揭示lncrna在结直肠癌中的诊断能力:一项荟萃分析

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Wen Chen, Xinliang Liu, Zhenheng Wu, Haifen Tan, Fuqian Yu, Dongmei Wang, Xiaodan Lin, Zhigang Chen
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引用次数: 0

摘要

背景:结直肠癌(CRC)是一种高度侵袭性和广泛性的恶性肿瘤。尽管长链非编码rna (lncRNAs)常被用作诊断性生物标志物,但其在结直肠癌中的诊断有效性仍不确定。方法:2015年1月1日至2024年4月1日,我们对Embase、中国知网、万方、PubMed、Cochrane Library、Web of Science (WoS)进行综合检索。采用合并敏感性、特异性、阳性似然比(PLR)、阴性似然比(NLR)、诊断优势比(DOR)、受试者工作特征曲线下面积(AUC)和Fagan图分析评价lncrna的总体检测性能。此外,我们使用Deeks漏斗图不对称检验来评估发表偏倚。结果:28篇出版物被纳入本荟萃分析。汇总诊断数据如下:汇总敏感性为0.79 (95% CI, 0.75-0.83)。合并特异性为0.81 (95% CI, 0.78-0.84)。PLR为3.68 (95% CI, 3.18-4.26)。NLR为0.28 (95% CI, 0.24-0.33)。DOR为15.01 (95% CI, 11.85-19.00)。AUC为0.87 (95% CI, 0.84-0.90)。Deeks漏斗图不对称检验未发现显著的发表偏倚证据(p < 0.05)。Fagan图分析显示,阳性结果的后验概率为81%,阴性结果的后验概率为20%。单变量元回归确定了数据中的多种异质性来源,包括年份、样本量和样本。结论:综上所述,我们的研究结果表明,lncrna对结直肠癌具有很好的诊断准确性,强调了它们作为有效的非侵入性生物标志物的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.

Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.

Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.

Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.

Background: Colorectal cancer (CRC) is a highly aggressive and extensive malignancy. Although long noncoding RNAs (lncRNAs) are often used as diagnostic biomarkers, their diagnostic effectiveness in CRC remains uncertain.

Methods: From January 1, 2015, to April 1, 2024, we conducted a comprehensive search of Embase, China National Knowledge Infrastructure (CNKI), Wanfang, PubMed, Cochrane Library, and Web of Science (WoS). The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the receiver operating characteristic curve (AUC) and Fagan plot analysis were used to assess the overall test performance of lncRNAs. Moreover, we evaluated the publication bias using the Deeks' funnel plot asymmetry test.

Results: Twenty-eight publications were identified and incorporated into this meta-analysis. The aggregated diagnostic data were as follows: The pooled sensitivity was 0.79 (95% CI, 0.75-0.83). The pooled specificity was 0.81 (95% CI, 0.78-0.84). The PLR was 3.68 (95% CI, 3.18-4.26). The NLR was 0.28 (95% CI, 0.24-0.33). The DOR was 15.01 (95% CI, 11.85-19.00). The AUC was 0.87 (95% CI, 0.84-0.90). Deeks' funnel plot asymmetry test indicated no significant evidence of publication bias (p > 0.05). The Fagan plot analysis showed that the post-test probability was 81% for positive results and 20% for negative results. Univariate meta-regression identified multiple sources of heterogeneity in the data, including year, sample size and specimen.

Conclusion: In summary, our findings demonstrate that lncRNAs have a promising diagnostic accuracy for CRC, underscoring their potential as effective non-invasive biomarkers.

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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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