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

Background: 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.

Objective: 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.

Methods: 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.

Results: 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 > 4.6 ng/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).

Conclusions: 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.