Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effect for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and Gene Expression Omnibus expression profiles, respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data, respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by graph neural networks. An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and DE genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR.

中文翻译：

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通过图神经网络和转录组分析校正的相互作用组网络对淀粉样转甲状腺素蛋白淀粉样变性进行药物重新定位。

淀粉样转甲状腺素蛋白 (ATTR) 淀粉样变性是由转甲状腺素蛋白错误折叠成神经和心脏中的淀粉样蛋白沉积物引起的，是一种进行性罕见疾病。未知的发病机制和缺乏治疗使得5年生存预后极差。目前可用的ATTR药物只能缓解症状并减缓病情进展，但尚无药物显示出对该病的治愈效果。不断增长的药理学数据以及大规模基因组和转录组数据为通过计算药物重新定位寻找潜在的新 ATTR 药物带来了新的机会。我们分别从五个公共数据库和Gene Expression Omnibus表达谱中收集了疾病发病机制中与ATTR相关的基因和差异表达（DE）基因，然后通过对ATTR相关基因进行校正的蛋白质-蛋白质网络分析来筛选候选药物作为药物靶点，然后根据化合物扰动的基因表达数据筛选候选药物。我们分别从5个公共数据库和转录组数据中收集了139个和56个ATTR相关基因，并进行了功能富集分析。我们根据校正的相互作用组网络中与 ATTR 相关基因的接近程度筛选出 355 种候选药物，并通过图神经网络进行细化。进一步应用反向基因集富集分析来估计扰动对 ATTR 相关基因和 DE 基因的影响。讨论了可能性较高的候选药物。在具有转录组数据的相互作用组网络上使用系统计算过程进行药物重新定位，以筛选出几种潜在的 ATTR 新候选药物。