| Chapter title |
Deep transcriptome profiling of ovarian cancer cells using next-generation sequencing approach.
|
|---|---|
| Chapter number | 12 |
| Book title |
Ovarian Cancer
|
| Published in |
Methods in molecular biology, January 2013
|
| DOI | 10.1007/978-1-62703-547-7_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-546-0, 978-1-62703-547-7
|
| Authors |
Lisha Li, Jie Liu, Wei Yu, Xiaoyan Lou, Bingding Huang, Biaoyang Lin, Li, Lisha, Liu, Jie, Yu, Wei, Lou, Xiaoyan, Huang, Bingding, Lin, Biaoyang |
| 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. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 26% |
| Student > Bachelor | 4 | 21% |
| Other | 3 | 16% |
| Student > Ph. D. Student | 2 | 11% |
| Student > Master | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 37% |
| Agricultural and Biological Sciences | 7 | 37% |
| Medicine and Dentistry | 2 | 11% |
| Unknown | 3 | 16% |