|
| Status |
Public on Jan 01, 2019 |
| Title |
Regressive 4 |
| Sample type |
RNA |
| |
|
| Source name |
Human preinvasive lung squamous cell carcinoma lesions_regressive
|
| Organism |
Homo sapiens |
| Characteristics |
tissue: Preinvasive lung squamous cell carcinoma lesions
lesion outcome: regression
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Laser-capture micro-dissection (LCM) was used to selectively isolate CIS cells for molecular analysis, reducing the extent of contamination by stromal cells. RNA was extracted using the High Pure FFPE RNA Kit (Roche Applied Science, West Sussex, UK) according to manufacturer’s protocol. Quantification was carried out using the Quant-iT RNA assay kit and the Qubit® 2.0 fluorometer (Life Technologies, Paisley, UK).
|
| Label |
biotin
|
| Label protocol |
Biotinylated cRNA were prepared with the Ambion MessageAmp kit for Illumina arrays.
|
| |
|
| Hybridization protocol |
Standard Illumina hybridization protocol
|
| Scan protocol |
Standard Illumina scanning protocol
|
| Data processing |
Raw gene expression data were expressed as log2 ratios of fluorescence intensities of the experimental samples. Quantile normalization was applied. Differential expression analysis was performed using the significance analysis of microarrays method as implemented in the samr Bioconductor package. Raw p-values were adjusted by the Benjamini-Hochberg procedure to give a FDR. A significance threshold of FDR < 0.01 was used to select differentially expressed genes.
|
| |
|
| Submission date |
Feb 07, 2017 |
| Last update date |
Jan 01, 2019 |
| Contact name |
Vitor H Teixeira |
| E-mail(s) |
v.teixeira@ucl.ac.uk
|
| Organization name |
University College London
|
| Department |
Medicine
|
| Lab |
Lungs for Lungs for Living Research Centre
|
| Street address |
5 University Street
|
| City |
London |
| ZIP/Postal code |
WC1E 6JF |
| Country |
United Kingdom |
| |
|
| Platform ID |
GPL18281 |
| Series (2) |
| GSE94611 |
Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis I |
| GSE108124 |
Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis |
|
