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.

中文翻译：

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Abemaciclib在结肠癌中的分子对接和机器学习分析。

癌症研究中的主要挑战是鉴定不同的组蛋白变量，这些变量可为每种肿瘤提供预后价值和个性化诊断。诊断个性化的事实为每位患者的设计和发现新的特定治疗方法打开了大门。在这种情况下，这项工作提供了重新使用现有数据库并在研究中创造附加值的新方法。确定了三个在结肠腺癌（COAD）中具有重要预后价值的已发表签名。这些签名被合并为新的元签名，并通过主要的机器学习（ML）和常规统计技术进行了验证。此外，通过分子对接（MD）方法进行了药物再利用实验，以发现COAD中潜在的新疗法。通过ML算法和差异基因表达分析验证了签名的预后潜力。所获得的结果支持这种元签名可能包含用于COAD的预后和治疗的感兴趣基因的可能性。我们在进行分子对接（MD）分析后研究了药物的用途，在该方法中，元签名基因的不同蛋白质数据库（PDB）结构（共155种）面临FDA批准的81种抗癌药物。我们观察到了感兴趣的四个相互作用：GLTP-尼洛替尼，PTPRN-威尼托克斯，VEGFA-威尼托克斯和FABP6-Abemaciclib。在肿瘤和正常组织中研究了FABP6基因及其在不同代谢途径中的作用，我们观察到FABP6基因成为治疗靶标的能力。我们的计算机分析结果显示，FABP6基因的蛋白质产物的结合具有显着的特异性，并且已知Abemaciclib作为CDK4 / 6蛋白的抑制剂，因此也具有细胞周期的抑制作用。我们的ML和差异表达实验的结果首先显示出FABP6基因可能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却没有表达。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亲和力。因此，计算机模拟实验显示了应通过实验验证的新机会，从而有助于降低药物筛选的成本和速度。由于这些原因，我们建议验证用于治疗结肠癌的药物Abemaciclib。