Gene-drug interaction of the key regulators.

Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes, which is mainly because of delayed disease detection, resistance to chemotherapy, and lack of specific targeted therapies. The disease’s development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan’s algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif defined by G (3,3). Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

胰腺导管腺癌（Pancreatic Ductal Adenocarcinoma, PDAC）是一种预后极差的致死性疾病，其主要成因包括疾病发现延迟、化疗耐药以及缺乏特异性靶向治疗手段。该疾病的发生发展涉及免疫、遗传与环境因素间的复杂交互作用，但其分子机制至今仍未明晰。解析调控PDAC网络的遗传特征，是阐明其病因学的核心挑战之一。为解决这一难题，本研究分析了PDAC的基因表达谱，并与健康对照样本进行比对，鉴定出差异表达基因（differentially expressed genes, DEGs）。以此类差异表达基因为基础，我们构建了PDAC的蛋白质相互作用网络，并计算了该网络的拓扑属性。研究发现，PDAC网络可自组织形成具有弱层级结构的无标度分形状态。采用社区检测的纽曼-吉尔万算法（领先特征向量法，LEV），共识别出4个社区，其中至少存在一个由G(3,3)定义的网络基序。分析结果显示，33个关键调控因子主要富集于神经活性配体-受体相互作用、细胞黏附分子、白细胞跨内皮迁移通路，细胞增殖正调控、蛋白激酶B信号通路正调控等生物学功能，以及G蛋白β亚基结合、受体结合等分子功能类别中。我们还获取了这些关键调控因子对应的转录因子与微小RNA（miRNA）。鉴于PDAC关键调控因子具备治疗潜力与生物标志物价值，本研究同时鉴定出针对特定基因的获批药物。不过，仍需针对此类关键调控因子开展实验验证，以明确其在PDAC背景下的疗效。