Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification

Diabetes and Renal Complications: An Overview on Pathophysiology, Biomarkers and Therapeutic Interventions

Diabetic kidney disease (DKD) is a major microvascular complication of both type 1 and type 2 diabetes. DKD is characterised by injury to both glomerular and tubular compartments, leading to kidney dysfunction over time. It is one of the most common causes of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Persistent high blood glucose levels can damage the small blood vessels in the kidneys, impairing their ability to filter waste and fluids from the blood effectively. Other factors like high blood pressure (hypertension), genetics, and lifestyle habits can also contribute to the development and progression of DKD. The key features of renal complications of diabetes include morphological and functional alterations to renal glomeruli and tubules leading to mesangial expansion, glomerulosclerosis, homogenous thickening of the glomerular basement membrane (GBM), albuminuria, tubulointerstitial fibrosis and progressive decline in renal function. In advanced stages, DKD may require treatments such as dialysis or kidney transplant to sustain life. Therefore, early detection and proactive management of diabetes and its complications are crucial in preventing DKD and preserving kidney function.

Hypoglycemic Activity of Rice Resistant-Starch Metabolites: A Mechanistic Network Pharmacology and In Vitro Approach

Rice (Oryza sativa L.) is one of the primary sources of energy and nutrients needed by the body, and rice resistant starch (RRS) has been found to have hypoglycemic effects. However, its biological activity and specific mechanisms still need to be further elucidated. In the present study, 52 RRS differential metabolites were obtained from mouse liver, rat serum, canine feces, and human urine, and 246 potential targets were identified through a literature review and database analysis. A total of 151 common targets were identified by intersecting them with the targets of type 2 diabetes mellitus (T2DM). After network pharmacology analysis, 11 core metabolites were identified, including linolenic acid, chenodeoxycholic acid, ursodeoxycholic acid, deoxycholic acid, lithocholic acid, lithocholylglycine, glycoursodeoxycholic acid, phenylalanine, norepinephrine, cholic acid, and L-glutamic acid, and 16 core targets were identified, including MAPK3, MAPK1, EGFR, ESR1, PRKCA, FYN, LCK, DLG4, ITGB1, IL6, PTPN11, RARA, NR3C1, PTPN6, PPARA, and ITGAV. The core pathways included the neuroactive ligand-receptor interaction, cancer, and arachidonic acid metabolism pathways. The molecular docking results showed that bile acids such as glycoursodeoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid, deoxycholic acid, and cholic acid exhibited strong docking effects with EGFR, ITGAV, ITGB1, MAPK3, NR3C1, α-glucosidase, and α-amylase. In vitro hypoglycemic experiments further suggested that bile acids showed significant inhibitory effects on α-glucosidase and α-amylase, with CDCA and UDCA having the most prominent inhibitory effect. In summary, this study reveals a possible hypoglycemic pathway of RRS metabolites and provides new research perspectives to further explore the therapeutic mechanism of bile acids in T2DM.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common malignant tumors worldwide. Nevertheless, GC still lacks effective diagnosed and monitoring method and treating targets. This study used multi omics data to explore novel biomarkers and immune therapy targets around sphingolipids metabolism genes (SMGs).

METHOD

LASSO regression analysis was performed to filter prognostic and differently expression SMGs among TCGA and GTEx data. Risk score model and Kaplan-Meier were built to validate the prognostic SMG signature and prognostic nomogram was further constructed. The biological functions of SMG signature were annotated via multi omics. The heterogeneity landscape of immune microenvironment in GC was explored. qRT-PCR was performed to validate the expression level of SMG signature. Competing endogenous RNA regulatory network was established to explore the molecular regulatory mechanisms.

RESULT

3-SMGs prognostic signature (GLA, LAMC1, TRAF2) and related nomogram were constructed combing several clinical characterizes. The expression difference and diagnostic value were validated by PCR data. Multi omics data reveals 3-SMG signature affects cell cycle and death via several signaling pathways to regulate GC progression. Overexpression of 3-SMG signature influenced various immune cell infiltration in GC microenvironment. RBP-SMGs-miRNA-mRNAs/lncRNAs regulatory network was built to annotate regulatory system.

CONCLUSION

Upregulated 3-SMGs signature are excellent predictive diagnosed and prognostic biomarkers, providing a new perspective for future GC immunotherapy.