Identification and validation of biomarkers related to mitotic catastrophe in keloid

Background : Keloid, a benign condition affecting the skin, is marked by an abnormal accumulation of collagen and an overgrowth of fibroblasts extending beyond the limits of the original wound. This condition manifests as prominent, thickened scars that frequently induce discomfort and pain, and may reoccur following surgical procedures. Studies have indicated that mitotic catastrophe (MC) could play a role in the development of keloid. This research sought to investigate the significance of MC-related genes (MCRGs) in the development of keloid. Methods : This research aimed to identify the genes that exhibit differential expression between keloid and control samples, utilizing a differential expression analysis approach. The meeting point of differentially expressed genes (DEGs) and MCRGs resulted in the identification of candidate genes. Subsequently, additional biomarkers were pinpointed through the utilization of protein-protein interaction (PPI) network analysis, a machine learning approach, and expression level confirmation. Crucially, the nomogram, gene set enrichment analysis (GSEA) of biomarkers, and immune infiltration analysis were utilized to investigate the underlying mechanism of biomarkers in keloid. Furthermore, the molecular regulatory network, drug prediction, and molecular docking techniques were employed to identify the drugs and regulatory factors. Ultimately, the expression of biomarkers was corroborated in clinical specimens through reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results : Ultimately, PPP1CA and NUDC were determined to be biomarkers, and the nomogram demonstrated effective predictive power for keloid. Furthermore, the expression of PPP1CA was enhanced during neutrophil degranulation, whereas NUDC exhibited increased activity in glucose metabolism. The analysis of immune infiltration revealed that the cells exhibiting disparities between the two groups and displaying the highest association with the biomarkers were predominantly T-cell-oriented. The microRNAs associated with PPP1CA comprised hsa-miR-31315b, whereas those connected to NUDC comprised hsa-miR-4743-3p and hsa-miR-8089.Furthermore, six transcription factors (TFs), including SP1 and ELK1, were identified to regulate two biomarkers. Significantly, the substance with superior compatibility with PPP1CA was cantharidin, while the one with NUDC was latamoxef. Ultimately, the findings from RT-qPCR aligned with the bioinformatics analysis. Conclusions : This research discovered two biomarkers (PPP1CA and NUDC) that could potentially be utilized as therapeutic targets for the treatment of keloid.