Seven non-differentially expressed 'dark biomarkers' show transcriptional dysregulation in chronic lymphocytic leukemia.

Aim: Transcriptional regulation is actively involved in the onset and progression of various diseases. This study used the feature-engineering approach model-based quantitative transcription regulation to quantitatively measure the correlation between mRNA and transcription factors in a reference dataset of chronic lymphocytic leukemia (CLL) transcriptomes. Methods: A comprehensive investigation of transcriptional regulation changes in CLL was conducted using 973 samples in six independent datasets. Results & conclusion: Seven mRNAs were detected to have significantly differential model-based quantitative transcription regulation values but no differential expression between CLL patients and controls. We called these genes 'dark biomarkers' because their original expression levels did not show differential changes in the CLL patients. The overlapping lncRNAs might have contributed their transcripts to the expression miscalculations of these dark biomarkers.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer.

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.