The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

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基于单细胞转录特征的结直肠癌药物再利用和体外评估

对智能和有效的疾病治疗的需求以及药物设计成本的增加使得药物重新利用成为生物医学的有效策略之一。药物再利用有多种计算方法，其中之一是使用转录特征，特别是单细胞 RNA 测序 (scRNA-seq) 数据，它向我们展示了细胞内部清晰、全面的视图，以比较疾病状态和健康。在这项研究中，我们使用了来自 29 名结直肠癌患者的 91,103 个 scRNA-seq 样本（GSE144735 和 GSE132465）。首先，使用 ASAP 网站进行差异基因表达 (DGE) 分析。然后，我们通过 iLINCS 网站找到了一份可以将基因特征模式从癌症逆转为正常的药物列表。此外，通过搜索各种数据库和文章，我们发现了 12 种获得 FDA 批准的药物，到目前为止，还没有人报道它们作为治疗任何癌症的药物。然后，为了评估这些药物的细胞毒性和性能，对两种结直肠癌细胞系（HT29 和 HCT116）进行了 MTT 测定和实时 PCR。根据我们的方法，建议使用 12 种药物用于治疗结直肠癌。选择四种药物进行生物学评价。这些药物的细胞毒性分析结果如下：tezacaftor（HCT-116的IC10=19μM，HT-29的IC10=2μM），芬替康唑（HCT-116的IC10=17μM，HT-29的IC10=7μM）。 HT-29）、bempedoic 酸（HCT-116 的 IC10 = 78 µM，HT-29 的 IC10 = 65 µM）和泛昔洛韦（HCT-116 的 IC10 = 422 µM，HT-29 的 IC10 = 959 µM）。成本、时间和有效性是寻找治疗疾病的新药的主要挑战。基于转录特征的药物再利用方法等计算方法为解决这些挑战开辟了新的视野。在这项研究中，tezacaftor、芬替康唑和 bempedoic 酸可以作为治疗结直肠癌的有前景的候选药物。这些药物在计算机和体外进行了评估，但有必要在体内对其进行评估。