Project description:Intrinsic biological fluctuation and/or measurement error can obscure the association of gene expression patterns between RNA and protein levels. Appropriate normalisation of reverse-transcription quantitative PCR (RT-qPCR) data can reduce technical noise in transcript measurement, thus uncovering such relationships. The accuracy of gene expression measurement is often challenged in the context of cancer due to its genetic instability and ‘splicing weakness’. Here we sequenced the poly(A) cancer transcriptome of canine osteosarcoma using mRNA-Seq. Expressed sequences were resolved at the level of two consecutive exons to enable the design of exon-border spanning RT-qPCR assays and ranked for stability based on the coefficient of variation (CV). Using the same template type for RT-qPCR validation, i.e. poly(A) RNA, avoided skewing of stability assessment by circular RNAs (circRNAs) and/or rRNA deregulation. The strength of the relationship between mRNA expression of the tumour marker S100A4 and its proportion score of quantitative immunochemistry (qIHC) was introduced as an experimental read-out to fine-tune the normalisation choice. Together with the essential logit transformation of qIHC scores, this approach reduced the noise of measurement as demonstrated by uncovering a highly significant, strong association between mRNA and protein expressions of S100A4 (Spearman's coefficient r = 0.72 (p = 0.006)).

2020-03-26 | GSE147550 | GEO

Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method.

Project description: Not available

| S-EPMC7915004 | biostudies-literature

Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.

Project description: Not available

| S-EPMC5633490 | biostudies-literature