TY - JOUR
T1 - Non-coding RNAs as prognostic biomarkers
T2 - A mirna signature specific for aggressive early-stage lung adenocarcinomas
AU - Dama, Elisa
AU - Melocchi, Valentina
AU - Mazzarelli, Francesco
AU - Colangelo, Tommaso
AU - Cuttano, Roberto
AU - Candia, Leonarda Di
AU - Ferretti, Gian Maria
AU - Taurchini, Marco
AU - Graziano, Paolo
AU - Bianchi, Fabrizio
N1 - Funding Information:
Funding: This work was supported by Associazione Italiana Ricerca sul Cancro [MFAG-17568 and IG-22827 to F.B.], the Italian Ministry of Health [GR-2016-02363975 and CLEARLY to F.B.; GR-2019-12370460 to T.C.]. R.C. was supported by a fellowship from Umberto Veronesi Foundation and Pezcoller Foundation. T.C. was supported by a fellowship from Associazione Italiana Ricerca sul Cancro (#19548) and Umberto Veronesi Foundation.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Lung cancer burden can be reduced by adopting primary and secondary prevention strategies such as anti-smoking campaigns and low-dose CT screening for high risk subjects (aged >50 and smokers >30 packs/year). Recent CT screening trials demonstrated a stage-shift towards earlier stage lung cancer and reduction of mortality (~20%). However, a sizable fraction of patients (30–50%) with early stage disease still experience relapse and an adverse prognosis. Thus, the identification of effective prognostic biomarkers in stage I lung cancer is nowadays paramount. Here, we applied a multi-tiered approach relying on coupled RNA-seq and miRNA-seq data analysis of a large cohort of lung cancer patients (TCGA-LUAD, n = 510), which enabled us to identify prognostic miRNA signatures in stage I lung adenocarcinoma. Such signatures showed high accuracy (AUC ranging between 0.79 and 0.85) in scoring aggressive disease. Importantly, using a network-based approach we rewired miRNA-mRNA regulatory networks, identifying a minimal signature of 7 miRNAs, which was validated in a cohort of FFPE lung adenocarcinoma samples (CSS, n = 44) and controls a variety of genes overlapping with cancer relevant pathways. Our results further demonstrate the reliability of miRNA-based biomarkers for lung cancer prognostication and make a step forward to the application of miRNA biomarkers in the clinical routine.
AB - Lung cancer burden can be reduced by adopting primary and secondary prevention strategies such as anti-smoking campaigns and low-dose CT screening for high risk subjects (aged >50 and smokers >30 packs/year). Recent CT screening trials demonstrated a stage-shift towards earlier stage lung cancer and reduction of mortality (~20%). However, a sizable fraction of patients (30–50%) with early stage disease still experience relapse and an adverse prognosis. Thus, the identification of effective prognostic biomarkers in stage I lung cancer is nowadays paramount. Here, we applied a multi-tiered approach relying on coupled RNA-seq and miRNA-seq data analysis of a large cohort of lung cancer patients (TCGA-LUAD, n = 510), which enabled us to identify prognostic miRNA signatures in stage I lung adenocarcinoma. Such signatures showed high accuracy (AUC ranging between 0.79 and 0.85) in scoring aggressive disease. Importantly, using a network-based approach we rewired miRNA-mRNA regulatory networks, identifying a minimal signature of 7 miRNAs, which was validated in a cohort of FFPE lung adenocarcinoma samples (CSS, n = 44) and controls a variety of genes overlapping with cancer relevant pathways. Our results further demonstrate the reliability of miRNA-based biomarkers for lung cancer prognostication and make a step forward to the application of miRNA biomarkers in the clinical routine.
KW - Biomarkers
KW - Gene expression
KW - Lung cancer
KW - MicroRNA
KW - Prognosis
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U2 - 10.3390/ncrna6040048
DO - 10.3390/ncrna6040048
M3 - Article
AN - SCOPUS:85097923290
VL - 6
SP - 1
EP - 13
JO - Non-coding RNA
JF - Non-coding RNA
SN - 2311-553X
IS - 4
M1 - 48
ER -