Transcriptional Dysregulations of Seven Non-Differentially Expressed Genes as Biomarkers of Metastatic Colon Cancer

Author:

Lv Xiaoying12,Li Xue123,Chen Shihong12,Zhang Gongyou12,Li Kewei45,Wang Yueying45,Duan Meiyu45ORCID,Zhou Fengfeng45ORCID,Liu Hongmei124

Affiliation:

1. School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China

2. Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, China

3. School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China

4. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

5. College of Computer Science and Technology, Jilin University, Changchun 130012, China

Abstract

Background: Colon cancer (CC) is common, and the mortality rate greatly increases as the disease progresses to the metastatic stage. Early detection of metastatic colon cancer (mCC) is crucial for reducing the mortality rate. Most previous studies have focused on the top-ranked differentially expressed transcriptomic biomarkers between mCC and primary CC while ignoring non-differentially expressed genes. Results: This study proposed that the complicated inter-feature correlations could be quantitatively formulated as a complementary transcriptomic view. We used a regression model to formulate the correlation between the expression levels of a messenger RNA (mRNA) and its regulatory transcription factors (TFs). The change between the predicted and real expression levels of a query mRNA was defined as the mqTrans value in the given sample, reflecting transcription regulatory changes compared with the model-training samples. A dark biomarker in mCC is defined as an mRNA gene that is non-differentially expressed in mCC but demonstrates mqTrans values significantly associated with mCC. This study detected seven dark biomarkers using 805 samples from three independent datasets. Evidence from the literature supports the role of some of these dark biomarkers. Conclusions: This study presented a complementary high-dimensional analysis procedure for transcriptome-based biomarker investigations with a case study on mCC.

Funder

Guizhou Provincial Science and Technology Projects

Senior and Junior Technological Innovation Team

Science and Technology Foundation of the Health Commission of Guizhou Province

Fundamental Research Funds for the Central Universities, JLU

Publisher

MDPI AG

Subject

Genetics (clinical),Genetics

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