Identification of a Novel Biomarker, SEMA5A, for Non–Small Cell Lung Carcinoma in Nonsmoking Women

By Tzu-Pin Lu, Mong-Hsun Tsai, Jang-Ming Lee, Chung-Ping Hsu, Pei-Chun Chen, Chung-Wu Lin, Jin-Yuan Shih, Pan‐Chyr Yang, Chuhsing Kate Hsiao, Liang-Chuan Lai and 1 more. 2010

289 citations recorded by the source corpus.

Background: Although cigarette smoking is the major risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. The genetic mechanisms of carcinogenesis in nonsmokers are unclear, but semaphorins have been suggested to play a role as lung tumor suppressors. This report is a comprehensive analysis of the molecular signature of nonsmoking female lung cancer patients in Taiwan, with a particular focus on the semaphorin gene family. Methods: Sixty pairs of tumor and adjacent normal lung tissue specimens were analyzed by using Affymetrix U133plus2.0 expression arrays. Differentially expressed genes in tumor tissues were identified by a paired t test and validated by reverse transcriptase-PCR and immunohistochemistry. Functional analysis was conducted by using Ingenuity Pathway Analysis as well as gene set enrichment analysis and sigPathway algorithms. Kaplan-Meier survival analyses were used to evaluate the association of SEMA5A expression and clinical outcome. Results: We identified 687 differentially expressed genes in non–small cell lung carcinoma (NSCLC). Many of these genes, most notably the semaphorin family, were participants in the axon guidance signaling pathway. The downregulation of SEMA5A in tumor tissue, both at the transcriptional and translational levels, was associated with poor survival among nonsmoking women with NSCLC. Conclusions: In summary, several semaphorin gene family members were identified as potential therapeutic targets, and SEMA5A may be useful as a prognostic biomarker for NSCLC, which may also be gender specific in Taiwanese patients. Impact: A novel biomarker for NSCLC is identified. Cancer Epidemiol Biomarkers Prev; 19(10); 2590–7. ©2010 AACR.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Fused Lasso Additive Model (2014, background): “Lung NS (Lu et al. 2010): 54,675 gene expression measurements from 60 pairs of tumor and adjacent normal lung tissue from nonsmoking women with nonsmall cell lung carcinoma, available from GEO at accession number GDS3837.”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “The microarray datasets consist of data from experiments GSE7670 [25] and GSE10072 [26], which were conducted using the HG-U133A array; and data from experiments GSE19804 [27] and GSE27262 [28], which were performed using HG-U133 plus 2.0 chip.”
miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets (2012, background): “Regarding the axon guidance pathway, many molecules play important roles in driving tumorigenesis and progression [42,43,44].”