Turky Mennatullah Abdelzaher, Youssef Ibrahim, El Amir Azza
Faculty of Science, Biotechnology Department, Cairo University, 1 Gamaa Street, Oula, Giza, 12613, Egypt.
Faculty of Engineering, Biomedical Engineering Department, Cairo University, Giza, 12613, Egypt.
Hum Genomics. 2025 Mar 19;19(1):29. doi: 10.1186/s40246-025-00733-w.
There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.
This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.
The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.
Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.
精神障碍极为普遍，但患者对精神科药物的反应波动不定。由于食物选择和日常习惯在这种波动中起着根本性作用，将机器学习与网络医学相结合可以为疾病系统以及生活方式对心理健康的调节作用提供有价值的见解。
本研究使用疾病模块识别DREAM挑战的亚军——模块化优化方法，分析了重度抑郁症（MDD）和创伤后应激障碍（PTSD）血液转录组图谱的共表达网络模块。使用随机森林模型检测MDD和PTSD模块的顶级疾病基因。随后，确定了两种主要习惯表型——饮食诱导肥胖和吸烟的调节特征。这些转录/翻译调节因子（TRFs）信号被传导至这两种疾病的疾病基因。构建了一个靶向TRFS以及PTSD或MDD中心节点的药物二分网络。
研究发现了一个MDD中心节点CENPJ，已知其会影响智力。这一观察结果为进一步研究CENPJ作为MDD治疗药物开发新靶点的潜力铺平了道路。此外，大多数预测的PTSD中心节点与多种癌症相关，其中最显著的是SHCBP1。SHCBP1是已知的神经胶质瘤风险因素，这表明持续监测PTSD患者以减轻潜在癌症合并症的重要性。信号网络表明，两种PTSD和三种MDD生物标志物受习惯表型TRFs共同调节。6-异戊烯基柚皮素和阿柏西普被确定为靶向MDD和PTSD中心节点ATP6V0A1和PIGF的潜在候选药物。然而，习惯表型TRFs对ATP6V0A1和PIGF没有调节作用。
将机器学习和网络生物学相结合成功揭示了两种广为传播的疾病——MDD和PTSD的生物标志物。这种方法提供了一种非侵入性诊断途径，并确定了潜在的药物靶点，这些靶点在进一步研究中可能会被重新利用。这些发现有助于我们理解精神障碍、日常习惯和精神科干预之间的复杂相互作用，从而促进更具针对性和个性化的治疗策略。
