Integrated analysis of potential biomarkers associated with diabetic periodontitis development based on bioinformatics: An observational study

Identification and validation of core genes associated with polycystic ovary syndrome and metabolic syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting women of reproductive age, affecting reproductive health, and increasing the incidence of diabetes mellitus and hypertension. Metabolic syndrome (MetS) is the most common metabolic disorder. Although clinical studies have shown a close association between PCOS and MetS, the molecular mechanisms are unknown. In this study, datasets of PCOS and MetS were obtained from the Gene Expression Omnibus database; differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed; and gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses also performed of differentially expressed genes (DEGs). The PCOS- and MetS-coexpressed DEGs were subsequently intersected with the coexpressed genes in the WGCNA module to obtain the core genes. By constructing receiver operating characteristic curves, we verified the predictive effects of the core genes. We also validated the expression of the core genes in the datasets. Finally, we verified the expression of the core genes by quantitative polymerase chain reaction in human follicular fluid granulosa cells. In addition, we used Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts to analyze the immune infiltration of immune cells in PCOS and MetS. Finally, we obtained 52 coexpressed DEGs of PCOS and MetS and 3 coexpressed genes in the WGCNA module. By taking the intersection of coexpressed DEGs and coexpressed genes of the WGCNA module, we get ELOVL fatty acid elongase 7 (ELOVL7) as the core gene. Receiver operating characteristic curve analysis showed that ELOVL7 is a reliable biological marker for PCOS and MetS. The expression level of ELOVL7 in human follicular fluid granulosa cells from PCOS patients was significantly higher than that of controls, as verified by quantitative polymerase chain reaction. This study provides the first evidence of the role of ELOVL7 in developing PCOS and MetS. This gene may serve as a potential diagnostic marker and therapeutic target for both conditions.

Development of an immune-related diagnostic predictive model for oral lichen planus

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.