Abstract
Patients with EGFR-mutant non-small-cell lung cancer (NSCLC) greatly benefit from EGFR-tyrosine kinase inhibitors (EGFR-TKIs) while the prognosis of patients who lack EGFR-sensitive mutations (EGFR wild type, EGFR-WT) remains poor due to a lack of effective therapeutic strategies. There is an urgent need to explore the key genes that affect the prognosis and develop potentially effective drugs in EGFR-WT NSCLC patients. In this study, we clustered functional modules related to the survival traits of EGFR-WT patients using weighted gene co-expression network analysis (WGCNA). We used these data to establish a two-gene prognostic signature based on the expression of CYP11B1 and DNALI1 by combining the least absolute shrinkage and selection operator (LASSO) algorithms and Cox proportional hazards regression analysis. Following the calculation of risk score (RS) based on the two-gene signature, patients with high RSs showed a worse prognosis. We further explored targeted drugs that could be effective in patients with a high RS by the connectivity map (CMap). Surprisingly, multiple HDAC inhibitors (HDACis) such as trichostatin A (TSA) and vorinostat (SAHA) that may have efficacy were identified. Also, we proved that HDACis could inhibit the proliferation and metastasis of NSCLC cells in vitro. Taken together, our study identified prognostic biomarkers for patients with EGFR-WT NSCLC and confirmed a novel potential role for HDACis in the clinical management of EGFR-WT patients.
Highlights
Lung cancer has the highest morbidity and mortality in China and around the world
In order to identify the key prognostic biomarkers for Epidermal growth factor receptor (EGFR)-WT non-small-cell lung cancer (NSCLC) patients, we performed a systematic analysis of the GSE31852 dataset that included 62 patients with complete progression-free survival (PFS) information
The area under the curve (AUC) for the two-gene risk score (RS) at 1, 2, and 6 months were 0.736, 0.781, and 0.848, respectively. These values were significantly better than those obtained based on simple gene expression for predicting PFS at each time point (Figures S2E, F). These results indicated that the two-gene RS signature established by the PFS-related modules of weighted gene co-expression network analysis (WGCNA) reflected the survival of EGFR wild-type (EGFR-WT) patients and had better predictive power than independent prognostic gene expression
Summary
Lung cancer has the highest morbidity and mortality in China and around the world. Most patients presented with lung cancer at a late stage owing to hidden onset and unspecific symptoms associated with the disease [1, 2]. Lung cancer is generally classified into nonsmall-cell lung cancer (NSCLC) and small cell lung cancer (SCLC) This traditional classification according to histological assessment fails to account for the complex prognosis and drug resistance associated with the disease [3]. Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) such as gefitinib and erlotinib were the first targeted therapy for NSCLC They have been widely applied in the clinical application for NSCLC patients carrying EGFR-sensitive mutations such as in-frame deletions at exon 19 and exon 21 point mutations (L858R). For patients with no EGFR gene mutations or an unknown mutation status, platinum-based doublet chemotherapy regimens remain the standard first-line therapy [9, 10] In these cases, the tumor response rate is estimated to be less than 10% and overall survival (OS) is only slightly improved [11]. There is an unmet need to develop a novel therapy and to improve the prognosis for patients with EGFR wild-type (EGFR-WT) NSCLC
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