卡托普利对膀胱癌 MMP9 和 LCN2 抑制潜力的计算洞察:对治疗应用的影响。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-09-19 eCollection Date: 2024-01-01 DOI:10.1177/11769351241276759
Sanjida Kabir Annana, Jannatul Ferdoush, Farzia Lamia, Ayan Roy, Pallab Kar, Monisha Nandi, Maliha Kabir, Ayan Saha
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引用次数: 0

摘要

目的:卡托普利是治疗新血管性高血压(高血压)、充血性心力衰竭、心肌梗塞后左心室功能障碍和肾病的常用药物。研究发现卡托普利与膀胱癌(BLCA)的相关蛋白有相互作用,这表明卡托普利可能是治疗并发高血压的膀胱癌患者的潜在药物:方法:利用 DrugBank 5.0 确定卡托普利的直接蛋白靶点(DPTs)。STRING 用于分析多蛋白相互作用。TNMPlot 用于比较正常组织、肿瘤组织和转移组织中的基因表达。然后,使用 Autodock 与目标蛋白进行对接。分子动力学模拟用于估算材料中的扩散系数和均方位移:结果:在所有这些蛋白中,MMP9 是 BLCA 中的高表达基因,其表达的增加与患者生存率的降低有关。我们的研究结果表明,卡托普利能有效抑制 BLCA 中 MMP9 的野生型和常见突变型。此外,同样在 BLCA 中过表达的 LCN2 基因与卡托普利相关蛋白相互作用。LCN2 的过表达同样与 BLCA 存活率的降低有关。通过分子对接分析,我们确定了与卡托普利相互作用的 MMP9 活性口袋中的特定氨基酸残基(Tyr179、Pro421、Tyr423 和 Lys603)以及 LCN2 活性口袋中的 Tyr78、Tyr106、Phe145、Lys147 和 Lys156。因此,我们的数据为卡托普利对人类蛋白 MMP9 和 LCN2 的抑制潜力提供了令人信服的证据,这两种蛋白在 BLCA 中都起着至关重要的作用:这些发现为开展后续的体外和体内验证研究提供了广阔的前景,其目的是评估卡托普利治疗 BLCA 患者的适宜性,无论患者的高血压状况如何,这些患者都表现出 MMP9 和 LCN2 表达水平升高。
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Computational Insights into Captopril's Inhibitory Potential Against MMP9 and LCN2 in Bladder Cancer: Implications for Therapeutic Application.

Objectives: Captopril is a commonly used therapeutic agent in the management of renovascular hypertension (high blood pressure), congestive heart failure, left ventricular dysfunction following myocardial infarction, and nephropathy. Captopril has been found to interact with proteins that are significantly associated with bladder cancer (BLCA), suggesting that it could be a potential medication for BLCA patients with concurrent hypertension.

Methods: DrugBank 5.0 was utilized to identify the direct protein targets (DPTs) of captopril. STRING was used to analyze the multiple protein interactions. TNMPlot was used for comparing gene expression in normal, tumor, and metastatic tissue. Then, docking with target proteins was done using Autodock. Molecular dynamics simulations were applied for estimate the diffusion coefficients and mean-square displacements in materials.

Results: Among all these proteins, MMP9 is observed to be an overexpressed gene in BLCA and its increased expression is linked to reduced survival in patients. Our findings indicate that captopril effectively inhibits both the wild type and common mutated forms of MMP9 in BLCA. Furthermore, the LCN2 gene, which is also overexpressed in BLCA, interacts with captopril-associated proteins. The overexpression of LCN2 is similarly associated with reduced survival in BLCA. Through molecular docking analysis, we have identified specific amino acid residues (Tyr179, Pro421, Tyr423, and Lys603) at the active pocket of MMP9, as well as Tyr78, Tyr106, Phe145, Lys147, and Lys156 at the active pocket of LCN2, with which captopril interacts. Thus, our data provide compelling evidence for the inhibitory potential of captopril against human proteins MMP9 and LCN2, both of which play crucial roles in BLCA.

Conclusion: These discoveries present promising prospects for conducting subsequent validation studies both in vitro and in vivo, with the aim of assessing the suitability of captopril for treating BLCA patients, irrespective of their hypertension status, who exhibit elevated levels of MMP9 and LCN2 expression.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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