The aim of the present study was to investigate the effect of epidermal growth factor (EGF) and acidic fibroblast growth factor (aFGF) on the healing of diabetic foot wounds. A total of 199 patients with diabetic foot ulcers were recruited and randomly divided into four groups: A recombinant human EGF group (n=50), an aFGF group (n=50), a combined EGF and aFGF group (n=50) and a normal saline control group (n=49). Patients in all groups received a daily dressing change and growth factor reagents were applied topically when dressing. To observe the time required for each stage of wound healing, the epidermal healing rate and granulation tissue growth were recorded. Following 3‑4 days of treatment, the wound healing stage was similar in all groups. Later stages (following 4 days) of wound healing were achieved significantly faster in the combined group compared with the control group (P<0.05). The rate of wound healing in the EGF group was similar to that observed in the combination group. No significant difference was observed between the EGF and aFGF groups during the initial period of wound healing. However, in the later stage (following 4 days), the combined use of recombinant human EGF and aFGF had a marked positive effect on wound healing when compared with the control group. Growth factors have extensive biological activities with functions including promoting cell proliferation as well as rehabilitating and regenerating tissues, which serve important roles in wound healing.

Background: The prevalence of diabetic foot ulcers (DFUs) has caused serious harm to human health. To date, a highly effective treatment is lacking. Long noncoding RNA X-inactive specific transcript (lncRNA XIST) has been the subject of mounting research studies, all of which have found that it serves as a protective factor against certain diseases; however, its function in DFUs is not entirely understood. This study was performed to determine the importance of the lncRNA XIST in the pathogenesis and biological function of DFUs. Methods: Diabetic ulcer skin from rats was analysed using haematoxylin-eosin (HE), Masson’s trichrome, and immunohistochemistry (IHC) staining. The differences in the expression of genes and proteins were examined with real-time quantitative polymerase chain reaction (RT–qPCR) and Western blotting. Next, the interaction was verified with a dual luciferase gene reporter assay. In addition, CCK-8, Transwell, and wound healing assays were used to assess the proliferation and migration of HaCaT cells. Results: The lncRNA XIST and epidermal growth factor receptor (EGFR) were downregulated, while microRNA-126-3p (miR-126-3p) was increased in diabetic ulcer rat skin tissues and high glucose-induced HaCaT cells. In addition, we found that the lncRNA XIST binds to miR-126-3p and that EGFR is directly targeted by miR‑126‑3p. Silencing XIST contributed to upregulated miR-126-3p expression, thus lowering EGFR levels and inhibiting the proliferative and migratory abilities of high glucose-treated HaCaT cells; however, the miR-126-3p inhibitor and overexpression of EGFR reversed this effect. Conclusion: Decreased lncRNA XIST expression inhibits the proliferative and migratory abilities of high glucose-induced HaCaT cells by modulating the miR-126-3p/EGFR axis, causing delayed wound healing.

Introduction: Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient’s prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.Methods: We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.Results: In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.Conclusion: This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.