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[0]
asset:data:geo:GSE3325 not downloaded
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asset:data:geo:GSE55945 not downloaded
[3]
asset:data:geo:GSE55945 licence not read
[4]
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[5]
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[0] object · 10 fields
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method-1
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differential_expression
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edger
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normalise_counts
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differential_expression_fit
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fdr_bh
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count_matrix_with_condition
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quote
The DEGs of PCa and normal tissues were detected by limma package of R software (5), P-Val < 0.05 and absolute logFC > 1. Using edgeR package of R software to normalize TCGA datasets and screen DEGs (6), P-Val < 0.05 and absolute logFC >1.
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time_to_event_with_censoring
evidence object · 3 fields
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method-3
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differential_expression
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limma_voom
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Differential expression analysis
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normalise_counts
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differential_expression_fit
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fdr_bh
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count_matrix_with_condition
evidence object · 3 fields
score
0.8
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high
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limma
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id
method-4
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network_analysis
specific
ppi_network
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Network / pathway analysis
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build_graph
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centrality_scores
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edge_list
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method-5
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network_analysis
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Network / pathway analysis
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pathway_enrich
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edge_list
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method-6
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diagnostic_evaluation
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ROC curve
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method.reader_differs_from_extractor
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complete
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capability:computational_analysis
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meta_analysis
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machine_learning
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survival_analysis
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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complete
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normalise_counts
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differential_expression_fit
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: edger.
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capability:computational_analysis
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owned
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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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R1
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true
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[0]
GSE69223
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GSE3325
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GSE55945
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GSE69223
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GSE3325
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GSE55945
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Publicly available datasets were analyzed in this study. This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/.
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The paper names GSE69223, GSE3325, GSE55945 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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normalise_counts
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differential_expression_fit
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run_analysis
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partial
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-10-31T18:20:22Z
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single_reader
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verbatim
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verbatim
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extraction
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complete
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minimax
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review.independent_reader
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review.human_critique
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absent
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pmid:32547947
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paper
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source:pmid:32547947
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manuscript
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PMID 32547947 full text
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data:geo:GSE3325
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dataset
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GEO GSE3325
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GEO GSE3325
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agent:corpus-assay-engine
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343fb30756ba6ec3
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f2132e96ae6c7a4e
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The DEGs of PCa and normal tissues were detected by limma package of R software (5), P-Val < 0.05 and absolute logFC > 1. Using edgeR package of R software to normalize TCGA datasets and screen DEGs (6), P-Val < 0.05 and absolute logFC >1.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32547947", "kind": "science-record", "revision": 8, "content_hash": "sha256:83486387bb6293a6313cae2d06f890ba14c496259ac85707f2aaf89338d9cdc6", "created_at": "2026-11-01T15:41:03Z", "updated_at": "2026-11-01T22:44:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas.", "doi": "10.3389/fonc.2020.00809", "pmid": "32547947", "authors": ["Shuang Liu", "Wenxin Wang", "Yan Zhao", "Kaige Liang", "Yaojiang Huang"], "journal": "Frontiers in oncology", "year": 2020, "abstract": "Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis. Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis. Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.", "claims": [{"id": "claim-method", "statement": "The DEGs of PCa and normal tissues were detected by limma package of R software (5), P-Val < 0.05 and absolute logFC > 1. 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This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32547947", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/."}], "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:32547947"], "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:GSE3325"], "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:GSE3325"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32547947", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2020.00809", "sha256": "sha256:17f80bc48c379fa3da6ec95e9f9e887cf1201e9ddbd66909f1b838c809939eb1", "bytes": 30650, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2020.00809", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7277826", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE3325", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3325", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE3325", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: http://www.ncbi.nlm.nih.gov/geo/ and http://cancergenome.nih.gov/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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