Abstract
The next-generation sequencing technology allows identification and cataloging of almost all mRNAs, even those with only one or a few transcripts per cell. To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels, we applied two next-generation sequencing technologies to study two ovarian chemotherapy response models: the in vitro acquired cisplatin-resistant cell line model (IGROV-1-CP and IGROV1) and the in vivo ovarian cancer tissue resistant model. We identified 3,422 signatures (2,957 genes) that are significantly differentially expressed between IGROV1 and IGROV-1-CP cells (P < .001). Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lin B et al (2005) Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease. Cancer Res 65(8):3081–3091
Lin B, Wang J, Cheng Y (2008) Recent patents and advances in the next-generation sequencing technologies. Recent Pat Biomed Eng 1:60–67
Niedringhaus TP et al (2011) Landscape of next-generation sequencing technologies. Anal Chem 83(12):4327–4341
Mardis ER (2008) Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet 9:387–402
Cheng L, Xu H, Lin B (2012) The application of the next-generation sequencing technologies in cancer research. In: Juan H-F, Huang H-C (eds) Systems biology-applications in cancer-related research. World Scientific, Singapore
Kim JB et al (2007) Polony multiplex analysis of gene expression (PMAGE) in mouse hypertrophic cardiomyopathy. Science 316(5830):1481–1484
Cheng L et al (2010) Analysis of chemotherapy response programs in ovarian cancers by the next-generation sequencing technologies. Gynecol Oncol 117(2):159–169
Ruan X, Ruan Y (2011) Genome wide full-length transcript analysis using 5′ and 3′ paired-end-tag next generation sequencing (RNA-PET). Methods Mol Biol 809:535–562
Benard J et al (1985) Characterization of a human ovarian adenocarcinoma line, IGROV1, in tissue culture and in nude mice. Cancer Res 45(10):4970–4979
Okayama H, Berg P (1982) High-efficiency cloning of full-length cDNA. Mol Cell Biol 2(2):161–170
D'Alessio JM, Gerard GF (1988) Second-strand cDNA synthesis with E. coli DNA polymerase I and RNase H: the fate of information at the mRNA 5′ terminus and the effect of E. coli DNA ligase. Nucleic Acids Res 16(5):1999–2014
Li R et al (2008) SOAP: short oligonucleotide alignment program. Bioinformatics 24(5):713–714
Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18(11):1851–1858
Langmead B et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25
Wang L et al (2010) DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26(1):136–138
Trapnell C et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515
Mortazavi A et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140
Gao D et al (2010) A survey of statistical software for analysing RNA-seq data. Hum Genomics 5(1):56–60
Yao JQ, Yu F (2011) DEB: A web interface for RNA-seq digital gene expression analysis. Bioinformation 7(1):44–45
Kvam VM, Liu P, Si Y (2012) A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot 99(2):248–256
Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, New York
About this protocol
Cite this protocol
Li, L., Liu, J., Yu, W., Lou, X., Huang, B., Lin, B. (2013). Deep Transcriptome Profiling of Ovarian Cancer Cells Using Next-Generation Sequencing Approach. In: Malek, A., Tchernitsa, O. (eds) Ovarian Cancer. Methods in Molecular Biology, vol 1049. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-547-7_12
Download citation
DOI: https://doi.org/10.1007/978-1-62703-547-7_12
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-546-0
Online ISBN: 978-1-62703-547-7
eBook Packages: Springer Protocols