Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy

Hao Wang; Qiuyue Tian; Jie Zhang; Hongqi Liu; Jinxia Zhang; Weijie Cao; Xiaoyu Zhang; Xingang Li; Lijuan Wu; Manshu Song; Yuanyuan Kong; Wei Wang; Youxin Wang

doi:10.1007/s13167-021-00238-1

Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy

EPMA J. 2021 Apr 20;12(2):103-115. doi: 10.1007/s13167-021-00238-1. eCollection 2021 Jun.

Authors

Hao Wang^#^{1

2

3}, Qiuyue Tian^#², Jie Zhang², Hongqi Liu⁴, Jinxia Zhang², Weijie Cao^{2

3}, Xiaoyu Zhang^{2

5}, Xingang Li³, Lijuan Wu², Manshu Song³, Yuanyuan Kong¹, Wei Wang³, Youxin Wang²

Affiliations

¹ Department of Clinical Epidemiology and Evidence-Based Medicine, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
² Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China.
³ Center for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia.
⁴ Student Healthcare Center, Weifang University, Weifang, China.
⁵ Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China.

^# Contributed equally.

Abstract

The early identification of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) in chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising "omics" technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive identification and targeted prevention of SHS. To explore the potential biomarkers, biological functions, and signalling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Transcriptome analysis identified a total of 46 differentially expressed genes (DEGs), in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts were selected as candidate predictive biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub genes related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM, and TMEM98. A transcriptome predictive model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% confidence interval (CI): 73.9-92.7%), a specificity of 90.0% (95% CI: 82.4-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882-0.994). In the present study, we demonstrated that blood (buffy coat) samples appear to be a very promising and easily accessible biological material for the transcriptomic analyses focused on the objective identification of SHS by using our transcriptome predictive model. The pattern of particularly determined DEGs can be used as predictive transcriptomic biomarkers for the identification of SHS in an individual who may, subjectively, feel healthy, but at the level of subcellular mechanisms, the changes can provide early information about potential health problems in this person. Our findings also indicate the potential therapeutic targets in dealing with chronic diseases related to SHS, such as T2DM and CVD, and an early onset of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, as well as the findings suggest the targets for personalized interventions as promoted in PPPM.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00238-1.

Keywords: ATP-binding cassette transporter; Glucokinase regulator; Neurodegeneration; Novel transcriptomic biomarkers; Predictive biomarkers; Predictive preventive personalized medicine; Suboptimal health status; Transcriptome predictive model; Transcriptome profiling.