Transcriptomic meta-analysis reveals biomarker pairs and key pathways in Tetralogy of Fallot

Abstract

Tetralogy of Fallot (TOF) is a cyanotic congenital condition contributed by genetic, epigenetic as well as environmental factors. We applied sparse machine learning algorithms to RNAseq and sRNAseq data to select the prospective biomarker candidates. Furthermore, we applied filtering techniques to identify a subset of biomarker pairs in TOF. Differential expression analysis disclosed 2757 genes and 214 miRNAs, which are dysregulated. Weighted gene co-expression network analysis on the differentially expressed genes extracted five significant modules that are enriched in GO terms, extracellular matrix, signaling and calcium ion binding. Also, voomNSC selected two genes and five miRNAs and transformed PLDA-predicted 72 genes and 38 miRNAs as prognostic biomarkers. Out of the selected biomarkers, miRNA target analysis revealed 14 miRNA–gene interactions. Also, 10 out of 14 pairs were oppositely expressed and four out of 10 oppositely expressed biomarker pairs shared common pathways of focal adhesion and P13K–Akt signaling. In conclusion, our study demonstrated the concept of biomarker pairs, which may be considered for clinical validation due to the high literature as well as experimental support.