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Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.
Li, Hongdong; Ma, Liyuan; Luo, Fengyuan; Liu, Wenkai; Li, Na; Hu, Tao; Zhong, Haijian; Guo, You; Hong, Guini.
Afiliación
  • Li H; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Ma L; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Luo F; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Liu W; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Li N; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Hu T; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Zhong H; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China.
  • Guo Y; Medical Big Data and Bioinformatics Research Centre at First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China. gy@gmu.edu.cn.
  • Hong G; School of Medical Information Engineering, Gannan Medical University, Ganzhou, 341000, China. hongguini08@gmail.com.
BMC Genomics ; 24(1): 96, 2023 Mar 02.
Article en En | MEDLINE | ID: mdl-36864382
ABSTRACT

BACKGROUND:

Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.

METHODS:

We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.

RESULTS:

Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436 glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387 glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).

CONCLUSIONS:

The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: MicroARNs / Enfermedad de Alzheimer Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: MicroARNs / Enfermedad de Alzheimer Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2023 Tipo del documento: Article País de afiliación: China