Syed Asif Hassan, Abujabal Hamza Ali S, Ahmad Shakeel, Malebary Sharaf J, Alromema Nashwan
Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 22254, Saudi Arabia.
Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel). 2024 Jun 4;14(11):1182. doi: 10.3390/diagnostics14111182.
This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (, , , , , and ) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. and were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.
本研究利用高通量技术和机器学习（ML），在炎症性肠病（IBD）中识别出了基因生物标志物和分子特征。通过使用GSE75214微阵列数据集比较172例IBD患者和22例健康个体结肠标本中的基因表达水平，我们能够识别出IBD患者中显著上调或下调的基因。我们的ML技术和特征选择方法揭示了六种具有强大IBD诊断潜力的差异表达基因（DEG）生物标志物（、、、、和）。随机森林（RF）模型表现卓越，准确率、F1分数和AUC值均超过0.98。我们的研究结果在独立数据集（GSE36807和GSE10616）中得到了严格验证，进一步增强了其可信度，并显示出良好的性能指标（准确率：0.841，F1分数：0.734，AUC：0.887）。我们的功能注释和通路富集分析为与这些失调基因相关的关键通路提供了见解。和被鉴定为新的IBD生物标志物，加深了我们对该疾病的理解。在独立队列中的验证提高了这些发现的可靠性，并强调了它们在IBD早期检测和个性化治疗中的潜力。有必要进一步探索这些基因，以全面了解它们在IBD发病机制中的作用，并开发改进的诊断工具和治疗方法。本研究为IBD研究做出了重大贡献，提供了宝贵的见解，可能极大地改善患者护理。
