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人类肿瘤中CDC7的泛癌分析:通过机器学习和计算方法获得的综合多组学见解及新型海洋来源抑制剂的发现

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.

作者信息

Saif Ahmed, Islam Md Tarikul, Raihan Md Obayed, Yousefi Niloofar, Rahman Md Ajijur, Faridi Hafeez, Hasan Al Riyad, Hossain Mirza Mahfuj, Saleem Rasha Mohammed, Albadrani Ghadeer M, Al-Ghadi Muath Q, Ahasan Setu Md Ali, Kamel Mohamed, Abdel-Daim Mohamed M, Aktaruzzaman Md

机构信息

Department of Pharmacy, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh; Laboratory of Advanced Computational Biology, Biological Research on the Brain (BRB), Jashore, 7408, Bangladesh.

Department of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh; Laboratory of Advanced Computational Biology, Biological Research on the Brain (BRB), Jashore, 7408, Bangladesh.

出版信息

Comput Biol Med. 2025 May;190:110044. doi: 10.1016/j.compbiomed.2025.110044. Epub 2025 Mar 22.

Abstract

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein.

摘要

癌症仍然是一项重大的全球健康挑战,细胞分裂周期7(CDC7)蛋白因其在肿瘤增殖、存活和耐药性方面的关键作用而成为一个潜在的治疗靶点。然而,目前缺乏对多种癌症中CDC7的全面分析,现有的治疗方案在临床上取得的成功有限。本研究的目的是将对CDC7的全面泛癌分析与新型海洋来源抑制剂的鉴定相结合,弥合对CDC7作为不同癌症类型的预后生物标志物和治疗靶点的认识差距。在本研究中,我们使用公开可用的数据集对33种肿瘤类型进行了CDC7的泛癌分析,以评估其表达、基因改变、免疫相互作用、存活率和预后意义。此外,利用化学信息学和机器学习对一个包含31492个分子的海洋来源化合物库进行筛选,以鉴定潜在的CDC7抑制剂。对排名靠前的候选物进行了严格评估,包括分子对接、药代动力学、毒性、密度泛函理论(DFT)计算和分子动力学(MD)模拟。研究结果表明,CDC7在几种癌症中过表达,与较差的生存结果和不良预后相关。富集分析将CDC7与关键的DNA复制途径联系起来,而其在调节肿瘤免疫相互作用中的作用突出了其作为免疫治疗靶点的潜力。在所有测试的化合物中,四氢变叶木素D(CMNPD21999)表现出最强的结合亲和力和稳定性,以及更好的类药性和零毒性。这些特性突出了其作为一种有前景的CDC7抑制药物候选物和未来癌症治疗开发的潜力。此外,还需要进行更多的体外和体内研究来证实这种药物候选物对CDC7蛋白的有效性。

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