Dama Elisa, Melocchi Valentina, Mazzarelli Francesco, Colangelo Tommaso, Cuttano Roberto, Di Candia Leonarda, Ferretti Gian Maria, Taurchini Marco, Graziano Paolo, Bianchi Fabrizio
Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo Della Sofferenza, 71013 San Giovanni Rotondo (FG), Italy.
Pathology Unit, Fondazione IRCCS Casa Sollievo Della Sofferenza, 71013 San Giovanni Rotondo (FG), Italy.
Noncoding RNA. 2020 Dec 15;6(4):48. doi: 10.3390/ncrna6040048.
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, = 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, = 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.
通过采取一级和二级预防策略，如开展反吸烟运动以及对高危人群（年龄>50岁且吸烟量>30包/年）进行低剂量CT筛查，可以减轻肺癌负担。最近的CT筛查试验表明肺癌分期向早期转变，且死亡率降低了约20%。然而，相当一部分（30-50%）早期疾病患者仍会复发并预后不良。因此，如今在I期肺癌中识别有效的预后生物标志物至关重要。在此，我们采用了一种多层方法，该方法依赖于对大量肺癌患者队列（TCGA-LUAD，n = 510）的RNA测序和miRNA测序数据分析，这使我们能够识别I期肺腺癌中的预后miRNA特征。这些特征在对侵袭性疾病进行评分时显示出高精度（AUC在0.79至0.85之间）。重要的是，我们使用基于网络的方法重新构建了miRNA-mRNA调控网络，确定了一个由7个miRNA组成的最小特征，该特征在一组FFPE肺腺癌样本（n = 44）中得到验证，并调控了多种与癌症相关途径重叠的基因。我们的结果进一步证明了基于miRNA的生物标志物用于肺癌预后评估的可靠性，并朝着将miRNA生物标志物应用于临床实践迈进了一步。
