Dataset Information

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.

SUBMITTER: Du J

PROVIDER: S-EPMC5360351 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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

Du Jiang J Zhang Lin L

International journal of molecular medicine 20170206 3

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 ...[more]

PMID: 28204826

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Project description:Asbestos-related lung cancer accounts for 4-12% of lung cancers worldwide. We have previously identified ADAM28 as a putative oncogene involved in asbestos-related lung adenocarcinoma (ARLC-AC). We hypothesised that similarly gene expression profiling of asbestos-related lung squamous cell carcinomas (ARLC-SCC) may identify candidate oncogenes for ARLC-SCC. We undertook a microarray gene expression study in 56 subjects; 26 ARLC-SCC (defined as lung asbestos body (AB) counts >20AB/gram wet weight (gww) and 30 non-asbestos related lung squamous cell carcinoma (NARLC-SCC; no detectable lung asbestos bodies; 0AB/gww). Microarray and bioinformatics analysis identified six candidate genes differentially expressed between ARLC-SCC and NARLC-SCC based on statistical significance (p<0.001) and fold change (FC) of >2-fold. Two genes MS4A1 and CARD18, were technically replicated by qRT-PCR and showed consistent directional changes. As we also found MS4A1 to be overexpressed in ARLC-ACs, we selected this gene for biological validation in independent test sets (one internal, and one external dataset (2 primary tumor sets)). MS4A1 RNA expression dysregulation was validated in the external dataset but not in our internal dataset, likely due to the small sample size in the test set as immunohistochemical (IHC) staining for MS4A1 (CD20) showed that protein expression localized predominantly to stromal lymphocytes rather than tumor cells in ARLC-SCC. We conclude that differential expression of MS4A1 in this comparative gene expression study of ARLC-SCC versus NARLC-SCC is a stromal signal of uncertain significance, and an example of the rationale for tumor cell enrichment in preparation for gene expression studies where the aim is to identify markers of particular tumor phenotypes. Finally, our study failed to identify any strong gene candidates whose expression serves as a marker of asbestos etiology. Future research is required to determine the role of stromal lymphocyte MS4A1 dysregulation in pulmonary SCCs caused by asbestos.

Dataset Information

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

Publications

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

Similar Datasets

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