3. Promising Transcriptomic Biomarkers Identified Using Meta-Analysis Approaches
Sweeney et al. [75] recently identified a transcriptomic signature to improve discrimination of patients with sepsis (infection) from those with sterile inflammation using blood samples. Their work analyzed publicly-available gene expression datasets from 22 independent cohorts (composed of 2903 microarrays in total) and applied a meta-analysis strategy implementing both effect size and p-values of differential gene expression. The investigators identified 82 genes differentially expressed between sepsis and inflammation and then performed a greedy forward search to determine which combination of these 82 genes produced the best improvement of area under the curve (AUC) in their discovery datasets. This resulted in an 11-gene transcriptional signature that was applied to 15 independent validation cohorts and was found to improve discrimination of patients with infection from those with sterile inflammation compared to use of clinical data alone. This gene signature requires further validation using prospective cohorts, however its excellent discriminatory power in both the discovery and validation cohorts suggests that it is likely to become a useful clinical assay in the future.
Santiago and Potashkin [76] implemented a transcriptomic and network-based meta-analysis in NetworkAnalyst (Table 1) to identify potential key hub genes in the blood of patients with Parkinson’s disease (PD). Their analysis identified hepatocyte nuclear factor 4 alpha (HNF4A) and polypyrimidine tract binding protein 1 (PTBP1), as the most significant up- and down-regulated genes in blood samples from PD patients. The relative abundance of HFN4A mRNA was found to correlate with disease severity in PD and the results were validated using samples obtained from two independent clinical trials. The abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during a 3-year follow-up period suggesting that these biomarkers may be useful for monitoring disease-modifying therapies for PD.