Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, A Coruña, 15071, Spain.
Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR). Instituto de Investigación Biomédica de A Coruña (INIBIC). Complexo Hospitalario Universitario de A Coruña (CHUAC), Sergas. Universidade da Coruña (UDC), Xubias de arriba, 84, A Coruña, 15006, Spain.
BMC Mol Cell Biol. 2020 Jul 8;21(1):52. doi: 10.1186/s12860-020-00295-w.
The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD.
The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP - Nilotinib, PTPRN - Venetoclax, VEGFA - Venetoclax and FABP6 - Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle.
The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.
癌症研究的主要挑战是确定不同的组学变量，这些变量对每个肿瘤具有预后价值和个性化诊断。诊断的个性化为每个患者设计和发现新的特异性治疗方法开辟了道路。在这种情况下，这项工作提供了新的方法来重新利用现有数据库并为研究创造附加值。本研究确定了三个在结肠腺癌（COAD）中具有显著预后价值的已发表签名。这些签名被组合在一个新的元签名中，并通过主要的机器学习（ML）和常规统计技术进行验证。此外，还通过分子对接（MD）方法进行了药物重新定位实验，以确定 COAD 中的新潜在治疗方法。
通过 ML 算法和差异基因表达分析验证了签名的预后潜力。获得的结果支持了这样一种可能性，即该元签名可能包含与 COAD 的预后和治疗相关的感兴趣基因。我们进行了药物重新定位研究，对元签名的不同蛋白质数据库（PDB）结构（共 155 个）与 FDA 批准的 81 种抗癌药物进行了分子对接（MD）分析。我们观察到四个感兴趣的相互作用：GLTP-Nilotinib、PTPRN-Venetoclax、VEGFA-Venetoclax 和 FABP6-Abemaciclib。研究了肿瘤和正常组织中 FABP6 基因及其在不同代谢途径中的作用，观察到 FABP6 基因作为治疗靶点的能力。我们的计算机模拟结果表明，FABP6 基因蛋白产物的联合以及 Abemaciclib 作为 CDK4/6 蛋白抑制剂的已知作用具有显著的特异性，因此，对细胞周期具有特异性。
我们的 ML 和差异表达实验的结果首先表明 FABP6 基因是一种新的癌症生物标志物，因为它在结肠肿瘤组织中具有特异性，而在健康的相邻组织中没有表达。接下来，MD 分析表明，药物 Abemaciclib 对 FABP6 基因的不同蛋白质结构具有特征亲和力。因此，计算机模拟实验表明，这是一个新的机会，应该通过实验验证，从而有助于降低药物筛选的成本和速度。基于这些原因，我们建议验证 Abemaciclib 药物治疗结肠癌。
