Sarker Sakib, Mahmud S M Hasan, Hosen Md Faruk, Michael Goh Kah Ong, Shoombuatong Watshara
Department of Computer Science and Engineering, Uttara University, Turag, Uttara, Dhaka 1230, Bangladesh.
Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh.
Brief Bioinform. 2025 Aug 31;26(5). doi: 10.1093/bib/bbaf473.
Preeclampsia is a complex pregnancy disorder that poses significant health risks to both mother and fetus. Despite its clinical importance, the underlying molecular mechanisms remain poorly understood. In this study, we developed an integrative deep learning and bioinformatics approach to identify potential biomarkers for preeclampsia. Three microarray datasets related to preeclampsia were initially analyzed to select a preliminary gene subset based on $P$-values. Feature selection was then performed in two consecutive rounds: first, the Fisher score method was applied to extract significant genes, followed by the minimum Redundancy Maximum Relevance method to refine the subset further. These selected gene subsets were trained using our proposed Attention-based Convolutional Neural Network (AttCNN), which achieved the highest classification accuracy compared with other models. From the experiments, a set of 58 common genes was identified between differentially expressed genes and the final optimized subset. Here, Gene Ontology and KEGG pathway enrichment analyses highlighted key biological processes and pathways associated with preeclampsia. Subsequently, a protein-protein interaction network was constructed, identifying 10 hub genes: TSC22D1, IRF3, MME, SRSF10, SOD1, HK2, ERO1L, SH3BP5, UBC, and ZFAND5. Further analysis of gene regulatory networks, including transcription factor-gene, gene-microRNA, and drug-gene interactions, revealed that seven hub genes (HK2, SRSF10, SOD1, ERO1L, IRF3, MME, and SH3BP5) were strongly associated with preeclampsia. Molecular docking analysis showed that HK2, SH3BP5, and SOD1 exhibited significant binding affinities with two preeclampsia drugs. These findings suggest that the identified hub genes hold promise as biomarkers for early prognosis, diagnosis, and potential therapeutic targets for preeclampsia.
子痫前期是一种复杂的妊娠疾病，对母亲和胎儿都构成重大健康风险。尽管其具有临床重要性，但其潜在的分子机制仍知之甚少。在本研究中，我们开发了一种综合深度学习和生物信息学方法来识别子痫前期的潜在生物标志物。最初分析了三个与子痫前期相关的微阵列数据集，以基于P值选择一个初步基因子集。然后连续两轮进行特征选择：首先，应用Fisher评分法提取显著基因，接着使用最小冗余最大相关性方法进一步优化该子集。使用我们提出的基于注意力的卷积神经网络（AttCNN）对这些选定的基因子集进行训练，与其他模型相比，该网络实现了最高的分类准确率。通过实验，在差异表达基因和最终优化子集中鉴定出一组58个常见基因。在此，基因本体论和KEGG通路富集分析突出了与子痫前期相关的关键生物学过程和通路。随后，构建了一个蛋白质 - 蛋白质相互作用网络，确定了10个枢纽基因：TSC22D1、IRF3、MME、SRSF10、SOD1、HK2、ERO1L、SH3BP5、UBC和ZFAND5。对基因调控网络的进一步分析，包括转录因子 - 基因、基因 - 微小RNA和药物 - 基因相互作用，表明七个枢纽基因（HK2、SRSF10、SOD1、ERO1L、IRF3、MME和SH3BP5）与子痫前期密切相关。分子对接分析表明，HK2、SH3BP5和SOD1与两种子痫前期药物表现出显著的结合亲和力。这些发现表明，所鉴定的枢纽基因有望作为子痫前期早期预后、诊断的生物标志物以及潜在的治疗靶点。
