Circulating miR-10b, soluble urokinase-type plasminogen activator receptor, and plasminogen activator inhibitor-1 as predictors of non-small cell lung cancer progression and treatment response.

Abstract

Despite advances in lung cancer treatment, most lung cancers are diagnosed at an advanced stage. Expression of microRNA10b (miR-10b) and fibrinolytic activity, as reflected by soluble urokinase-type plasminogen activator receptor (suPAR) and plasminogen activator inhibitor 1 (PAI-1), are promising biomarker candidates. To assess the expression of miR-10b, and serum levels of suPAR and PAI-1 in advanced stage non-small cell lung cancer (NSCLC) patients, and their correlation with progression, treatment response and prognosis. The present prospective cohort and survival study was conducted at Dharmais National Cancer Hospital and included advanced stage NSCLC patients diagnosed between March 2015 and September 2016. Expression of miR-10b was quantified using qRT-PCR. Levels of suPAR and PAI-1 were assayed using ELISA. Treatment response was evaluated using the RECIST 1.1 criteria. Patients were followed up until death or at least 1 year after treatment. Among the 40 patients enrolled, 25 completed at least four cycles of chemotherapy and 15 patients died during treatment. Absolute miR-10b expression ⩾ 592,145 copies/μL or miR-10b fold change ⩾ 0.066 were protective for progressive disease and poor treatment response, whereas suPAR levels ⩾ 4,237 pg/mL was a risk factor for progressive disease and poor response. PAI-1 levels > 4.6ng/mL was a protective factor for poor response. Multivariate analysis revealed suPAR as an independent risk factor for progression (ORa⁢d⁢j, 13.265; 95% confidence intervals (CI), 2.26577.701; P= 0.006) and poor response (ORa⁢d⁢j, 15.609; 95% CI, 2.221-109.704; P= 0.006), whereas PAI-1 was an independent protective factor of poor response (ORa⁢d⁢j, 0.127; 95% CI, 0.019-0.843; P= 0.033). Since miR-10b cannot be used as an independent risk factor for NSCLC progression and treatment response, we developed a model to predict progression using suPAR levels and treatment response using suPAR and PAI-1 levels. Further studies are needed to validate this model.