尿液生物标志物分析作为早期检测胰腺癌的诊断工具:分子量化方法

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-08-08 DOI:10.1016/j.compbiolchem.2024.108171
Safia Samir , Mohamed El-Ashry , Waleed Soliman , Marwa Hassan
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引用次数: 0

摘要

背景和目的胰腺导管腺癌(PDAC)并不常见。目前,还没有用于早期检测 PDAC 的非侵入性生物标志物。在此,我们打算找出一组能够区分早期 PDAC 患者和健康人的尿液标记物。患者和方法我们使用定量实时 PCR(qPCR)检测了 75 份 PDAC 患者和 50 份健康对照者的尿液样本。所选生物标记物为淋巴管内皮 HA 受体 (LYVE-1)、再生胰岛衍生 1 alpha (REG1A) 和三叶草因子家族 (TFF1)。结果LYVE-1、REG1A 和 TFF1 在 PDAC 中的表达与健康人相比明显升高(p < 0.05)。结论我们发现 REG1A、TFF1 和 LYVE1 这三种诊断生物标志物可以在无创尿液标本中检测出早期胰腺癌患者,并提高了灵敏度和特异性。据我们所知,此前还没有研究对埃及人群尿液中这些生物标志物的 mRNA 表达水平进行过调查。
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Urinary biomarkers analysis as a diagnostic tool for early detection of pancreatic adenocarcinoma: Molecular quantification approach

Background and aims

Pancreatic ductal adenocarcinoma (PDAC) is infrequent. Currently, non-invasive biomarkers for early detection of PDAC are not accessible. Here, we intended to identify a set of urine markers able to discriminate patients with early-stage PDAC from healthy individuals.

Patients and methods

Seventy-five urine samples from PDAC patients and 50 healthy controls were assayed using quantitative real-time PCR (qPCR). The chosen biomarkers were lymphatic vessel endothelial HA receptor (LYVE-1), regenerating islet-derived 1 alpha (REG1A), and trefoil factor family (TFF1).

Results

LYVE-1, REG1A, and TFF1 expression in PDAC proved to be significantly elevated compared to healthy individuals (p < 0.05). Determination of these markers' expression might be useful for early tumor diagnosis with a sensitivity of 96 %, 100 %, and 73.33 % respectively, and a specificity of 100 %, 82 %, and 100 % respectively.

Conclusion

We have recognized three diagnostic biomarkers REG1A, TFF1, and LYVE1 that can detect patients with early-stage pancreatic cancer in non-invasive urine specimens with improved sensitivity and specificity. To the best of our knowledge, there have been no prior investigations examining the mRNA expression levels of them in urine within the Egyptian population.

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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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