Chun‐Houh Chen

BACKGROUND: Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. METHODS: We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. RESULTS: Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. CONCLUSIONS: Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

PURPOSE: Inflammation plays a critical role in cancer progression. In this study we investigate the pro-tumorigenic activities and gene expression profiles of lung cancer cells after interaction with macrophages. MATERIALS AND METHODS: We measured intratumoral microvessel counts and macrophage density in 41 lung cancer tumor specimens and correlated these with the patients' clinical outcome. The interaction between macrophages and cancer cell lines was assessed using a transwell coculture system. The invasive potential was evaluated by in vitro invasion assay. The matrix-degrading activity was assayed by gelatin zymography. The microarray was applied to a large-scale analysis of the genes involved in the interaction, as well as to monitor the gene expression profiles of lung cancer cells responding to anti-inflammatory drugs in cocultures. RESULTS: The macrophage density positively correlated with microvessel counts and negatively correlated with patient relapse-free survival (P < .05). After coculture with macrophages, lung cancer cell lines exhibited higher invasive potentials and matrix-degrading activities. We identified 50 genes by microarray that were upregulated more than two-fold in cancer cells after coculture. Northern blot analyses confirmed some gene expression such as interleukin-6, interleukin-8, and matrix metalloproteinase 9. The two-dimensional hierarchical clustering also demonstrated that the gene expression profiles of lung cancer cells responding to various anti-inflammatory drugs in cocultures are distinct. CONCLUSION: The interaction of lung cancer cells and macrophages can promote the invasiveness and matrix-degrading activity of cancer cells. Our results also suggest that a great diversity of gene expression occurs in this interaction, which may assist us in understanding the process of cancer metastasis.

Carbon nanotubes are a nanomaterial that is extensively used in industry. The potential health risk of chronic carbon nanotubes exposure has been raised as of great public concern. In the present study, we have demonstrated that intratracheal instillation of 0.5 mg of single-walled carbon nanotubes (SWCNT) into male ICR mice (8 weeks old) induced alveolar macrophage activation, various chronic inflammatory responses, and severe pulmonary granuloma formation. We then used Affymetrix microarrays to investigate the molecular effects on the macrophages when exposed to SWCNT. A biological pathway analysis, a literature survey, and experimental validation suggest that the uptake of SWCNT into the macrophages is able to activate various transcription factors such as nuclear factor kappaB (NF-kappaB) and activator protein 1 (AP-1), and this leads to oxidative stress, the release of proinflammatory cytokines, the recruitment of leukocytes, the induction of protective and antiapoptotic gene expression, and the activation of T cells. The resulting innate and adaptive immune responses may explain the chronic pulmonary inflammation and granuloma formation in vivo caused by SWCNT.

Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.