Association between lncRNA and GCKR gene in type 2 diabetes mellitus

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies

Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS datasets from the GEO database. A total of 215 differentially expressed genes (DEGs) and 78 differentially expressed miRNAs (DEMs) were identified via "Limma" package. 60 DEGs, mainly enriched in abnormal transportation of structure and energy substances, were selected after protein-protein interaction (PPI) network construction, of which 20 were chosen for machine learning after three algorithms (betweenness, closeness, and degree) filtration using Cytoscape. Four overlapping DEGs (ACTN1, CFTR, GCKR, LAMA3) were finally selected as the candidate markers based on three machine-learning approaches (Lasso, random forest, and support vector machine-recursive feature elimination). Furthermore, we collected peripheral blood from MFS patients and healthy control to validate the findings and the results showed that compared with the control, the expression of the four DEGs was all statistically different in MFS patients validated by qRT-PCR. Besides, the area under the receiver operating characteristics curve was greater than 0.8 for each DEG. Single-sample gene-set enrichment analysis showed that the four DEGs were strongly associated with inflammation and myogenesis pathway. Finally, we constructed the mRNA-miRNA network based on the intersection of DEMs and predicted miRNAs targeting DEGs. In conclusion, our study partially provided four potential markers for MFS pathogenesis.Communicated by Ramaswamy H. Sarma.

Long noncoding RNAs as potential diagnostic biomarkers for diabetes mellitus and complications: A systematic review and meta-analysis

AIMS

Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences.

MATERIALS AND METHODS

We performed a systematic review and meta-analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022. We evaluated human case-control or cohort studies on differential lncRNA expression in type 2 diabetes mellitus or its associated comorbidities. We excluded studies if they were non-peer reviewed or published in languages other than English. From 2387 identified studies, we included 17 (4685 participants).

RESULTS

Analysis of the pooled data showed that lncRNAs had a diagnostic area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87), with a sensitivity of 0.79 (95% CI: 0.74-0.83) and a specificity of 0.75 (95% CI: 0.69-0.80). LncRNAs had an AUC of 0.65 for the diagnosis of prediabetes, with 82% sensitivity and 65% specificity.

CONCLUSIONS

LncRNAs may be promising diagnostic markers for type 2 diabetes mellitus and its complications.

Non-coding RNAs and type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a worldwide disease with rapidly increasing prevalence. This complex disorder caused by interplay between genetic predisposition factors, early developmental elements, diet and inactive lifestyle. Several researches have shown impact of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in the pathogenesis of this disorder. Several miRNAs such as miR-126, miR-222-3p, miR-182, let-7b-5p, and miR-1-3p have been down-regulated in different biological sources of patients with T2DM. Some other miRNAs including miR-21, miR-30d, miR-148a-3p, miR-146b and miR-486 have the opposite trends. In addition, a number of lncRNAs such as LY86-AS, HCG27_201, VIM-AS1, CTBP1-AS2, CASC2, GAS5, LINC-PINT, and MALAT1 have been altered in the peripheral blood, serum samples or tissues obtained from patients with T2DM. Taken together, both miRNAs and lncRNAs contribute to the development of T2DM and might be applied as markers or therapeutic molecules for this disorder.

Susceptibility genes of hyperuricemia and gout

Gout is a chronic metabolic disease that seriously affects human health. It is also a major challenge facing the world, which has brought a heavy burden to patients and society. Hyperuricemia (HUA) is the most important risk factor for gout. In recent years, with the improvement of living standards and the change of dietary habits, the incidence of gout in the world has increased dramatically, and gradually tends to be younger. An increasing number of studies have shown that gene mutations may play an important role in the development of HUA and gout. Therefore, we reviewed the existing literature and summarized the susceptibility genes and research status of HUA and gout, in order to provide reference for the early diagnosis, individualized treatment and the development of new targeted drugs of HUA and gout.

Long non-coding RNA screening and identification of potential biomarkers for type 2 diabetes

BACKGROUND

To investigate new lncRNAs as molecular markers of T2D.

METHODS

We used microarrays to identify differentially expressed lncRNAs and mRNAs from five patients with T2D and paired controls. Through bioinformatics analysis, qRT-PCR validation, ELISA, and receiver operating characteristic (ROC) curve analysis of 100 patients with T2D and 100 controls to evaluate the correlation between lncRNAs and T2D, and whether lncRNAs could be used in the diagnosis of T2D patients.

RESULTS

We identified 68 and 74 differentially expressed lncRNAs and mRNAs, respectively. The top five upregulated lncRNAs are ENST00000381108.3, ENST00000515544.1, ENST00000539543.1, ENST00000508174.1, and ENST00000564527.1, and the top five downregulated lncRNAs are TCONS_00017539, ENST00000430816.1, ENST00000533203.1, ENST00000609522.1, and ENST00000417079.1. The top five upregulated mRNAs are Q59H50, CYP27A1, DNASE1L3, GRIP2, and lnc-TMEM18-12, and the top five downregulated mRNAs are GSTM4, PODN, GLYATL2, ZNF772, and CLTC. Examination of lncRNA-mRNA interaction pairs indicated that the target gene of lncRNA XR_108954.2 is E2F2. Multiple linear regression analysis showed that XR_108954.2 (r = 0.387, p < 0.01) and E2F2 (r = 0.368, p < 0.01) expression levels were positively correlated with glucose metabolism indicators. Moreover, E2F2 was positively correlated with lipid metabolism indicators (r = 0.333, p < 0.05). The area under the ROC curve was 0.704 (95% CI: 0.578-0.830, p = 0.05) for lncRNA XR_108954.2 and 0.653 (95% CI: 0.516-0.790, p = 0.035) for E2F2.

CONCLUSIONS

This transcriptome analysis explored the aberrantly expressed lncRNAs and identified E2F2 and lncRNA XR_108954.2 as potential biomarkers for patients with T2D.

Integrated analysis of transcriptome profiling of lncRNAs and mRNAs in livers of type 2 diabetes mellitus

Long noncoding RNAs (lncRNAs) influence the progression of almost all human diseases, but the participation of lncRNAs in type 2 diabetes mellitus (T2DM) has not been fully elucidated. The present study aimed to systematically compare the transcriptome profiling of lncRNAs and mRNAs in livers between T2DM patients and controls, to identify key genes associated with T2DM pathogenesis, and to predict the underlying molecular mechanisms. As a result, a total of 1,512 differentially expressed (DE) lncRNAs and 1,923 DE mRNAs were identified through microarray analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that multiple metabolic processes were dysregulated such as small molecule, organic acid, lipid and branched chain amino acid metabolism. Protein-protein interaction network was constructed and 10 hub mRNAs were identified, including EHHADH, ATM, ACOX1, PIK3R1, EGFR, UQCRFS1, HMGCL, UQCRC2, NDUFS3 and F2. RT-qPCR was conducted to verify the validity of microarray results. Then, coding-noncoding co-expression network and competing endogenous RNA (ceRNA) network were analyzed to predict the lncRNA-mRNA and lncRNA-miRNA-mRNA regulatory patterns. Subsequently, 10 key intermediating miRNAs in ceRNA networks with a node degree > 80 were identified, including hsa-miR-5692a, hsa-miR-12136, hsa-miR-5680, hsa-miR-1305, hsa-miR-6833-5p, hsa-miR-7159-5p, hsa-miR-548as-3p, hsa-miR-6873-3p, hsa-miR-1290 and hsa-miR-4768-5p. In conclusion, the present study evaluated the transcriptome profiling of lncRNAs and mRNAs in livers from T2DM patients, with a value for understanding the molecular mechanism of disease pathogenesis and identifying effective biomarkers in clinical diagnosis.

Transcriptome profiling of lncRNA and co-expression network in the vaginal epithelial tissue of women with lubrication disorders

Background

Vaginal lubrication is a crucial physiological response that occurs at the beginning of sexual arousal. However, research on lubrication disorders (LD) is still in its infancy, and the role of long non-coding RNAs (lncRNAs) in LD remains unclear. This study aimed to explore the function of lncRNAs in the pathogenesis of vaginal LD.

Methods

The expression profiles of LD and normal control (NC) lncRNAs were examined using next-generation sequencing (NGS), and eight selected differentially expressed lncRNAs were verified by quantitative real-time PCR. We conducted GO annotation and KEGG pathway enrichment analyses to determine the principal functions of significantly deregulated genes. LncRNA-mRNA co-expression and protein-protein interaction (PPI) networks were constructed and the lncRNA transcription factors (TFs) were predicted.

Results

From the results, we identified 181,631 lncRNAs and 145,224 mRNAs in vaginal epithelial tissue. Subsequently, our preliminary judgment revealed a total of 499 up-regulated and 337 down-regulated lncRNAs in LD. The top three enriched GO items of the dysregulated lncRNAs included the following significant terms: “contractile fiber part,” “actin filament-based process,” and “contractile fiber”. The most enriched pathways were “cell-extracellular matrix interactions,” “muscle contraction,” “cell-cell communication,” and “cGMP-PKG signaling pathway”. Our results also showed that the lncRNA-mRNA co-expression network was a powerful platform for predicting lncRNA functions. We determined the three hub genes, ADCY5, CXCL12, and NMU, using PPI network construction and analysis. A total of 231 TFs were predicted with RHOXF1, SNAI2, ZNF354C and TBX15 were suspected to be involved in the mechanism of LD.

Conclusion

In this study, we constructed the lncRNA-mRNA co-expression network, predicted the lncRNA TFs, and comprehensively analyzed lncRNA expression profiles in LD, providing a basis for future studies on LD clinical biomarkers and therapeutic targets. Further research is also needed to fully determine lncRNA’s role in LD development.

Inhibition of lncRNA TCONS_00077866 Ameliorates the High Stearic Acid Diet-Induced Mouse Pancreatic β-Cell Inflammatory Response by Increasing miR-297b-5p to Downregulate SAA3 Expression

Long-term consumption of a high-fat diet increases the circulating concentration of stearic acid (SA), which has a potent toxic effect on β-cells, but the underlying molecular mechanisms of this action have not been fully elucidated. Here, we evaluated the role of long noncoding (lnc)RNA TCONS_00077866 (lnc866) in SA-induced β-cell inflammation. lnc866 was selected for study because lncRNA high-throughput sequencing analysis demonstrated it to have the largest fold-difference in expression of five lncRNAs that were affected by SA treatment. Knockdown of lnc866 by virus-mediated shRNA expression in mice or by Smart Silencer in mouse pancreatic β-TC6 cells significantly inhibited the SA-induced reduction in insulin secretion and β-cell inflammation. According to lncRNA-miRNAs-mRNA coexpression network analysis and luciferase reporter assays, lnc866 directly bound to miR-297b-5p, thereby preventing it from reducing the expression of its target serum amyloid A3 (SAA3). Furthermore, overexpression of miR-297b-5p or inhibition of SAA3 also had marked protective effects against the deleterious effects of SA in β-TC6 cells and mouse islets. In conclusion, lnc866 silencing ameliorates SA-induced β-cell inflammation by targeting the miR-297b-5p/SAA3 axis. lnc866 inhibition may represent a new strategy to protect β-cells against the effects of SA during the development of type 2 diabetes.

Diagnostic significance of serum lncRNA HOTAIR and its predictive value for the development of chronic complications in patients with type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) affects the social economy and quality of life, and has become a major threat to human health. This observation aimed to study the possibility of serum HOTAIR as a diagnostic index in patients with T2DM and to explore the prognostic potential of HOTAIR in the development of T2DM.

METHODS

The expression of HOTAIR in serum of 96 patients with T2DM and 82 healthy controls was detected by the qRT-PCR technique. The related biochemical indexes of all participants were determined, such as total cholesterol (TC) and fasting blood glucose (FBG). The value of serum HOTAIR in the diagnosis of T2DM in the two groups was analyzed by the ROC curve. Moreover, the prognostic value of HOTAIR on T2DM was examined by the K-M curve and COX multivariate analysis.

RESULTS

The results of the qRT-PCR analysis showed that the serum level of HOTAIR in patients with T2DM was significantly higher than that in healthy controls. ROC analysis showed that HOTAIR in serum was a diagnostic factor of T2DM. Further multivariate analysis showed that HOTAIR could be an independent biomarker in the prediction of chronic complications for T2DM patients, such as diabetic retinopathy and diabetic nephropathy.

CONCLUSIONS

We found the augment of HOTAIR expression was a character of T2DM. The high expression of serum HOTAIR was a potential non-invasive diagnostic marker and independent prognostic factor in patients with T2DM.

The Impact of lncRNAs in Diabetes Mellitus: A Systematic Review and In Silico Analyses

Long non-coding RNAs (lncRNAs) are non-coding transcripts that have emerged as one of the largest and diverse RNA families that regulate gene expression. Accumulating evidence has suggested a number of lncRNAs are involved in diabetes mellitus (DM) pathogenesis. However, results about lncRNA expressions in DM patients are still inconclusive. Thus, we performed a systematic review of the literature on the subject followed by bioinformatics analyses to better understand which lncRNAs are dysregulated in DM and in which pathways they act. Pubmed, Embase, and Gene Expression Omnibus (GEO) repositories were searched to identify studies that investigated lncRNA expression in cases with DM and non-diabetic controls. LncRNAs consistently dysregulated in DM patients were submitted to bioinformatics analysis to retrieve their target genes and identify potentially affected signaling pathways under their regulation. Fifty-three eligible articles were included in this review after the application of the inclusion and exclusion criteria. Six hundred and thirty-eight lncRNAs were differentially expressed between cases and controls in at least one study. Among them, six lncRNAs were consistently dysregulated in patients with DM (Anril, Hotair, Malat1, Miat, Kcnq1ot1, and Meg3) compared to controls. Moreover, these six lncRNAs participate in several metabolism-related pathways, evidencing their importance in DM. This systematic review suggests six lncRNAs are dysregulated in DM, constituting potential biomarkers of this disease.