As a reversible stage in advance of chronic disease, SHS proposes a new effective conception for population risk stratification under the perspective of PPPM. In addition, identifying key biological pathways relevant to the progression of SHS towards chronic diseases is considered as a novel and viable strategy for predictive diagnosis, targeted prevention and personalized therapy of chronic disease. With the emergence of RNA-Seq-based technologies, transcriptome profiling plays a significant role in deciphering gene expressions on RNA level and identifying molecular biomarkers. In the present study, 46 genes are differentially expressed between individuals with SHS and individuals with ideal health status. GO annotations and KEGG pathway enrichment analysis also revealed that several biological processes, such as ABC transporter and neurodegeneration, were related to SHS, and 10 hub genes with the highest degree of connectivity were identified. In addition, the AUC of the predictive diagnostic model based on transcriptomic biomarkers was 0.938 (95% CI: 0.882–0.994). These findings suggest that blood transcripts are potentially objective biomarkers for the SHS diagnosis. These transcriptomic biomarkers provide better insight into the critical genes associated with SHS and a deeper understanding of its biological processes.
In the present study, significantly lower level of GCRK mRNA was found in individuals with SHS. Glucokinase regulator protein, encoded by glucokinase regulator (GCKR) gene, is a hepatocyte-specific regulatory protein that inhibits glucokinase in liver cells [33]. Glucokinase, a hexokinase isozyme, is a key regulator of glucose disposal and storage, and responds to increases in circulating glucose concentration by initiating a signalling cascade that results in insulin secretion from pancreatic islets β cell [34]. Alterations in glucokinase expression and activity are associated with poorly controlled T2DM [35] and nonalcoholic fatty liver disease (NAFLD) [36]. It has been reported that common variants in the GCKR gene are associated with increased blood triglycerides [37, 38], lower fasting glucose [38], and NAFLD [39]. The glucokinase regulator protein, which binds with glucokinase and inactivates it from carbohydrate metabolism, is able to serve as a new treatment target for T2DM [40]. Significantly lower levels of GCKR mRNA in the SHS individuals in this study indicate that disorders of glucose metabolism might play an important role in the pathophysiology of SHS. Given the findings, GCKR mRNA might be a potential predictive diagnostic biomarker for the progression of SHS towards T2DM, and glucokinase regulator protein could be applied as potential therapeutic or preventive targets for SHS and T2DM.
Functional annotation and pathway enrichment analysis of DEGs provided an intuitive overview of the mechanism of SHS. The significant GO terms were BMPs signalling pathway and regulation of ERK1/2 cascade. BMPs, a group of signalling molecules, are part of the transforming growth factor-β superfamily of proteins. Initially discovered for their ability to induce bone formation [41], BMPs are now known to play important roles in the adult vascular endothelium, promoting angiogenesis and mediating oxidative stress [42]. Due to the critical roles of BMPs in maintenance of adult tissue homeostasis, it is found that dysregulation in BMPs signalling pathway contribute to various diseases, including cancer, skeletal disorders and CVD [43]. Our previous studies suggested that SHS might precede the occurrence of CVD [6, 7]. In the present study, significantly lower levels of BMPER and hemojuvelin BMP co-receptor (HJV) mRNA, which involved in the BMPs signalling pathway, were found in individuals with SHS. Our findings indicate that BMPs signalling pathway may play an important role in the pathophysiology of SHS, and transcripts of BMPER and HJV could be potential diagnosis biomarkers for SHS.
ERK1 and ERK2 cascade is key signalling pathway that regulates a large variety of cellular processes, including adhesion, migration, differentiation, metabolism, and proliferation [44]. This signalling cascade is dysregulated in a variety of diseases including CVD [45], insulin resistance [46], and inflammation [47]. In current study significantly lower levels of metallothionein 3 (MT3) and C-C motif chemokine ligand 3 (CCL3) mRNA in SHS individuals indicates that the ERK1 and ERK2 cascade is associated with the progression of SHS towards chronic disease, such as CVD and T2DM.
The KEGG enrichment analysis revealed that ABC transporter and neurodegeneration are the biological pathways related to SHS. ABC transporters are a large family of transmembrane proteins. These proteins bind ATP and use the energy to drive the transport of various molecules across cell membranes [48]. In human, the 48 ABC proteins are divided into seven subfamilies, from A to G, based on sequence and organization of their ATP-binding domain [48]. The ABCA4 protein transports vitamin A derivatives and perform a crucial role in the visual cycle [49]. In the present study, the downregulation of ABCA4 was observed in the individuals with SHS, which indicates that the decreased level of ABCA4 mRNA is associated with the SHS phenotype of eye, such as eye ache and fatigue.
The ABCG8 protein functions to facilitate the transport of sterols in the intestine and liver [50]. Our previous study found that steroid hormone biosynthesis pathway is disturbed in SHS individuals [16], which indicates that the upregulation of ABCG8 might be associated with the disorder of steroid hormone biosynthesis in SHS individuals. Chen and colleagues have observed that trimethylamine-N-oxide, a metabolite produced by gut microbiota, is associated with increased ABCG8 expression [51]. In addition, Zhu et al. has proved that intestinal microbiota, Enterococcus faecalis, increase the expression of ABCG8 [52]. Our previous study has found that alterations of intestinal microbiota occur in SHS individuals [15]. In the present study, the higher level of ABCG8 mRNA might be associated with the diversity of intestinal microbiota in SHS individuals. Significantly lower levels of TUBB3 and calcium/calmodulin dependent protein kinase II β (CAMK2B) mRNA, which involved in the Parkinson’s disease and neurodegeneration, were observed in SHS individuals. These findings indicate that neurodegeneration might be involved in the pathophysiology of SHS.
The PPI network enables the exploration and visualization of functional interactions between the DEGs. As shown in Fig. 5, GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM and TMEM98, were identified and selected as critical hub genes. Gap junction protein α1 (GJA1), also known as connexin 43 protein, is protein subunit that constitute gap junction channels [53]. The intercellular channels of gap junction facilitate the transfer of ions and small molecular from cell to cell, and are thought to modulate several processes, including embryogenesis, differentiation, and electrotonic coupling [54]. GJA1 expression is affected by several pathophysiological conditions, such as hypertension, hypercholesterolemia, and diabetes [55]. In the present study, the higher level of GJA1 mRNA in SHS individuals indicates that GJA1 mRNA could be associated with the progression of SHS phenotype towards CVD and T2DM. In addition, Squecco et al. has reported that the bioactive sphingolipid, sphingosine 1-phosphate, can enhance GJA1 protein expression [56]. Our previous study has found sphingolipids metabolism is the disturbed metabolic pathway related to SHS, and significantly higher levels of sphinganine 1-phosphate and sphingomyelin are observed in SHS individuals [16]. Given these findings, the upregulated GJA1 mRNA could be affected by the disturbed sphingolipids metabolism in SHS individuals. These critical genes play hub roles in predictive, preventive, and personalized medicine related to SHS, and be worthy of further investigation.
To establish a relatively accurate diagnosis model for individuals with SHS, a logistic regression analysis was performed based on the transcripts of 10 identified hub genes. ROC curve analysis showed that the predictive diagnosis model based on transcriptomic biomarkers can distinguish individuals with SHS from individuals with ideal health status with a sensitivity of 83.3%, a specificity of 90.0%, and an AUC of 0.938. These findings exhibit strong predictive abilities of transcriptomic biomarkers for SHS diagnosis. Blood transcripts are potentially objective biomarkers for the SHS diagnosis. The proposed transcriptomic biomarkers have a promising prospect of clinical application in the prediction and prevention of chronic disease.
To the best of our knowledge, this is the first study to screen transcriptomic biomarkers for SHS using RNA-Seq-based transcriptome profiling. Nevertheless, several limitations in the present study are noteworthy. First, our study is a case-control study with a relatively small sample size, hence the generalisation of these findings could be questioned. However, considering the fact that our study provides the original observations on the transcriptomic features of SHS population, the present study has provided a new idea that buffy coat transcripts might offer a novel alternative for the predictive diagnosis, targeted prevention and personalized treatment of chronic diseases. In addition, considering the quantitative accuracy of RNA-Seq technology, a RT‑qPCR study is underway against the same cohort to validate the putative transcript biomarkers and selected hub genes based on the findings in this study. Building on the present findings, further studies of larger cohorts from diverse geographical areas and populations with different age ranges are warranted.