MicroRNAs as a novel class of diagnostic biomarkers for the detection of osteosarcoma: a meta-analysis

Liu, Hong; Li, Ping; Chen, Liang; Jian, Chao; Li, Zonghuan und Yu, Aixi (2017): MicroRNAs as a novel class of diagnostic biomarkers for the detection of osteosarcoma: a meta-analysis. In: Oncotargets and therapy, Bd. 10: S. 5229-5236

Abstract

MicroRNAs (miRNAs) have been considered as promising diagnostic biomarkers for many diseases, especially for cancers. Numerous studies have reported the value of miRNAs in the diagnosis of osteosarcoma (OS), but the results vary greatly across different studies. Therefore, we conducted this meta-analysis to assess the prospective diagnostic value of miRNAs in diagnosing OS. All relevant articles from prior to July 28, 2017 were selected from PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, and Wan-fang databases. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was performed to assess the quality of each article. A random-effects model was used to pool the sensitivity and specificity of the positive likelihood ratio (PLR), negative likelihood ratio (NLR) and, diagnostic odds ratio (DOR) together with the area under the curve (AUC) to evaluate diagnostic values. Seventeen studies comprising 2,214 OS patients and 1,534 healthy humans were included in our meta-analysis. The pooled estimations indicated that the miRNAs had a high accuracy for diagnosing OS, with a sensitivity of 0.82, specificity of 0.88, PLR of 10.96, NLR of 0.20, DOR of 54.55, and AUC of 0.93. Twenty-five miRNAs were differentially expressed in OS, including 17 upregulated and 8 downregulated. These miRNAs were correlated with survival time, tumor size, cell differentiation, tumor node metastasis staging, metastasis, tumor/cell invasion, pathological type, and response to radiotherapy and chemotherapy. Several different miRNAs are expressed in OS, and some of them might be potential biomarkers for the early diagnosis of OS.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1178-6930
• Sprache: Englisch