Uncovering hidden biomarkers in systemic lupus erythematosus through mqTrans analysis

Transcriptomic data is a valuable resource in precision medicine, providing crucial insights into disease diagnosis and treatment. Differential regulation relationship analysis, focusing on changes in regulatory relationships between different phenotypes, is an essential research direction to elucidate the mechanisms of complex molecular networks. However, many existing studies overlook associations with non-differential genes that exhibit quantitative changes in phenotypes. This study analyzes transcriptomic features by examining correlations among high-dimensional features through feature construction. We define the difference between an mRNA feature's predicted and actual expression as the mqTrans feature and build a predictive model. Three features with no differential expression in the original transcriptomic values across three independent SLE datasets were identified as latent biomarkers. By constructing a PPI network of latent biomarkers and biomarkers at the original expression levels, we explore interactive relationships between genes and discover functional interactions involving the latent biomarker ANXA2 and seven hub genes. In conclusion, mqTrans analysis can uncover essential biomarkers that traditional differential expression analysis often misses.