7/10. And where it matters most, early stage. The platform reached 73.7% pooled sensitivity for Stage I–II disease at 90% specificity, exactly where DNA-based assays tend to struggle. cfRNA looks like a genuine orthogonal layer to cfDNA.

5/10. We tamed the technical noise. A double-DNase protocol plus RUVg normalization stripped out platelet-driven “unwanted variation”, the single biggest confounder in cfRNA-seq. Clean signal in, better model out.

4/10. It’s real biology, not noise. The genes we found differentially expressed light up hallmark CRC processes: inflammation, EMT, hypoxia, coagulation, and the Warburg effect (glycolysis up, oxidative phosphorylation down). cfRNA is reading tumour biology from a blood draw.

3/10. So we built an optimized, end-to-end cfRNA platform and applied it to 505 plasma samples (255 healthy controls + 250 CRC patients) from Spanish biobanks, one standardized protocol, one uniform bioinformatics pipeline.

🫁 This Friday is World Lung Cancer Day. At Flomics, we’re developing RNA-based liquid biopsy tools for early, non-invasive detection. Every dataset matters. Every life matters. 🎗️ To patients, families & researchers: we see you, we work for you. #WorldLungCancerDay #EarlyDetection #Flomics

No endless waiting ⏳ No invasive procedures 💉 Just fast, accurate, and early answers ⚡ Our goal: detect cancer early through a simple blood test — powered by RNA🩸🧬 Behind every breakthrough is a dedicated team 🧬💻🧪 #LiquidBiopsy #RNA #CancerDetection #Bioinformatics #PrecisionMedicine #TeamScience

And now for a not-so-humble brag… We were thrilled when the analysis revealed our own in-house Flomics libraries, using a rigorous double-DNase digestion protocol, ranked as the highest-quality dataset among all whole-cfRNA-Seq methods! 🎉💪

Beware of confounders! We highlight how technical factors are often mixed with patient phenotype, making true, generalizable biomarker discovery a huge challenge.

Protocols are not created equal. We show how different workflows give you vastly different pictures of the transcriptome.

This technical noise is loud! The variation we found is greater than the biological variation across 29 different human tissues. Let that sink in.

It’s the method, not the biology. We found that inter-lab batch effects, genomic DNA contamination, and library diversity explain the main differences between samples.

We had a hunch, so we conducted a massive cross-study analysis of >2,300 samples from 15 studies, plus libraries generated in-house at Flomics. We used a uniform bioinformatics pipeline to see what was really going on. Results were… illuminating.