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Pathway deviation-based biomarker and multi-effect target identification in asbestos-related squamous cell carcinoma of the lung.

Abstract Asbestos-related lung carcinoma is one of the most devastating occupational cancers, and effective techniques for early diagnosis are still lacking. In the present study, a systematic approach was applied to detect a potential biomarker for asbestos-related lung cancer (ARLC); in particular asbestos-related squamous cell carcinoma (ARLC-SCC). Microarray data (GSE23822) were retrieved from the Gene Expression Omnibus database, including 26 ARLC-SCCs and 30 non-asbestos-related squamous cell lung carcinomas (NARLC-SCCs). Differentially expressed genes (DEGs) were identified by the limma package, and then a protein-protein interaction (PPI) network was constructed according to the BioGRID and HPRD databases. A novel scoring approach integrating an expression deviation score and network degree of the gene was then proposed to weight the DEGs. Subsequently, the important genes were uploaded to DAVID for pathway enrichment analysis. Pathway correlation analysis was carried out using Spearman's rank correlation coefficient of the pathscore. In total, 1,333 DEGs, 391 upregulated and 942 downregulated, were obtained between the ARLC-SCCs and NARLC-SCCs. A total of 524 important genes for ARLC-SCC were significantly enriched in 22 KEGG pathways. Correlation analysis of these pathways showed that the pathway of SNARE interactions in vesicular transport was significantly correlated with 12 other pathways. Additionally, obvious correlations were found between multiple pathways by sharing cross-talk genes (EGFR, PRKX, PDGFB, PIK3R3, SLK, IGF1, CDC42 and PRKCA). On the whole, our data demonstrate that 8 cross-talk genes were found to bridge multiple ARLC-SCC-specific pathways, which may be used as candidate biomarkers and potential multi-effect targets. As these genes are involved in multiple pathways, it is possible that drugs targeting these genes may thus be able to influence multiple pathways simultaneously.
PMID
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Authors

Mayor MeshTerms

Biomarkers

Signal Transduction

Keywords
Journal Title international journal of molecular medicine
Publication Year Start




PMID- 28204826
OWN - NLM
STAT- MEDLINE
DA  - 20170216
DCOM- 20170315
LR  - 20170315
IS  - 1791-244X (Electronic)
IS  - 1107-3756 (Linking)
VI  - 39
IP  - 3
DP  - 2017 Mar
TI  - Pathway deviation-based biomarker and multi-effect target identification in
      asbestos-related squamous cell carcinoma of the lung.
PG  - 579-586
LID - 10.3892/ijmm.2017.2878 [doi]
AB  - Asbestos-related lung carcinoma is one of the most devastating occupational
      cancers, and effective techniques for early diagnosis are still lacking. In the
      present study, a systematic approach was applied to detect a potential biomarker 
      for asbestos-related lung cancer (ARLC); in particular asbestos-related squamous 
      cell carcinoma (ARLC-SCC). Microarray data (GSE23822) were retrieved from the
      Gene Expression Omnibus database, including 26 ARLC-SCCs and 30
      non-asbestos-related squamous cell lung carcinomas (NARLC-SCCs). Differentially
      expressed genes (DEGs) were identified by the limma package, and then a
      protein-protein interaction (PPI) network was constructed according to the
      BioGRID and HPRD databases. A novel scoring approach integrating an expression
      deviation score and network degree of the gene was then proposed to weight the
      DEGs. Subsequently, the important genes were uploaded to DAVID for pathway
      enrichment analysis. Pathway correlation analysis was carried out using
      Spearman's rank correlation coefficient of the pathscore. In total, 1,333 DEGs,
      391 upregulated and 942 downregulated, were obtained between the ARLC-SCCs and
      NARLC-SCCs. A total of 524 important genes for ARLC-SCC were significantly
      enriched in 22 KEGG pathways. Correlation analysis of these pathways showed that 
      the pathway of SNARE interactions in vesicular transport was significantly
      correlated with 12 other pathways. Additionally, obvious correlations were found 
      between multiple pathways by sharing cross-talk genes (EGFR, PRKX, PDGFB, PIK3R3,
      SLK, IGF1, CDC42 and PRKCA). On the whole, our data demonstrate that 8 cross-talk
      genes were found to bridge multiple ARLC-SCC-specific pathways, which may be used
      as candidate biomarkers and potential multi-effect targets. As these genes are
      involved in multiple pathways, it is possible that drugs targeting these genes
      may thus be able to influence multiple pathways simultaneously.
FAU - Du, Jiang
AU  - Du J
AD  - Department of Thoracic Surgery, First Affiliated Hospital of China Medical
      University, Shenyang, Liaoning 110001, P.R. China.
FAU - Zhang, Lin
AU  - Zhang L
AD  - Department of Thoracic Surgery, First Affiliated Hospital of China Medical
      University, Shenyang, Liaoning 110001, P.R. China.
LA  - eng
PT  - Journal Article
DEP - 20170206
PL  - Greece
TA  - Int J Mol Med
JT  - International journal of molecular medicine
JID - 9810955
RN  - 0 (Biomarkers)
SB  - IM
MH  - Asbestosis/*complications
MH  - *Biomarkers
MH  - Carcinoma, Squamous Cell/*etiology/*metabolism
MH  - Cluster Analysis
MH  - Computational Biology/methods
MH  - Databases, Genetic
MH  - Gene Expression Profiling
MH  - Gene Regulatory Networks
MH  - Humans
MH  - Lung Neoplasms/*etiology/*metabolism
MH  - Protein Interaction Mapping
MH  - Protein Interaction Maps
MH  - *Signal Transduction
EDAT- 2017/02/17 06:00
MHDA- 2017/03/16 06:00
CRDT- 2017/02/17 06:00
PHST- 2016/03/03 [received]
PHST- 2017/01/20 [accepted]
AID - 10.3892/ijmm.2017.2878 [doi]
PST - ppublish
SO  - Int J Mol Med. 2017 Mar;39(3):579-586. doi: 10.3892/ijmm.2017.2878. Epub 2017 Feb
      6.

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