Suppr超能文献

非编码RNA与胃肠道癌症预后:观察性研究的系统评价和荟萃分析的综合评价

Non-coding RNAs and gastrointestinal cancers prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies.

作者信息

Zha Bowen, Luo Yuxi, Kamili Muladili, Zha Xiaqin

机构信息

The Sixth Clinical Medical College, Capital Medical University, Beijing, China.

The First Clinical Medical College, Capital Medical University, Beijing, China.

出版信息

Front Oncol. 2023 Jul 20;13:1193665. doi: 10.3389/fonc.2023.1193665. eCollection 2023.

Abstract

AIM

Provide an overview and a systematic evaluation of the evidence quality on the association between non-coding RNAs (ncRNAs) and prognosis value for gastrointestinal cancers (GICs).

METHODS

We searched the literature from three electronic databases: Pubmed, Embase, and Web of science, then carefully screened and extracted the primary information and results from the included articles. We use A measurable systematic review and meta-analysis evaluation tool (AMSTAR2) to evaluate the quality of methodology and then use the Grading of Recommendations Assessment 2, Development and Evaluation guideline (GRADE) make sure the reliability of the meta-analysis.

RESULTS

Overall, 182 meta-analyses from 58 studies were included in this study. Most of these studies are of low or very low quality. Using the scoring tool, we found that only two meta-analyses were rated as high reliability, and 17 meta-analyses were rated as medium reliability.

CONCLUSIONS

Although ncRNA has good prognostic value in some studies, only a tiny amount of evidence is highly credible at present. More research is needed in the future.

PROSPERO REGISTRATION NUMBER

CRD42022382296.

摘要

目的

对非编码RNA(ncRNA)与胃肠道癌(GICs)预后价值之间关联的证据质量进行概述和系统评价。

方法

我们检索了三个电子数据库(PubMed、Embase和Web of science)中的文献,然后仔细筛选并提取纳入文章的原始信息和结果。我们使用一种可测量的系统评价和荟萃分析评估工具(AMSTAR2)来评估方法学质量,然后使用推荐分级评估2、制定与评价指南(GRADE)来确保荟萃分析的可靠性。

结果

总体而言,本研究纳入了来自58项研究的182项荟萃分析。这些研究大多质量较低或非常低。使用评分工具,我们发现只有两项荟萃分析被评为高可靠性,17项荟萃分析被评为中等可靠性。

结论

虽然ncRNA在一些研究中具有良好的预后价值,但目前只有极少部分证据高度可信。未来需要更多的研究。

PROSPERO注册号:CRD42022382296。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e62/10399243/663fc6c04390/fonc-13-1193665-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验