Identification and verification of ferroptosis-related genes in diabetic foot using bioinformatics analysis.

Ferroptosis is a novel form of cell death that plays a key role in several diseases, including inflammation and tumours; however, the role of ferroptosis-related genes in diabetic foot remains unclear. Herein, diabetic foot-related genes were downloaded from the Gene Expression Omnibus and the ferroptosis database (FerrDb). The least absolute shrinkage and selection operator regression algorithm was used to construct a related risk model, and differentially expressed genes were analysed through immune infiltration. Finally, we identified relevant core genes through a protein-protein interaction network, subsequently verified using immunohistochemistry. Comprehensive analysis showed 198 genes that were differentially expressed during ferroptosis. Based on functional enrichment analysis, these genes were primarily involved in cell response, chemical stimulation, and autophagy. Using the CIBERSORT algorithm, we calculated the immune infiltration of 22 different types of immune cells in diabetic foot and normal tissues. The protein-protein interaction network identified the hub gene TP53, and according to immunohistochemistry, the expression of TP53 was high in diabetic foot tissues but low in normal tissues. Accordingly, we identified the ferroptosis-related gene TP53 in the diabetic foot, which may play a key role in the pathogenesis of diabetic foot and could be used as a potential biomarker.

Bioinformatics analysis and verification of m6A related genes based on the construction of keloid diagnostic model.

Keloids are formed due to abnormal hyperplasia of the skin connective tissue. We explored the relationship between N6-methyladenosine (m6A)-related genes and keloids. The transcriptomic datasets (GSE44270 and GSE185309) of keloid and normal skin tissues samples were obtained from the Gene Expression Omnibus database. We constructed the m6A landscape and verified the corresponding genes using immunohistochemistry. We extracted hub genes for unsupervised clustering analysis using protein-protein interaction (PPI) network; gene ontology enrichment analysis was performed to determine the biological processes or functions affected by the differentially expressed genes (DEGs). We performed immune infiltration analysis to determine the relationship between keloids and the immune microenvironment using single-sample gene set enrichment analysis and CIBERSORT. Differential expression of several m6A genes was observed between the two groups; insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) was significantly upregulated in keloid patients. PPI analysis elucidated six genes with significant differences between the two keloid sample groups. Enrichment analysis revealed that the DEGs were mainly enriched in cell division, proliferation, and metabolism. Moreover, significant differences in immunity-related pathways were observed. Therefore, the results of this study will provide a reference for the elucidation of the pathogenesis and therapeutic targets of keloids.

Identification of Novel Biomarkers With Diagnostic Value and Immune Infiltration in Burn Injury.

Burn injury is an intractable problem in the field of surgery where screening relevant target genes and exploring pathological mechanisms through bioinformatic methods has become a necessity. Herein, we integrated three burn injury mRNA microarray datasets from the Gene Expression Omnibus database to analyze the hub differentially expressed genes (DEGs) between burn injury patient samples and healthy human samples; we conducted multiple functional enrichment analyses and constructed the protein-protein interaction (PPI) network. Finally, we evaluated the immune infiltration in the burn injury microenvironment. A total of 84 intersection DEGs (32 upregulated and 52 downregulated) were screened in burn injury patients via integrated analyses. Upregulated genes were primarily enriched in regulation of T cell activation, regulation of response to DNA damage stimulus, positive regulation of innate immune response, positive regulation of defense response. We also identified 10 hub genes from the PPI network (CCNB2, MYO10, TTK, POLQ, VASP, TIMP1, CDK16, MMP1, ZYX, and PKMYT1). Next, we found that 22 immune cells were substantially changed during the burn injury by CIBERSORT. In addition, we verified that VASP and POLQ are two novel diagnostic markers in burn processes with high diagnostic efficacy via immunohistochemistry. In summary, we identified several key genes involved in burn injury and provided a favorable basis for elucidating the molecular mechanisms of burn injury through comprehensive bioinformatic analysis.