Computational characterization of undifferentially expressed genes with altered transcription regulations in lung cancers

Transcriptome is an OMIC type with mature commercial production technologies and a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expressions of the individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expressions were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of the 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 fluctuation between the predicted and real expressions of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of 13 transcriptomic features with differentially expressed mqTrans features but without differential expressions on the original transcriptomic values in three independent datasets of lung cancers. These features were called dark biomarkers because they would be ignored by the conventional differential analysis. The detailed discussion of one dark biomarker GBP5 suggested that the antisense long non-coding RNA might have contributed to this interesting phenomenon.