Integrative Meta-Analysis of Multiple Gene Expression Profiles in Acquired Gemcitabine-Resistant Cancer Cell Lines to Identify Novel Therapeutic Biomarkers

In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem thatreduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal theoverall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that areabsolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis ofthree publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, usingthe 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, thetop 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in manycells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatorynetwork, a gene co-expression network, and a protein-protein interaction network. The identified DEGs werefunctionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathwayanalyses. By systemic combinational analysis of the three molecular networks, we could condense the total numberof DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patternsassociated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.