Epithelial-mesenchymal transition (EMT), a critical process of cancer invasion and metastasis, is associated with stemness property of cancer cells. Though Oct4 and Nanog are homebox transcription factors essential to the self-renewal of stem cells and are expressed in several cancers, the role of Oct4/Nanog signaling in tumorigenesis is still elusive. Here microarray and quantitative real-time PCR analysis showed a parallel, elevated expression of Oct4 and Nanog in lung adenocarcinoma (LAC). Ectopic expressions of Oct4 and Nanog in LACs increased the percentage of CD133-expressing subpopulation and sphere formation, enhanced drug resistance, and promoted EMT. Ectopic expressions of Oct4 and Nanog activated Slug and enhanced the tumorinitiating capability of LAC. Furthermore, double knockdown of Oct4 and Nanog suppressed the expression of Slug, reversed the EMT process, blocked the tumorigenic and metastatic ability, and greatly improved the mean survival time of transplanted immunocompromised mice. The immunohistochemical analysis demonstrated that expressions of Oct4, Nanog, and Slug were present in high-grade LAC, and triple positivity of Oct4/Nanog/ Slug indicated a worse prognostic value of LAC patients. Our results support the notion that the Oct4/Nanog signaling controls epithelial-mesenchymal transdifferentiation, regulates tumor-initiating ability, and promotes metastasis of LAC. Cancer Res; 70(24); 10433-44. Ó2010 AACR.

The hypoxic tumor microenvironment induces epithelial-mesenchymal transition (EMT) in tumor cells and increases tumor cell malignancy. Previous studies indicated that malfunction of Wnt signaling is observed in some lung cancer patients. Athough crosstalk between hypoxia and Wnt signaling in tumor cells has recently been revealed, the detailed underlying mechanisms have not been well defined. In the present study, we demonstrated that hypoxia in lung adenocarcinoma cells can enhance Wnt signaling activity by stabilizing β-catenin and altering its localization into the nucleus. Overexpression of HIF-2α increased β-catenin expression, promoted cell mobility, and induced morphological changes to a greater degree than HIF-1α overexpression. Knockdown of HIF-2α decreased β-catenin expression and inhibited hypoxia-induced cell mobility. Moreover, we identified that phosphorylational activation of AKT1 by hypoxia and HIF-2α was required for Wnt activation upon hypoxia treatment. Downregulation of HIF-2α and β-catenin reduced colony formation when cells were exposed to long-term hypoxia treatment. Taken together, our data support that hypoxia activates PI3K/AKT as well as Wnt signaling in a HIF-2α-dependent manner, thus elevating the resistance of lung cancer cells to chronic hypoxia-induced stress.

We previously demonstrated that the epidermal growth factor receptor (EGFR) up-regulated miR-7 to promote tumor growth during lung cancer oncogenesis. Several lines of evidence have suggested that alterations in chromatin remodeling components contribute to cancer initiation and progression. In this study, we identified SMARCD1 (SWI/SNF-related, matrixassociated, actin-dependent regulator of chromatin, subfamily d, member 1) as a novel target gene of miR-7. miR-7 expression reduced SMARCD1 protein expression in lung cancer cell lines. We used luciferase reporters carrying wild type or mutated 3UTR of SMARCD1 and found that miR-7 blocked SMARCD1 expression by binding to two seed regions in the 3UTR of SMARCD1 and down-regulated SMARCD1 mRNA expression. Additionally, upon chemotherapy drug treatment, miR-7 downregulated p53-dependent apoptosis-related gene BAX (BCL2-associated X protein) and p21 expression by interfering with the interaction between SMARCD1 and p53, thereby reducing caspase3 cleavage and the downstream apoptosis cascades. We found that although SMARCD1 sensitized lung cancer cells to chemotherapy drug-induced apoptosis, miR-7 enhanced the drug resistance potential of lung cancer cells against chemotherapy drugs. SMARCD1 was down-regulated in patients with non-small cell lung cancer and lung adenocarcinoma cell lines, and SMARCD1 and miR-7 expression levels were negatively correlated in clinical samples. Our investigation into the involvement of the EGFR-regulated microRNA pathway in the SWI/ SNF chromatin remodeling complex suggests that EGFRmediated miR-7 suppresses the coupling of the chromatin remodeling factor SMARCD1 with p53, resulting in increased chemo-resistance of lung cancer cells.

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A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

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