patient id: DOD39 disease state: prostate cancer tissue: serum sample type: serum EVs ap: 1 grade of prostate cancer at biopsy: high-grade (1) capra risk: INTERMEDIATE capra score: 5 fraction: microRNA
Treatment protocol
n/a
Growth protocol
n/a
Extracted molecule
total RNA
Extraction protocol
Total RNA was extracted with the miRNeasy Serum/Plasma kit (Qiagen).
Label
SYBR Green
Label protocol
PCR assay were performed using a miRCURY LNA miRNA Custom PCR Panel Catalog#YCA25430 (Qiagen) following the manufacturers instructions. Reverse transcription was performed from 5ยต L total RNA using the miRCURY LNA RT Kit (Qiagen). Quantitative real-time PCR were performed (Applied Biosystems ViiA 7) with 40 cycles at 95C for 10 seconds and 56C for 60 seconds.
Hybridization protocol
n/a
Scan protocol
n/a
Data processing
The raw data (raw CT) values were input into GeneGlobe with a CT cutoff value of 33. In the GeneGlobe preprocessing, all CTs greater than 33 were changed to a value of 33 and the CTs were adjusted for the interplate calibrator (UniSp3) according to the miRCURY LNA miRNA PCR Panels & Assays Data Analysis Handbook. Normalization values for each sample were calculated using the NormFinder method in GeneGlobe. The microRNAs used for normalization of serum samples included: miR-107, miR-24-3p, let-7i-5p, miR-19b-3p, miR-16-5p, miR-320a, miR-23a-3p, miR-21-5p, let-7b-5p, and miR-25-3p. The microRNAs included for normalization of serum EV samples included: included hsa-miR-107, hsa-miR-24-3p, hsa-miR-30c-5p, hsa-miR-93-5p, hsa-let-7i-5p, hsa-miR-222-3p, hsa-miR-27a-3p, hsa-miR-23a-3p, hsa-miR-21-5p, and hsa-miR-27b-3p. These normalized values were then used to make comparisons between groups (adverse pathology versus not adverse pathology) to determine significantly different microRNAs using p < 0.05 The significant microRNAs were then used to generate random forest models to predict adverse pathology The Fold Change Template shows the p-values for each microRNA. The microRNAs with p<0.05 were included in the random forest model to predict adverse pathology.
