Diagnostic biomarker for type 2 diabetic peripheral neuropathy via comprehensive bioinformatics analysis.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of Type 2 diabetes mellitus (T2DM), which frequently results in disabling neuropathic pain and lower-limb amputation. The identification of noninvasive biomarkers for DPN may help early detection and individualized treatment of DPN. METHODS: In this study, we identified differentially expressed genes (DEGs) between DPN and the control based on blood-source (GSE95849) and tissue-source gene expression profiles (GSE143979) from the Gene Expression Omnibus (GEO) database using limma, edgeR, and DESeq2 approaches. KEGGG and GO functional enrichments were performed. Hub genes and their correlation with infiltrating immune cells were analyzed. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to quantify hub gene expression. RESULTS: In total, 144 DEGs between DPN and the control were identified. Functional enrichment revealed that the DEGs were mainly enriched in immune-related pathways like the Fc epsilon receptor Ig signaling pathway. By protein-protein interaction (PPI) network analysis, FCER1G, SYK, ITGA4, F13A1, MS4A2, and PTK2B were screened as hub genes with higher expression in DPN patients, among which half were immune genes (FCER1G, PTK2B, and SYK). RT-qPCR demonstrated that mRNA expression of FCER1G, PTK2B, and SYK was significantly increased in patients with DPN compared with both diabetic nonperipheral neuropathy (DNN) and normal subjects. The area under the receiver operating characteristic (ROC) curve of FCER1G, PTK2B, and SYK was 0.84, 0.81, and 0.73, respectively, suggesting their great advantages as diagnostic biomarkers to predict the progression of neuropathy in T2DM. Further analysis indicated that the expression of FCER1G, PTK2B, and SYK was negatively correlated with the cell proportion of significantly altered resting natural killer cells, T follicular helper cells, and activated mast cells, but positively correlated with monocytes. CONCLUSIONS: Our findings demonstrated FCER1G, PTK2B, and SYK are potential diagnostic biomarkers and therapeutic targets for DPN, which provides new insight into DPN pathogenesis and therapies.

Impact of Visceral Obesity on Structural and Functional Alterations of Gut Microbiota in Polycystic Ovary Syndrome (PCOS): A Pilot Study Using Metagenomic Analysis.

Objective: We aimed to identify structural and functional alterations of gut microbiota associated with visceral obesity in adult women with polycystic ovary syndrome (PCOS). Methods: Twenty-seven adults with PCOS underwent stool and fasting blood collection, oral glucose tolerance testing, and visceral fat area (VFA) measurement via dual-bioimpedance technique. Metagenomic analysis was used to analyze gut microbiota. Results: PCOS patients were divided into three groups: visceral obesity group (PCOS-VO, n=9, age 28.33±5.68 years, BMI 37.06±4.27 kg/m2, VFA 128.67±22.45 cm2), non-visceral obesity group (PCOS-NVO, n=10, age 25.40±4.53, BMI 30.74±3.95, VFA 52.00±24.04), normal BMI group (PCOS-NB, n=8, age 27.88±2.53, BMI 21.56±2.20, VFA 27.00±21.18), with no statistical difference in age (P>0.05) and significantly statistical differences in BMI and VFA (P<0.05). The groups showed a significant difference in microbial ß-diversity between PCOS-VO and PCOS-NVO (P=0.002) and no difference between PCOS-NVO and PCOS-NB (P=0.177). Bacteroidetes was the phylum with the highest relative abundance among all patients, followed by Firmicutes. Those with visceral obesity had a higher abundance of Prevotella, Megamonas, and Dialister genera, positively correlated with metabolic markers (r>0.4, P<0.05), and lower abundance of Phascolarctobacterium and Neisseria genera, negatively correlated with metabolic markers (r<-0.4, P<0.05). Functional annotation analysis showed significant differences in relative abundance of ribosome pathway, fatty acid biosynthesis pathway, and sphingolipid signaling pathway between groups, affecting lipid homeostasis and visceral fat accumulation. Conclusion: Alteration in ß-diversity of gut microbiota exists in PCOS with visceral obesity versus those without visceral obesity and relates to functional differences in ribosomes, fatty acid biosynthesis, and sphingolipid signaling pathways.