A smoking-associated 7-gene signature for lung cancer diagnosis and prognosis

Abstract

Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival. This approach identified and validated a 7-gene signature for lung cancer diagnosis and prognosis in smokers using patient transcriptional profiles (n=847). The smoking-associated gene coexpression networks in lung adenocarcinoma tumors (n=442) were highly significant in terms of biological relevance (network precision = 0.91, FDR<0.01) when evaluated with numerous databases containing multi-level molecular associations. The gene coexpression network in smoking lung adenocarcinoma patients was confirmed in qRT-PCR assays of the identified biomarkers and involved signaling pathway genes in human lung adenocarcinoma cells (H23) treated with 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Furthermore, the western blotting results of p53, phospho-p53, Rb and EGFR in NNK-treated H23 and transformed normal human lung epithelial cells (BEAS-2B) support their functional involvement in smoking-induced lung cancer carcinogenesis and progression.

The antiangiogenic action of cisplatin on endothelial cells is mediated through the release of tissue inhibitor of matrix metalloproteinases-1 from lung cancer cells

“…To evaluate whether the observed antiangiogenic effects were restricted to a cancer cell microenvironment, the same experimental setting was used to perform experiments with non-cancer cells. For this purpose, BEAS-2B, a cell line established from the bronchial epithelium of individuals without cancer [ 28 ], which was therefore assigned as “a normal lung epithelial cell line” [ 29 , 30 ], was used for the preparation of CM.…”

“…Since angiogenesis is a multistep mechanism, additional intercellular regulations involved in cisplatin-induced antiangiogenic responses appear feasible. Accordingly, MMP-2 downregulation does not exclusively occur in endothelial cells [ 6 , 30 ], but has also been shown to occur in lysates and culture media from cancer cells such as glioblastoma cells exposed to 25 µM cisplatin [ 49 ]. However, the effects of cisplatin on members of the MMP family in cancer cells are obviously inconsistent.…”

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

“…These signatures developed based on gene expression profiles suggested that screening new prognostic cancer biomarkers is a promising approach to identify high-risk patients for subsequent clinical decision-making, such as therapy selection, outcome prediction and disease progression tracking. As regard to lung cancer, Wan et al (2012) , identified a smoking-associated 7-gene signature for predicting patient’s diagnosis and prognosis in smokers. Similarly, the authors also developed a smoking-associated 6-gene signature that predicts patient’s risk and survival by modeling crosstalk with major lung cancer signaling pathways ( Guo and Wan, 2012 ).…”

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer

“…This is an important improvement because categorized CNV and gene expression data should have at least three states to be biologically relevant, representing amplification/normal/deletion, or upregulation/normal/downregulation. We have utilized the Boolean implication networks to model genome-scale co-expression networks and crosstalk with major NSCLC signaling pathways [37,116,117]. Our Boolean implication networks identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases [37,116,117]; meanwhile, Boolean implication networks revealed more biologically relevant molecular interactions than the Boolean networks [113][114][115], Bayesian networks, and Pearson's correlation networks [118].…”