Biomarker screening in preeclampsia: an RNA-sequencing approach based on data from multiple studies

Identification and validation of autophagy-related genes in sepsis based on bioinformatics studies

We identified 14 key genes associated with mitochondrial autophagy in sepsis through differential analysis of the dataset and then analysed the identified genes for functional enrichment. The analysis of key genes and deeper analysis of key genes by molecular typing, Weighted Gene Correlation Network Analysis (WGCNA) and ceRNA were also carried out. We have also validated these key genes with clinical data. Finally, sepsis diagnostic models are constructed by combining key genes with machine learning methods. In addition, we discuss the importance of the immune system in sepsis and its relationship with signature genes, which opens up new directions for studying the role of the immune system in sepsis. Overall, our study adds new ideas to the diagnosis and treatment of sepsis.

Exploring gene expression signatures in preeclampsia and identifying hub genes through bioinformatic analysis

INTRODUCTION

Preeclampsia (PE) is a multisystem disease that affects women during the pregnancy. Its pathogenicity remains unclear, and no definitive screening test can predict its occurrence so far. The aim of this study is to identify the critical genes that are involved in the pathogenicity of PE by applying integrated bioinformatic methods and to investigate the genes' diagnostic capability.

METHODS

Datasets that investigated PE have been downloaded from Gene Expression Omnibus (GEO) datasets. Differential gene expression, weighted gene co-expression analysis (WGCNA), protein-protein interaction (PPI) network construction, and finally, the calculation of area under the curve and Receiver operating characteristic curve (ROC) analysis were done for the potential hub genes. The results generated from the GSE186257 dataset (discovery cohort) were validated in the GSE75010 dataset (validation cohort). Following validation of the hub-genes, a multilayer regulatory network was constructed to include the up-stream regulatory elements (transcription factors and miRNAs) of the validated hub-genes.

RESULTS

WGCNA revealed six modules that were significantly correlated with PE. A total of 231 differentially expressed genes (DEGs) were identified. DEGs were intersected with the WGCNA modules' genes, totalling 55 genes. These shared genes were used to construct the PPI network; subsequently, four genes, namely FLT1, HTRA4, LEP and PAPPA2, were identified as hub-genes for PE in the discovery cohort. The expressional of these four hub genes were validated in the validation cohort and found to be highly expressed. ROC analysis in both datasets revealed that all these genes had a significant PE diagnostic ability. The regulatory network showed that FLT1 gene is the most connected and regulated gene among the validated hub-genes.

DISCUSSION

This integrated analysis revealed that FLT1, LEP, HTRA4 and PAPPA2 may be strongly involved in the pathogenicity of PE and act as promising biomarkers and potential therapeutic targets for PE.

New genetic biomarkers from transcriptome RNA-sequencing for Mycobacterium tuberculosis complex and Mycobacterium avium complex infections by bioinformatics analysis

The study aims to accurately identify differentially expressed genes (DEGs) and biological pathways in mycobacterial infections through bioinformatics for deeper disease understanding. Differentially expressed genes (DEGs) was explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Unique DEGs were submitted on least absolute shrinkage and selection operator (LASSO) regression analysis. 1,057 DEGs from two GSE datasets were identified, which were closely connected with NTM/ latent TB infection (LTBI)/active TB disease (ATB). It was demonstrated that these DEGs are mainly associated with detoxification processes, and virus and bacterial infections. Moreover, the METTL7B gene was the most informative marker for distinguishing LTBI and ATB with an area under the curve (AUC) of 0.983 (95%CI: 0.964 to 1). The significantly upregulated HBA1/2 genes were the most informative marker for distinguishing between individuals of IGRA-HC/NTM and LTBI (P < 0.001). Moreover, the upregulated HBD gene was also differ between IGRA-HC/NTM and ATB (P < 0.001). We have identified gene signatures associated with Mycobacterium infection in whole blood, which could be significant for understanding the molecular mechanisms and diagnosis of NTM, LTBI, or ATB.

Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.

Minerals and the Menstrual Cycle: Impacts on Ovulation and Endometrial Health

The role of minerals in female fertility, particularly in relation to the menstrual cycle, presents a complex area of study that underscores the interplay between nutrition and reproductive health. This narrative review aims to elucidate the impacts of minerals on key aspects of the reproductive system: hormonal regulation, ovarian function and ovulation, endometrial health, and oxidative stress. Despite the attention given to specific micronutrients in relation to reproductive disorders, there is a noticeable absence of a comprehensive review focusing on the impact of minerals throughout the menstrual cycle on female fertility. This narrative review aims to address this gap by examining the influence of minerals on reproductive health. Each mineral's contribution is explored in detail to provide a clearer picture of its importance in supporting female fertility. This comprehensive analysis not only enhances our knowledge of reproductive health but also offers clinicians valuable insights into potential therapeutic strategies and the recommended intake of minerals to promote female reproductive well-being, considering the menstrual cycle. This review stands as the first to offer such a detailed examination of minerals in the context of the menstrual cycle, aiming to elevate the understanding of their critical role in female fertility and reproductive health.

Biomarker screening in fetal growth restriction based on multiple RNA-seq studies

Objective

Fetal growth restriction (FGR) is a severe pathological complication associated with compromised fetal development. The early diagnosis and prediction for FGR are still unclear. Sequencing technologies present a huge opportunity to identify novel biomarkers. However, limitation of individual studies (e.g., long lists of dysregulated genes, small sample size and conflicting results) hinders the selection of the best-matched ones.

Study design

A multi-step bioinformatics analysis was performed. We separately reanalyzed data from four public RNA-seq studies, followed by a combined analysis of individual results. The differentially expressed genes (DEGs) were identified based on DESeq2. Then, function enrichment analyses and protein-protein interaction network (PPI) were conducted to screen for hub genes. The results were further verified by using external microarray data.

Results

A total of 65 dysregulated genes (50 down and 15 upregulated) were identified in FGR compared to controls. Function enrichment and PPI analysis revealed ten hub genes closely related to FGR. Validation analysis found four downregulated candidate biomarkers (CEACAM6, SCUBE2, DEFA4, and MPO) for FGR.

Conclusions

The use of omics tools to explore mechanism of pregnancies disorders contributes to improvements in obstetric clinical practice.