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Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

BACKGROUND: Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs. RESULTS: We developed a ne...

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Detalles Bibliográficos
Autores principales: Ren, Shulei, Lee, Wook, Han, Kyungsook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9014599/
https://www.ncbi.nlm.nih.gov/pubmed/35430805
http://dx.doi.org/10.1186/s12920-022-01231-x
Descripción
Sumario:BACKGROUND: Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs. RESULTS: We developed a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions and identified key miRNA–RNA pairs from the network. A prediction model using differential correlations of the miRNA–RNA pairs of a patient as features showed a much higher performance than other methods which use gene expression data. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient in several types of cancer. CONCLUSIONS: Differential correlations of miRNA–RNA pairs identified from patient-specific networks of miRNA mediated RNA interactions are powerful in predicting lymph node metastasis in cancer patients. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient of solid cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01231-x.