Pediatric Oncology of the First Hospital of Jilin University, Changchun, 130021, China.
Mircrosurgery Department of PLA General Hospital, Beijing, 100853, China.
Mol Med. 2024 Nov 14;30(1):215. doi: 10.1186/s10020-024-00955-z.
To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).
Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level.
Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages.
The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.
利用机器学习识别糖尿病足溃疡（DFU）的治疗反应基因。
收集 DFU 患者的转录组数据并进行综合分析。首先，进行差异表达分析，以识别 DFU 患者与健康对照之间表达水平有显著变化的基因。接下来，进行富集分析，以揭示与这些差异表达基因相关的生物学途径和过程。然后，将机器学习算法（包括特征选择和分类技术）应用于数据，以确定在 DFU 发病机制中起关键作用的关键基因。使用独立的转录组数据集验证我们研究中确定的关键基因。进一步对单细胞数据集进行分析，以研究关键基因在单细胞水平上的变化。
通过这种综合方法，确定了 SCUBE1 和 RNF103-CHMP3 作为与 DFU 显著相关的关键基因。发现 SCUBE1 参与免疫调节，在机体对炎症和感染的反应中发挥作用，这在 DFU 中很常见。RNF103-CHMP3 与细胞外相互作用有关，表明其参与细胞通讯和组织修复机制，这对伤口愈合至关重要。我们的分析结果在独立的转录组数据集中得到了验证。此外，在单细胞转录组数据中检查了 SCUBE1 和 RNF103-CHMP3 的表达，结果表明，这些基因在治愈的 DFU 患者组中显著下调，尤其是在 NK 细胞和巨噬细胞中。
将 SCUBE1 和 RNF103-CHMP3 鉴定为 DFU 的潜在生物标志物，标志着我们在理解该疾病的分子基础方面迈出了重要一步。这些基因为诊断和治疗提供了新的方向，有可能开发出针对这些基因的靶向治疗方法，从而改善患者的治疗效果。本研究强调了将计算方法与生物数据相结合，以揭示复杂疾病（如 DFU）的新见解的价值。未来的研究应集中在更大的队列中验证这些发现，并探索在临床环境中靶向 SCUBE1 和 RNF103-CHMP3 的治疗潜力。
