Long Noncoding RNA GUSBP3 And SAM4 Function As Potential Diagnostic Biomarkers of Type 2 Diabetes Mellitus In Kazak Populations In The Xinjiang Region of China

Background: The pathogenesis of T2DM is influenced by a combination of genetic and environmental factors, among which LncRNAs have a huge impact on diabetes. In this study, we investigated the diagnostic value of lncRNA GUSBP3 and lncRNA SAM4 for T2DM in Kazakhs in Xinjiang. Methods: I n this study, differentially expressed lncRNAs and mRNAs were screened by microarray analysis microarray in a Kazakh population in Xinjiang, and the expression of two candidate lncRNAs (lnc-GUSBP-3 and lnc-SAM-4) was further validated by quantitative real-time polymerization chain reaction (qRT-PCR). Conclusion: There were significant changes in lncRNA and mRNA in Kazakh T2DM patients. LncRNA-GUSBP3 and lncRNA-SAM4 may serve as potential diagnostic biomarkers for T2DM in Kazakhs in Xinjiang. 2-h postprandial glucose fasting insulin (FIns), fasting C-peptide, glycosylated hemoglobin and blood lipids] all participants. The following inclusion applied: long-term intermarriage with other ethnic groups within three no autoimmune no serious or kidney and no the hepatic,


SAM4 Background
Diabetes mellitus (DM) comprises a group of metabolic diseases characterized by chronic elevation of blood glucose levels, and is one of the most important non-communicable diseases currently threatening human health worldwide. It is estimated that about 285 million people are affected by DM worldwide, with 60% of these located in Asian countries [1]. China now has the highest disease burden of DM in the world. According to the latest national epidemiological survey data, 114 million adults over the age of 20 years have DM, representing a prevalence of 11.6%, and Type 2 diabetes mellitus (T2DM) accounts for >90% of cases [2]. The dramatic escalation in the prevalence of DM is mainly due to the increase in the total number of patients with T2DM. Therefore, in-depth study of the etiology and pathogenesis of T2DM is urgently required to devise strategies for its prevention and control.
Kazakhs, who represent the main ethnic minority in the Xinjiang region of China, have a unique lifestyle and eating habits. Kazakhs have a high intake of animal fat, with a diet consisting mainly of beef, lamb and dairy products, and rarely including vegetables and fruit. As a result of this imbalanced diet, obesity and insulin resistance are common in adults. Epidemiological surveys in 2010 showed that the obesity rate among Kazakhs was 40.1%, which was significantly higher than that in the local Han population (18.4%) [3]. However, the prevalence of T2DM in the Kazakh population was only 3.16% [4], which was significantly lower than the national level. This discrepancy is considered to be related to the unique genetic backgrounds and environments of the different ethnic groups. In recent years, a large number of studies have confirmed the involvement of epigenetics in the development of T2DM. For example, a study reported the epigenetic mechanism of PDX-1 involvement in T2DM, and Ling et al. investigated the relationship between metabolism-related epigenetics and T2DM [5] [6]. These studies revealed that noncoding RNAs are an important component of the epigenetic regulation of gene expression in the pathogenesis of diabetes.
Long noncoding RNAs (lncRNAs) are defined as functional RNA molecules containing >200 nucleotide units and with a structure that is similar to mRNA, but no protein-coding function [7]. LncRNAs can regulate gene expression at the epigenetic, transcriptional, and post-transcriptional levels [8], and they are involved in various important regulatory processes such as chromatin modification, transcriptional activation, transcriptional interference, and intranuclear transport [9]. Studies have indicated the involvement of lncRNAs in maintaining β-cell function and insulin signaling, which may influence the development of T2DM and its complications.
Consequently, lncRNAs have become a research hotspot in the field of T2DM. In terms of islet -cell function, lncRNA HI-LNC25 has been implicated in the development of pancreatic β-cells and regulation of insulin gene expression [10]. In terms of insulin resistance-related mechanisms, lncRNAs regulate the expression of insulin-like growth factor receptors via the insulin signaling system. For example, Zhu et al. found that lncRNA MEG3 acts as a molecular sponge for miRNA-214 to target activating transcription factor 4 (ATF4) in the regulation of gluconeogenesis, and ATF4 increases FoxO1 expression by affecting the transcriptional activity of forkhead box protein O1 (FoxO1), thereby enhancing hepatic insulin resistance [11].
In addition, in an investigation of the relationship between lncRNA-DRAIR and T2DM at the epigenetic level based on high-throughput sequencing analysis of CD14 + monocytes, Reddy et al. identified differences in the lncRNA-DRAIR expression profiles between T2DM patients and healthy controls, with lncRNA-DRAIR being downregulated in T2DM patients. Furthermore, this study revealed the involvement of lncRNA-DRAIR in the inflammatory complications of diabetes through regulation of the inflammatory phenotype of monocytes/macrophages [12]. Thus, based on the mounting evidence that lncRNAs play an important role in the pathogenesis of T2DM, we investigated the correlation between differentially expressed lncRNAs in peripheral blood cells of healthy controls and patients with T2DM in a Xinjiang Kazakh population and evaluated their use as potential biomarkers of T2DM this population.

Ethics and subjects
The study was conducted within the Kazakh ethnic population in Xinjiang Province, China, and it was carried out according to the principles of the Helsinki's Declaration and was approved by the Institutional Ethics Committee of the first Affiliated Glycosylated hemoglobin A1c (HbA1C) was measured using an HLC-723G8 instrument (Tosoh Corporation, Tokyo, Japan). Serum insulin levels were measured using an Access Immunoassay System (Beckman Coulter Unicel DxI 800). The steady-state model was used to assess the insulin resistance index (HOMA-IR) and islet β-cell function (HOMA-β), HOMA-IR = FIns × FPG/22.5; HOMA-β = 20 × FIns/(FPG-3.5) ( Table 1).

Identification of differentially expressed genes (DEGs)
Array signal intensities were analyzed with Expression Console software (version 1.4.1; Affymetrix). Briefly, raw data probes were normalized using robust multiarray analysis for background correction and a quantile algorithm. Then, the differential expression profiles and alternative splicing events within the different variants were defined by one-way analysis of variance using Transcriptome Analysis Console Software (version 3.1, Affymetrix). A threshold of a fold-change (FC) in expression ≥2.0 (up or down) and P ≤ 0.05 were applied to identify significant DEGs (Table 2).  Table 3. The specificity of the PCR reaction was analyzed according to the dissolution curve and the Ct value of each sample was obtained. The relative gene expression was calculated by the 2 −ΔΔCt method using β-actin (ACTB) as an internal reference according to the formula: ΔCt = average Ct value of the target gene − average Ct value of the internal reference gene [13].

Biological function analysis of lnc-SMA4 and lnc-GUSBP3
The biological functions of lnc-SMA4 and lnc-GUSBP3 were investigated by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, applied to determine the roles of these differentially expressed mRNAs in GO terms or pathways.

Statistical analysis
Categorical data expressed as numbers and percentages (including sex) were analyzed using the χ 2 test. Other data are expressed as mean ± standard deviation (SD) values or median and interquartile range. For data conforming to a normal distribution, Student's t-test was used for comparison between groups. Mann-Whitney U-tests were used for analysis of data with an abnormal distribution. All the statistical

Microarray analysis of lncRNA and mRNA expression profiles
We conducted a microarray analysis of PBMC collected from six patients with T2DM (three males and three females) and two healthy controls (one male and one female), all of Kazakh origin. In the microanalysis, we identified 89 differentially expressed lncRNAs (73 upregulated and 16 downregulated), and 147 differentially expressed mRNAs (124 upregulated and 23 downregulated) (Fig.1A,Fig.1B and Fig.2A,Fig.2B respectively).   Each column represents a sample, and each row represents a gene. DM, diabetes group, NC, healthy control group. Red represents high expression, green represents low expression.

GO analysis of differentially expressed mRNAs
The biological processes (partial), cellular components, and molecular functions of the differentially expressed mRNAs identified in the GO analysis are shown in Figure   3.t

Validation of two candidate lncRNAs
We identified two lncRNAs, lnc-SMA4 and lnc-GUSBP3, which were found to be

Correlation of lnc-SMA4 and lnc-GUSBP3 expression with parameters of T2DM
Spearman correlation analysis was used to investigate whether expression levels of lnc-SMA4 and lnc-GUSBP3 correlate with clinical parameters and experimental indices in the study participants. We found that lnc-GUSBP3 expression correlated positively with Cr and UA (P = 0.010 and P = 0.012, respectively) and negatively with FIns (P = 0.022). Lnc-SAM4 expression correlated positively with UA (P = 0.033) and negatively with LDL-C levels (P = 0.010) ( Table 4).

Evaluation of BMI, DBP, FIns, TG, lnc-GUSBP3 and lnc-SMA4 as risk factors for T2DM
We performed logistic regression analysis to evaluate BMI, DBP, FIns, TG, lnc-GUSBP3, and lnc-SMA4 as risk factors for T2DM ( Table 5). The model explained 64.2% (Nagelkerke R 2 ) of the variance in T2DM and correctly classified 81.7% of the cases. DBP, FIns, and lnc-GUSBP3 were found to be associated with T2DM. Specifically, lnc-GUSBP3 upregulation was associated with increased risk of developing T2DM (OR = 0.378, 95% CI, 0.149-0.958). However, lnc-SAM4 did not add significantly to the model prediction. Elevated DBP and decreased FIns were also linked to an increased risk of developing T2DM.

Diagnostic potential of lnc-GUSBP3 and lnc-SAM4
To assess whether these two lncRNAs can be used to distinguish T2DM patients from healthy controls, we performed ROC curve analysis and calculated AUC values for lnc-GUSBP3 and lnc-SAM4 in the two groups. As shown in Figure 5,

Discussion
The pathogenesis of T2DM is complex and there are many causative factors, but it is generally accepted that pancreatic β-cell dysfunction and insulin resistance are the most common and major causes of the disease. It was reported that the prevalence of diabetes in China was <1% in 1980, but in 2013, the prevalence of diabetes in China was nearly 11 times that recorded in 1980. This increase is closely related to poor dietary habits and lifestyles [14]. found that obesity-repressed LincIRS2 is controlled by MAFG and observed that genetic and RNAi-mediated reduction of LincIRS2 in lean mice resulted in elevated blood glucose, insulin resistance, and abnormal glucose efflux, thus demonstrating its association with the development of diabetes [17]. Similarly, Radhakrishnan et al. showed that the lncRNA MALAT1 regulates antioxidant defense in diabetic retinopathy through the Keap1-Nrf2 pathway [18]. Therefore, a comprehensive understanding of lncRNAs is of critical importance in clarifying the pathogenesis of reported that lncRNA uc.322 was associated with FIns, and that lncRNA uc.322 upregulation in pancreatic β-cells increased the expression of insulin transcription factors and promoted insulin secretion [19]. In addition, dysregulation of lncRNAs has been associated with the development of complications such as diabetic nephropathy [20], which may account for the association of lnc-GUSBP3 with Cr and UA. Furthermore, regression analysis showed that the lnc-SAM4 expression level was associated with LDL-C and UA concentrations. Related studies have also demonstrated the role of lncRNAs in lipid metabolism and adipogenesis [21,22]. For example, Li et al. found that knockdown of the lncRNA Gm10804 inhibited disorders of hepatic glucose and lipid metabolism in diabetic patients with nonalcoholic fatty liver disease [23]. Overexpression of lncRNA RP11-728F11.4 in cells caused elevated Na + /K + -ATPase activity, leading to increased intracellular cholesterol accumulation and production of proinflammatory factors [24]. In our study, lnc-GUSBP3 expression was not associated with metabolic factors such as HDL-C or LDL-C. This may be Some limitations of our study should be noted. First, the low prevalence of T2DM in Kazakhs limits the sample size. In addition, we did not assess differences in the expression profiles of lnc-GUSBP3 and lnc-SAM4 in patients with T2DM with different disease courses. In future studies, we plan to continue collections of blood from Kazakh patients with T2DM to analyze the expression levels of each lncRNA and further expand the sample size, to allow multivariate correlation analysis with body measurements, blood lipids, and biochemical pathways.

Conclusion
Lnc-GUSBP3 and lnc-SAM4 expression is significantly elevated in Xinjiang Kazakh T2DM patients compared with healthy controls, and correlates with FIns, LDL-C, Cr, and UA, thus implicating these two lncRNAs in T2DM pathogenesis. In addition, ROC curve analysis validated the potential of lnc-GUSBP3 and lnc-SAM4 as new diagnostic markers of T2DM.