Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma.

Abstract

BACKGROUND: Lung adenocarcinom (AC) is the most common form of lung cancer. Currently, the number of medical options to deal with lung cancer is very limited. In this study, we aimed to investigate potential therapeutic compounds for lung adenocarcinoma based on integrative analysis. METHODOLOGY/PRINCIPAL FINDINGS: The candidate therapeutic compounds were identified in a two-step process. First, a meta-analysis of two published microarray data was conducted to obtain a list of 343 differentially expressed genes specific to lung AC. In the next step, expression profiles of these genes were used to query the Connectivity-Map (C-MAP) database to identify a list of compounds whose treatment reverse expression direction in various cancer cells. Several compounds in the categories of HSP90 inhibitor, HDAC inhibitor, PPAR agonist, PI3K inhibitor, passed our screening to be the leading candidates. On top of the list, three HSP90 inhibitors, i.e. 17-AAG (also known as tanespimycin), monorden, and alvespimycin, showed significant negative enrichment scores. Cytotoxicity as well as effects on cell cycle regulation and apoptosis were evaluated experimentally in lung adenocarcinoma cell line (A549 or GLC-82) with or without treatment with 17-AAG. In vitro study demonstrated that 17-AAG alone or in combination with cisplatin (DDP) can significantly inhibit lung adenocarcinoma cell growth by inducing cell cycle arrest and apoptosis. CONCLUSIONS/SIGNIFICANCE: We have used an in silico screening to identify compounds for treating lung cancer. One such compound 17-AAG demonstrated its anti-lung AC activity by inhibiting cell growth and promoting apoptosis and cell cycle arrest.