- Volume 16 Issue 7
DOI QR Code
Integrative Meta-Analysis of Multiple Gene Expression Profiles in Acquired Gemcitabine-Resistant Cancer Cell Lines to Identify Novel Therapeutic Biomarkers
- 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)
- Published : 2015.04.14
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.
meta-analysis;microarray;DEG;acquired drug resistance;gemcitabine
Supported by : Konkuk University
- Ali S, Ahmad A, Banerjee S, et al (2010). Gemcitabine sensitivity can be induced in pancreatic cancer cells through modulation of miR-200 and miR-21 expression by curcumin or its analogue CDF. Cancer Res, 70, 3606-17. https://doi.org/10.1158/0008-5472.CAN-09-4598
- Bleckmann A, Siam L, Klemm F, et al (2013). Nuclear LEF1/TCF4 correlate with poor prognosis but not with nuclear beta-catenin in cerebral metastasis of lung adenocarcinomas. Clin Exp Metastasis, 30, 471-82. https://doi.org/10.1007/s10585-012-9552-7
- Chen KG, Sikic BI (2012). Molecular pathways: regulation and therapeutic implications of multidrug resistance. Clin Cancer Res, 18, 1863-9. https://doi.org/10.1158/1078-0432.CCR-11-1590
- de Sousa Cavalcante L, Monteiro G (2014). Gemcitabine: metabolism and molecular mechanisms of action, sensitivity and chemoresistance in pancreatic cancer. Eur J Pharmacol, 741, 8-16. https://doi.org/10.1016/j.ejphar.2014.07.041
- Firoz A, Malik A, Singh SK, et al (2014). Comparative analysis of glycogene expression in different mouse tissues using RNA-Seq data. Int J Genomics, 2014, 837365.
- Foo J, Michor F (2014). Evolution of acquired resistance to anti-cancer therapy. J Theor Biol, 355, 10-20. https://doi.org/10.1016/j.jtbi.2014.02.025
- Griesmann H, Ripka S, Pralle M, et al (2013). WNT5A-NFAT signaling mediates resistance to apoptosis in pancreatic cancer. Neoplasia, 15, 11-22. https://doi.org/10.1593/neo.121312
- Gupta A, Schulze TG, Nagarajan V, et al (2012). Interaction networks of lithium and valproate molecular targets reveal a striking enrichment of apoptosis functional clusters and neurotrophin signaling. Pharmacogenomics J, 12, 328-41. https://doi.org/10.1038/tpj.2011.9
- Hung SW, Mody HR, Govindarajan R (2012). Overcoming nucleoside analog chemoresistance of pancreatic cancer: a therapeutic challenge. Cancer Lett, 320, 138-49. https://doi.org/10.1016/j.canlet.2012.03.007
- Khan S, Ebeling MC, Zaman MS, et al (2014). MicroRNA-145 targets MUC13 and suppresses growth and invasion of pancreatic cancer. Oncotarget, 5, 7599-609. https://doi.org/10.18632/oncotarget.2281
- Koyama-Nasu R, Nasu-Nishimura Y, Todo T, et al (2013). The critical role of cyclin D2 in cell cycle progression and tumorigenicity of glioblastoma stem cells. Oncogene, 32, 3840-5. https://doi.org/10.1038/onc.2012.399
- Li J, Wood WH, 3rd, Becker KG, et al (2007). Gene expression response to cisplatin treatment in drug-sensitive and drug-resistant ovarian cancer cells. Oncogene, 26, 2860-72. https://doi.org/10.1038/sj.onc.1210086
- Lin Q, Chen T, Lin Q, et al (2013). Serum miR-19a expression correlates with worse prognosis of patients with non-small cell lung cancer. J Surg Oncol, 107, 767-71. https://doi.org/10.1002/jso.23312
- Longley DB, Johnston PG (2005). Molecular mechanisms of drug resistance. J Pathol, 205, 275-92. https://doi.org/10.1002/path.1706
- McLachlan E, Shao Q, Wang HL, et al (2006). Connexins act as tumor suppressors in three-dimensional mammary cell organoids by regulating differentiation and angiogenesis. Cancer Res, 66, 9886-94. https://doi.org/10.1158/0008-5472.CAN-05-4302
- Mini E, Nobili S, Caciagli B, et al (2006). Cellular pharmacology of gemcitabine. Ann Oncol, 17, 7-12.
- Molina-Navarro MM, Trivino JC, Martinez-Dolz L, et al (2014). Functional networks of nucleocytoplasmic transport-related genes differentiate ischemic and dilated cardiomyopathies. A new therapeutic opportunity. PLoS One, 9, 104709. https://doi.org/10.1371/journal.pone.0104709
- Nakano Y, Tanno S, Koizumi K, et al (2007). Gemcitabine chemoresistance and molecular markers associated with gemcitabine transport and metabolism in human pancreatic cancer cells. Br J Cancer, 96, 457-63. https://doi.org/10.1038/sj.bjc.6603559
- Nepusz T, Yu H, Paccanaro A (2012). Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods, 9, 471-2. https://doi.org/10.1038/nmeth.1938
- Nguyen DX, Chiang AC, Zhang XH, et al (2009). WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell, 138, 51-62. https://doi.org/10.1016/j.cell.2009.04.030
- Pan XH (2012). Pathway crosstalk analysis based on protein-protein network analysis in ovarian cancer. Asian Pac J Cancer Prev, 13, 3905-9. https://doi.org/10.7314/APJCP.2012.13.8.3905
- Saiki Y, Yoshino Y, Fujimura H, et al (2012). DCK is frequently inactivated in acquired gemcitabine-resistant human cancer cells. Biochem Biophys Res Commun, 421, 98-104. https://doi.org/10.1016/j.bbrc.2012.03.122
- Saraswathy M, Gong S (2013). Different strategies to overcome multidrug resistance in cancer. Biotechnol Adv, 31, 1397-407. https://doi.org/10.1016/j.biotechadv.2013.06.004
- Shi WY, Liu KD, Xu SG, et al (2014). Gene expression analysis of lung cancer. Eur Rev Med Pharmacol Sci, 18, 217-28.
- Song GG, Kim JH, Seo YH, et al (2014). Meta-analysis of differentially expressed genes in primary Sjogren's syndrome by using microarray. Hum Immunol, 75, 98-104.
- Szakacs G, Paterson JK, Ludwig JA, et al (2006). Targeting multidrug resistance in cancer. Nat Rev Drug Discov, 5, 219-34. https://doi.org/10.1038/nrd1984
- Teraishi F, Zhang L, Guo W, et al (2005). Activation of c-Jun NH2-terminal kinase is required for gemcitabine's cytotoxic effect in human lung cancer H1299 cells. FEBS Lett, 579, 6681-7. https://doi.org/10.1016/j.febslet.2005.10.064
- Tooker P, Yen WC, Ng SC, et al (2007). Bexarotene (LGD1069, Targretin), a selective retinoid X receptor agonist, prevents and reverses gemcitabine resistance in NSCLC cells by modulating gene amplification. Cancer Res, 67, 4425-33. https://doi.org/10.1158/0008-5472.CAN-06-4495
- Toro-Dominguez D, Carmona-Saez P, Alarcon-Riquelme ME (2014). Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjogren inverted question marks syndrome uncovered through gene expression metaanalysis. Arthritis Res Ther, 16, 489. https://doi.org/10.1186/s13075-014-0489-x
- Toschi L, Cappuzzo F (2009). Gemcitabine for the treatment of advanced nonsmall cell lung cancer. Onco Targets Ther, 2, 209-17.
- Toschi L, Finocchiaro G, Bartolini S, et al (2005). Role of gemcitabine in cancer therapy. Future Oncol, 1, 7-17. https://doi.org/10.1517/147966188.8.131.52
- Tufman A, Huber RM (2010). Biological markers in lung cancer: A clinician's perspective. Cancer Biomark, 6, 123-35.
- Warde-Farley D, Donaldson SL, Comes O, et al (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 38, W214-20. https://doi.org/10.1093/nar/gkq537
- Xia J, Fjell CD, Mayer ML, et al (2013). INMEX--a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res, 41, 63-70. https://doi.org/10.1093/nar/gks1029
- Zhang X, Jin FS, Zhang LG, et al (2013). Predictive and prognostic roles of ribonucleotide reductase M1 in patients with pancreatic cancer treated with gemcitabine: a meta-analysis. Asian Pac J Cancer Prev, 14, 4261-5. https://doi.org/10.7314/APJCP.2013.14.7.4261