肾损伤患者肿瘤标志物胃泌素释放肽前体的评价。

IF 3.6 3区 医学 Q2 ONCOLOGY
American journal of cancer research Pub Date : 2025-02-15 eCollection Date: 2025-01-01 DOI:10.62347/CBSP3728
Nan Duan, Zhihui Li, Zhiyan Li, Lu Pang, Jialin Du, Le Chang, Haiming Huang, Haixia Li
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引用次数: 0

摘要

胃泌素释放肽前体(ProGRP)是一种具有生物活性的GRP前体,可能作为一种新兴的肿瘤标志物在早期癌症诊断中发挥重要作用。在非恶性疾病和肾功能异常中也可能出现异常。本研究旨在探讨肾损伤特别是慢性肾病(chronic kidney disease, CKD)患者ProGRP水平的变化,确定CKD患者ProGRP水平的上参考区间及临床诊断价值,从而帮助肿瘤学家对ProGRP水平的解释及对恶性肿瘤的临床判断。这项横断面研究共纳入676人,分为五组:健康对照组(n=194)、CKD (n=272)、肾病综合征(n=137)、抗中性粒细胞细胞质抗体(ANCA)相关血管炎(AAV) (n=41)和尿路感染(UTI) (n=32)。共分析27项特征,包括年龄、性别和25项实验室指标。建立CKD诊断模型的机器学习算法。采用R软件进行统计分析。结果表明,CKD患者血清ProGRP水平显著高于健康对照组、UTI组和NS组(P < 0.01)。CKD的ProGRP参考上限为188.42 pg/ml, CKD IV-V型为245.40 pg/ml, NS为97.25 pg/ml。与健康对照组相比,CKD II、III、IV-V期患者血清ProGRP水平显著升高,且随CKD分级逐渐升高(P < 0.01)。随机森林(RF)模型在4种建筑机器学习算法中效果最好。选取ProGRP、估计肾小球滤过率(eGFR)、尿素、白蛋白(ALB)、直接胆红素(DBIL) 5个生命指标,建立诊断CKD的RF模型,曲线下面积(AUC)为0.96(95%可信区间[CI]: 0.94-0.97),灵敏度(0.89)和特异性(0.92)较高。本研究表明CKD、肾病综合征或AAV患者的ProGRP水平明显高于健康人群。基于DBIL、eGFR、ALB、尿素的ProGRP机器学习模型对CKD评价具有较好的临床价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of a tumor marker gastrin-releasing peptide precursor in the patients with kidney injuries.

Gastrin-releasing peptide precursor (ProGRP) is a bioactive precursor of GRP and might play an important role as an emerging tumor marker in early cancer diagnosis. It might also be abnormal in the nonmalignant disease and renal function abnormalities. The present study was undertaken to investigate the changes of ProGRP levels in patients with kidney injuries, especially with chronic kidney disease (CKD), determine the upper reference intervals and clinical diagnostic value of ProGRP in CKD, and thus help oncologists in interpreting ProGRP levels and making clinical judgments of malignances. 676 individuals were enrolled in this cross-sectional study and divided into five groups: healthy control (n=194), CKD (n=272), nephrotic syndrome (NS) (n=137), antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) (n=41), and urinary tract infection (UTI) (n=32). A total of 27 features including age, gender, and 25 laboratory markers were analyzed. Machine learning algorithms were built for the diagnostic models of CKD. Statistical analysis was performed by R software. It was shown that serum ProGRP level in CKD was significantly higher than that in healthy controls, UTI and NS (P < 0.01). The upper reference limit of ProGRP was 188.42 pg/ml for CKD, 245.40 pg/ml for CKD IV-V, and 97.25 pg/ml for NS. Compared with the healthy control, the level of serum ProGRP in CKD stages II, III, IV-V was significantly increased and elevated progressively with CKD grade (P < 0.01). Random Forest (RF) model works best among 4 building machine learning algorithms. 5 vital indicators, ProGRP, estimated glomerular filtration rate (eGFR), urea, albumin (ALB), and direct bilirubin (DBIL), were selected to establish RF model for diagnosing CKD with an area under the curve (AUC) of 0.96 (95% confidence interval [CI]: 0.94-0.97) and high sensitivity (0.89) and specificity (0.92). This study demonstrates that the level of ProGRP in patients with CKD, nephrotic syndrome or AAV, was significantly higher than that in the healthy population. The machine learning model of ProGRP with DBIL, eGFR, ALB, and urea, could provide good clinical value for CKD evaluation.

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来源期刊
自引率
3.80%
发文量
263
期刊介绍: The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.
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