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从发现到临床应用的生物标志物:面对肺癌的计算机临床前验证方法

Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer.

作者信息

Kori Medi, Gov Esra, Arga Kazim Yalcin, Sinha Raghu

机构信息

Department of Medical Biotechnology, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye.

Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.

出版信息

Biomark Insights. 2024 Oct 3;19:11772719241287400. doi: 10.1177/11772719241287400. eCollection 2024.

Abstract

BACKGROUND

Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application.

OBJECTIVE

Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment.

DESIGN

As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach.

METHODS

We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs.

RESULTS

We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively.

CONCLUSION

In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

摘要

背景

临床生物标志物有助于根据疾病风险、预后和/或对治疗的反应对患者进行更好的分类。尽管基于组学的经济实惠的方法为更快地识别潜在生物标志物铺平了道路,但生物标志物的验证对于将发现转化为临床应用是必要的。

目的

因此,在本研究中,我们强调了计算机方法的潜力,并提出并应用了3个新的计算机临床前验证步骤,以更好地识别真正值得临床投资的生物标志物。

设计

由于蛋白质生物标志物在临床上与其他分子生物标志物一样变得越来越重要,并且肺癌是癌症相关死亡的最常见原因,我们以肺癌的蛋白质生物标志物为例应用我们的计算机临床前验证方法。

方法

我们收集了报道的3种病例(肺腺癌-LUAD、鳞状细胞癌-LUSC和未指定的肺癌)的蛋白质生物标志物,并评估这些蛋白质生物标志物是否具有癌症改变特性(即作为肿瘤抑制因子或癌蛋白并代表癌症特征)、是否在体液中表达以及是否可以被FDA批准的药物靶向。

结果

我们收集了3008种肺癌蛋白质生物标志物、1189种LUAD蛋白质生物标志物和182种LUSC蛋白质生物标志物。在这些肺癌、LUAD和LUSC的蛋白质生物标志物中,分别只有28种、25种和6种蛋白质生物标志物通过了所检查的3个计算机临床前验证步骤,其中分别只有5种和2种生物标志物对肺癌和LUAD具有特异性。

结论

在本研究中,我们将计算机临床前验证方法应用于肺癌病例的蛋白质生物标志物。然而,这种方法可以应用并适用于所有癌症生物标志物。我们相信这种方法将极大地促进癌症生物标志物向临床阶段的转化,并为未来的生物标志物研究提供巨大潜力。

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