{"title":"揭示lncrna在结直肠癌中的诊断能力:一项荟萃分析","authors":"Wen Chen, Xinliang Liu, Zhenheng Wu, Haifen Tan, Fuqian Yu, Dongmei Wang, Xiaodan Lin, Zhigang Chen","doi":"10.1186/s12938-025-01431-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>In summary, our findings demonstrate that lncRNAs have a promising diagnostic accuracy for CRC, underscoring their potential as effective non-invasive biomarkers.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"103"},"PeriodicalIF":2.9000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12379428/pdf/","citationCount":"0","resultStr":"{\"title\":\"Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.\",\"authors\":\"Wen Chen, Xinliang Liu, Zhenheng Wu, Haifen Tan, Fuqian Yu, Dongmei Wang, Xiaodan Lin, Zhigang Chen\",\"doi\":\"10.1186/s12938-025-01431-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>In summary, our findings demonstrate that lncRNAs have a promising diagnostic accuracy for CRC, underscoring their potential as effective non-invasive biomarkers.</p>\",\"PeriodicalId\":8927,\"journal\":{\"name\":\"BioMedical Engineering OnLine\",\"volume\":\"24 1\",\"pages\":\"103\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12379428/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BioMedical Engineering OnLine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s12938-025-01431-3\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-025-01431-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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