Deep transcriptome profiling of ovarian cancer cells using next-generation sequencing approach

Lisha Li; Jie Liu; Wei Yu; Xiaoyan Lou; Bingding Huang; Biaoyang Lin

doi:10.1007/978-1-62703-547-7_12

Deep transcriptome profiling of ovarian cancer cells using next-generation sequencing approach

Methods Mol Biol. 2013:1049:139-69. doi: 10.1007/978-1-62703-547-7_12.

Authors

Lisha Li¹, Jie Liu, Wei Yu, Xiaoyan Lou, Bingding Huang, Biaoyang Lin

Affiliation

¹ Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang Province, P R China.

PMID: 23913215
DOI: 10.1007/978-1-62703-547-7_12

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.

MeSH terms

Cell Line, Tumor
Cisplatin / therapeutic use
Drug Resistance, Neoplasm / genetics
Female
Gene Expression Profiling / methods*
Gene Expression Regulation, Neoplastic
High-Throughput Nucleotide Sequencing / methods*
Humans
Molecular Biology / methods*
Ovarian Neoplasms / drug therapy
Ovarian Neoplasms / genetics*
Ovarian Neoplasms / pathology

Substances

Cisplatin