[해외논문]Integrative Meta-Analysis of Multiple Gene Expression Profiles in Acquired Gemcitabine-Resistant Cancer Cell Lines to Identify Novel Therapeutic Biomarkers원문보기
Lee, Young Seok
(Department of Biochemistry, School of Medicine, Konkuk University)
,
Kim, Jin Ki
(Department of Biochemistry, School of Medicine, Konkuk University)
,
Ryu, Seoung Won
(Department of Biochemistry, School of Medicine, Konkuk University)
,
Bae, Se Jong
(Department of Biochemistry, School of Medicine, Konkuk University)
,
Kwon, Kang
(School of Korean Medicine, Pusan National University)
,
Noh, Yun Hee
(Department of Biochemistry, School of Medicine, Konkuk University)
,
Kim, Sung Young
(Department of Biochemistry, School of Medicine, Konkuk University)
In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic ...
In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.
In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.
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제안 방법
However, published lists of identified genes show large inconsistencies because of the small sample size, low sample quality, and different laboratory protocol and platform in each individual study. In order to overcome these limitations, we identified DEGs that consistently appeared in all pairwise samples by meta-analysis of multiple microarray datasets, and performed integrative analysis of systemic molecular networks at the gene and/or protein level, in order to establish a theoretical framework for prospective molecular biological and clinical experiments. To our knowledge, we are the first to perform a cross-platform meta-analysis of the gene expression profiles associated with AGR in different cancer cell lines.
The network was further subdivided into five functional modules that were closely connected by >20 nodes, using the fast-greedy HEN (G) algorithm of the Cytoscape GLay plug-in, followed by functional enrichment analysis according to GO hierarchy and KEGG pathway.
From fifteen distinct protein clusters surrounding hub proteins, with >15 nodes, twelve functional hub cluster proteins were specifically identified, based on their p value and node density derived using the Cytoscape ClusterONE plug-in followed by functional enrichment analysis by GO hierarchy, as follows: SYT1, FHL1, CCND2, GJA1, SORBS2, LEF1, SATB1, RRM1, FGF2, DCK, BATF, and SERPINB9.
In order to identify the network regulating gene expression of the top 50 up- and down-regulated DEGs, which might directly influence AGR, we analyzed potential regulatory elements that target the DEGs depending on their upstream DNA sequence (Table 4). The target sites of the following transcription factors were significantly enriched in the DEGs: JUN, LEF1, NFAT, MAZ, MLLT4, and TCF1.
대상 데이터
Functional Hub Clusters in PPI Network of the Identified DEGs. From PPI network of proteins encoded by the top 50 up- and down-regulated DEGs, twelve functional hub clusters were identified. The node and edge of each hub cluster stand for protein encoded by genes with the identified DEGs and interaction of the proteins, respectively.
The twelve functional hub DEGs identified in the PPI network were enriched using GO terms for biological processes with a close relationship to the AGR process, such as abnormal apoptosis (hub clusters 3, 4, 5, 9, and 12), membrane transport of small molecules (hub cluster 1), deregulated transcription (hub clusters 2, 6, 8, and 11), and arrested replication escape by reactivation of DNA synthesis (hub clusters 7, 8, and 10). By comparing four lists of DEGs, for the gene regulation network, gene co-expression network, PPI network, and the top 50 up- and down-regulated DEGs, seven DEGs were shortlisted as AGR candidate genes from the total of 158 DEGs identified by metaanalysis; these included four up-regulated DEGs (SYT1, GJA1, LEF1, and SATB1) and three down-regulated DEGs (FHL1, CCND2, and SORBS2) that appeared in all four lists. In particular, GJA1, LEF1, and CCND2 were affiliated to the lists of the top 20 up- and down-regulated DEGs more likely to be crucial for the etiology of AGR.
이론/모형
, 2014). The hypergeometric algorithm and Benjamini-Hochberg adjustment were used for statistical processing and multiple-test revision of the network analysis, respectively.
성능/효과
A total of 158 DEGs identified by the meta-analysis were classified according to GO hierarchy functional category (biological process, molecular function, and cellular component) and KEGG pathway, with a significance threshold of p<0.05 (Table 3).
In many studies, hub nodes have been found to be necessary factors for the specific function that is executed by their corresponding network in an organic system, and play important functions in maintaining that network within the system. The twelve functional hub DEGs identified in the PPI network were enriched using GO terms for biological processes with a close relationship to the AGR process, such as abnormal apoptosis (hub clusters 3, 4, 5, 9, and 12), membrane transport of small molecules (hub cluster 1), deregulated transcription (hub clusters 2, 6, 8, and 11), and arrested replication escape by reactivation of DNA synthesis (hub clusters 7, 8, and 10). By comparing four lists of DEGs, for the gene regulation network, gene co-expression network, PPI network, and the top 50 up- and down-regulated DEGs, seven DEGs were shortlisted as AGR candidate genes from the total of 158 DEGs identified by metaanalysis; these included four up-regulated DEGs (SYT1, GJA1, LEF1, and SATB1) and three down-regulated DEGs (FHL1, CCND2, and SORBS2) that appeared in all four lists.
In conclusion, by performing a cross-platform meta-analysis of three microarray datasets for different cancer cell lines with AGR, we have identified a total of 158 candidate DEGs that have a high probability of being involved in the molecular mechanism of AGR. We have also provided a comprehensive overview of the gene expression pattern of the AGR-related DEGs by attempting integrated in silico analysis of three molecular networks.
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