Seven non-Differentially Expressed ‘Dark Biomarkers’ Show Transcriptional Dysregulation in Chronic Lymphocytic Leukemia

Xin, Ruihao; Feng, Xin; Zhang, Hang; Wang, Yueying; Duan, Meiyu; Xie, Tunyang; Dong, Longlong; Yu, Qiong; Huang, Lan; Zhou, Fengfeng

doi:10.2217/pme-2022-0123

Cited by 2 publications

(4 citation statements)

References 58 publications

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“…This study searched for the dark biomarkers whose original expression levels did not show significantly differential expression (Gene_P > 0.05) while its mqTrans values showed (mqTrans_P < 0.05). [32][33][34] The dataset used in this search consisted of 42 281 gene features and 3501 TF features in each of the five datasets.…”

Section: Detection Of Dark Biomarkersmentioning

confidence: 99%

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

et al. 2023

Advanced Biology

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This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome‐based detections of biomarkers that are ignored by many existing studies.

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Section: Detection Of Dark Biomarkersmentioning

confidence: 99%

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

et al. 2023

Advanced Biology

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“…The mqTrans model was trained using the healthy control samples, and the predicted expression level of a screened gene F was ensured to be highly correlated with the real level using a PCC(mRNA'(F), mRNA(F)) > 0.5. So, a gene F's mqTrans feature, mqTrans(F), tends to be close to zero if this gene's transcription regulation in the current query sample is quantitatively maintained in the same pattern as the training healthy samples [23].…”

Section: Calculation Of the Mqtrans Featuresmentioning

confidence: 99%

“…Deep learning algorithms, like convolutional neural network (CNN), have been used to consolidate the cis signals in promoters and distal regulatory regions for the prediction of cell-type-specific gene expression [36]. But, the transcriptome is one of the OMIC types with the most abundant public datasets and open-source analysis tools [22,23,37].…”

Section: The Quantitative Transcription Regulatory Modelsmentioning

confidence: 99%

“…Our hypothesis is that previous studies have ignored many undifferentially expressed genes, whose transcription regulation may be quantitatively altered in a phenotype. We use the recently developed algorithm mqTrans [22,23] to quantitatively measure the transcription regulation machinery in healthy samples. Then, we screen three lung cancer datasets for undifferentially expressed genes whose quantitative transcription regulation is significantly altered in lung cancer samples compared to those in the healthy controls.…”

Section: Introductionmentioning

confidence: 99%

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

Xin,

Cheng,

Chi

et al. 2023

Genes

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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.

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Seven non-Differentially Expressed ‘Dark Biomarkers’ Show Transcriptional Dysregulation in Chronic Lymphocytic Leukemia

Cited by 2 publications

References 58 publications

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

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

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