Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition

Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach

Diabetes mellitus is a severe condition that has the potential to impair strength. The disease known as diabetes mellitus, which is a chronic condition, is brought on by a significant rise in blood glucose levels. The diagnosis of this condition is made using a variety of chemical and physical testing. Diabetes, however, can harm the organs if it goes undetected. This study develops a hybrid deep-learning technique to recognize Type 2 diabetes mellitus. The data is cleaned up at the pre-processing stage using a data transformation technique based on the Yeo-Jhonson transformation. The tanimoto similarity is used in the feature selection process to select the best features from the data. To prepare data for future processing, data augmentation is performed. The Deep Residual Network and the Rider-based Neural Network are recommended and trained separately for the T2DM identification using the Competitive Multi-Verse Rider Optimizer. The outputs generated by the RideNN and DRN classifiers are blended using correlation-based fusion. The suggested CMVRO-based NN-DRN has shown improved performance with the highest accuracy of 91.4%, sensitivity of 94.8%, and specificity of 90.1%.

Identification of potential gene markers in gestational diabetes mellitus

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub‐modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real‐time polymerase chain reaction (qRT‐PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up‐regulated and 684 down‐regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune‐related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down‐regulated HLA‐DQA1, HLA‐DRB1, HLA‐DPA1, and HLA‐DQB1 served as hub genes. In addition, six significant sub‐clusters were extracted and functional analysis suggested that these four genes in sub‐module 1 were also associated with immune and T1DM‐related pathways. Finally, they were also confirmed by qRT‐PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT-benefit/-futile groups. Patients in the predictive ACT-benefit group with 5-fluorouracil (5-Fu)-based ACT had significantly longer relapse-free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT-futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P_interaction  = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.

Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis

BACKGROUND

Gestational diabetes mellitus (GDM) has a high prevalence in the period of pregnancy. However, the lack of gold standards in current screening and diagnostic methods posed the biggest limitation. Regulation of gene expression caused by DNA methylation plays an important role in metabolic diseases. In this study, we aimed to screen GDM diagnostic markers, and establish a diagnostic model for predicting GDM.

METHODS

First, we acquired data of DNA methylation and gene expression in GDM samples (N = 41) and normal samples (N = 41) from the Gene Expression Omnibus (GEO) database. After pre-processing the data, linear models were used to identify differentially expressed genes (DEGs). Then we performed pathway enrichment analysis to extract relationships among genes from pathways, construct pathway networks, and further analyzed the relationship between gene expression and methylation of promoter regions. We screened for genes which are significantly negatively correlated with methylation and established mRNA-mRNA-CpGs network. The network topology was further analyzed to screen hub genes which were recognized as robust GDM biomarkers. Finally, the samples were randomly divided into training set (N = 28) and internal verification set (N = 27), and the support vector machine (SVM) ten-fold cross-validation method was used to establish a diagnostic classifier, which verified on internal and external data sets.

RESULTS

In this study, we identified 465 significant DEGs. Functional enrichment analysis revealed that these genes were associated with Type I diabetes mellitus and immunization. And we constructed an interactional network including 1091 genes by using the regulatory relationships of all 30 enriched pathways. 184 epigenetics regulated genes were screened by analyzing the relationship between gene expression and promoter regions' methylation in the network. Moreover, the accuracy rate in the training data set was increased up to 96.3, and 82.1% in the internal validation set, and 97.3% in external validation data sets after establishing diagnostic classifiers which were performed by analyzing the gene expression profiles of obtained 10 hub genes from this network, combined with SVM.

CONCLUSIONS

This study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.

The impact of omega-3 fatty acid supplementation on glycemic control in patients with gestational diabetes: a systematic review and meta-analysis of randomized controlled studies

Background: Omega-3 fatty acid supplementation shows some treatment efficacy for gestational diabetes. This systematic review and meta-analysis is conducted to investigate the efficacy of omega-3 fatty acid supplementation for glycemic control in patients with gestational diabetes.Methods: The databases including PubMed, Embase, Web of science, EBSCO, and Cochrane Library databases are systematically searched for collecting the randomized controlled trials (RCTs) regarding the efficacy of omega-3 fatty acid versus placebo for gestational diabetes.Results: This meta-analysis has included seven RCTs. Compared with placebo group in patients with gestational diabetes, omega-3 fatty acids supplementation results in remarkably reduced fasting plasma glucose (FPG) (standard mean difference (std. MD) = -0.56; 95% confidence interval (CI) = -0.87 to -0.24; p = .0005), homeostatic model of assessment for insulin resistance (HOMA-IR) (std. MD = -0.52; 95% CI = -0.83 to -0.21; p = .001), but has no statistical impact on macrosomia (risk ratio (RR) = 0.48; 95% CI = 0.22-1.02; p = .06), newborns' hyperbilirubinemia (RR = 0.46; 95% CI = 0.19-1.10; p = .08), nitric oxide (NO) (std. MD = 0.17; 95% CI = -0.64-0.98; p = .68), preterm delivery (RR = 1.75; 95% CI = 0.08-3.80; p=.16) and preeclampsia (RR =0.74; 95% CI = 0.26-2.16; p = .59). However, notably decreased high sensitivity C-reactive protein (hs-CRP) is revealed after omega-3 fatty acids supplementation (std. MD = -1.14; 95% CI = -2.0 to -0.29; p = .009).Conclusions: Omega-3 fatty acids supplementation can provide substantially beneficial effects on glycemic control and inflammatory response for gestational diabetes.