Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.

Dysregulation of microRNAs has a critical role in cancer progression. Here we identify an intronic microRNA, miR-135b that is upregulated in highly invasive non-small-cell lung cancer cells. Expression of miR-135b enhances cancer cell invasive and migratory abilities in vitro and promotes cancer metastasis in vivo, while specific inhibition of miR-135b by a miR-135b-specific molecular sponge and antagomirs suppresses cancer cell invasion, orthotopic lung tumour growth and metastasis in a mouse model. miR-135b targets multiple key components in the Hippo pathway, including LATS2, b-TrCP and NDR2, as well as LZTS1. Expression of miR-135b, LZTS1, LATS2 and nuclear TAZ predicts poor outcomes of non-small-cell lung cancer. We find that miR-135b is dually regulated by DNA demethylation and nuclear factor-kappaB signalling, implying that abnormal expression of miR-135b in cancer may result from inflammatory and epigenetic modulations. We conclude that miR-135b is an oncogenic microRNA and a potential therapeutic target for non-small-cell lung cancer.

Highlights d First deep proteogenomic landscape of non-smoking lung adenocarcinoma in East Asia d Identified age, sex-related endogenous, and environmental carcinogen mutagenic processes d Proteome-informed classification distinguished clinical features within early stages d Protein networks identified tumorigenesis hallmarks, biomarkers, and druggable targets