Zheng Bowen, Qiao Jianxiong, Yu Xiaoping, Zhou Hanghang, Wang Anqi, Zhang Xuanfen
Department of Plastic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu, 730030, China.
The Department of Burn, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
BMC Med Genomics. 2025 Jul 1;18(1):108. doi: 10.1186/s12920-025-02174-9.
Keloid disorder (KD) encompasses a spectrum of fibroproliferative dermal conditions, the pathogenesis remains complex and incompletely understood. This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data.
RNA sequencing was performed on skin tissue samples from 13 patients with KD and 14 healthy controls. Using weighted gene co-expression network analysis and differential expression analysis revealed differentially expressed key module genes, and the CytoHubba plugin identified candidate genes. Subsequently analyzed using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) methods to pinpoint feature genes associated with KD. Following this, biomarkers were determined through expression level validation, enrichment analysis, and immune infiltration analysis.
A total of 420 differentially expressed key module genes were identified, and the top 10 genes with DMNC values were selected as candidate genes. Five feature genes were selected through LASSO and SVM-RFE, with NID2, MFAP2, COL8A1, and P4HA3 showing significant expression differences between KD and control samples, along with consistent expression patterns across datasets, identified as potential biomarkers. These four biomarkers were proved to possess high diagnostic potential, and they were found to exhibit significant positive correlations with one another. Functional enrichment analysis indicated that the primary KEGG pathways associated with these biomarkers included "steroid hormone biosynthesis" and "cytokine-cytokine receptor interaction." Moreover, immune infiltration analysis revealed that the four biomarkers were negatively correlated with type 17 T helper cells and positively correlated with 15 immune cell types, including activated B cells and central memory CD4 T cells.
In conclusion, NID2, MFAP2, COL8A1, and P4HA3 were identified as key biomarkers for KD, offering new avenues for more targeted and effective diagnostic and therapeutic strategies for managing this condition.
瘢痕疙瘩疾病（KD）涵盖一系列纤维增生性皮肤病症，其发病机制仍然复杂且尚未完全明确。本研究旨在通过综合生物信息学方法和对RNA测序数据的机器学习分析，识别KD的生物标志物和潜在治疗靶点。
对13例KD患者和14例健康对照的皮肤组织样本进行RNA测序。使用加权基因共表达网络分析和差异表达分析揭示差异表达的关键模块基因，然后通过CytoHubba插件识别候选基因。随后使用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）方法进行分析，以确定与KD相关的特征基因。在此之后，通过表达水平验证、富集分析和免疫浸润分析确定生物标志物。
共鉴定出420个差异表达的关键模块基因，并选择DMNC值最高的前10个基因作为候选基因。通过LASSO和SVM-RFE选择了5个特征基因，其中NID2、MFAP2、COL8A1和P4HA3在KD样本和对照样本之间表现出显著的表达差异，并且在各数据集中具有一致的表达模式，被确定为潜在的生物标志物。这四种生物标志物被证明具有较高的诊断潜力，并且它们之间呈现出显著的正相关。功能富集分析表明，与这些生物标志物相关的主要KEGG通路包括“类固醇激素生物合成”和“细胞因子-细胞因子受体相互作用”。此外，免疫浸润分析显示，这四种生物标志物与17型辅助性T细胞呈负相关，并与15种免疫细胞类型呈正相关，包括活化B细胞和中枢记忆CD4 T细胞。
总之，NID2、MFAP2、COL8A1和P4HA3被确定为KD的关键生物标志物，为针对该病症的更具针对性和有效性的诊断及治疗策略提供了新途径。
