Study on the regulatory effect of Panax notoginseng saponins combined with bone mesenchymal stem cell transplantation on IRAK1/TRAF6-NF-κB pathway in patients with diabetic cutaneous ulcers

Targeting senescent HDF with the USP7 inhibitor P5091 to enhance DFU wound healing through the p53 pathway

OBJECTIVE

The objective of this study was to examine the potential of USP7 as a target for senolytic therapy and to investigate the molecular mechanism by which its inhibitor selectively induced apoptosis in senescent HDF and enhanced DFU wound healing.

METHODS

Clinical samples of DFU were collected to detect the expression of USP7 and aging-related proteins using immunohistochemistry and Western blot. In addition, β-galactosidase staining, qPCR, flow cytometry, ROS and MMP kits, and Western blot were used to analyze the biological functions of P5091 on senescence, cycle, and apoptosis. RNAseq was employed to further analyze the molecular mechanism of P5091. Finally, the DFU rat model was established to evaluate the effect of P5091 on wound healing.

RESULTS

The expression of USP7 and p21 were increased in DFU clinical samples. After treatment with d-glucose (30 mM, 7 days), β-galactosidase staining was deepened, proliferation rate decreased. USP7 inhibitors (P5091) could reduce the release of SASP factors, activate the production of ROS, and reduce MMP. In addition, it induced apoptosis and selectively clears senescent cells through the p53 signaling pathway. Finally, P5091 can improve diabetic wound healing in rats.

CONCLUSION

This study clarified the molecular mechanism of USP7 inhibitor (P5091) selectively inducing apoptosis of high glucose senescent HDF cells. This provides a new senolytics target and experimental basis for promoting DFU wound healing.

Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning

Background

A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.

Methods

This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.

Results

This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.

Conclusion

This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.