Pancreatic cancer is highly aggressive and refractory to existing therapies. Connective tissue growth factor (CTGF/ CCN2) is a fibrosis-related gene that is thought to play a role in pancreatic tumor progression. However, CCN2 can be expressed in a variety of cell types, and the contribution of CCN2 derived from either tumor cells or stromal cells as it affects the growth of pancreatic tumors is unknown. Using genetic inhibition of CCN2, we have discovered that CCN2 derived from tumor cells is a critical regulator of pancreatic tumor growth. Pancreatic tumor cells derived from CCN2 shRNA-expressing clones showed dramatically reduced growth in soft agar and when implanted s.c. We also observed a role for CCN2 in the growth of pancreatic tumors implanted orthotopically, with tumor volume measurements obtained by positron emission tomography imaging. Mechanistically, CCN2 protects cells from hypoxia-mediated apoptosis, providing an in vivo selection for tumor cells that express high levels of CCN2. We found that CCN2 expression and secretion was increased in hypoxic pancreatic tumor cells in vitro, and we observed colocalization of CCN2 and hypoxia in pancreatic tumor xenografts and clinical pancreatic adenocarcinomas. Furthermore, we found increased CCN2 staining in clinical pancreatic tumor tissue relative to stromal cells surrounding the tumor, supporting our assertion that tumor cell-derived CCN2 is important for pancreatic tumor growth. Taken together, these data improve our understanding of the mechanisms responsible for pancreatic tumor growth and progression, and also indicate that CCN2 produced by tumor cells represents a viable therapeutic target for the treatment of pancreatic cancer. [Cancer Res 2009;69(3):775-84]

Purpose: Hepatocyte growth factor/scatter factor (HGF/SF) and its receptor, c-Met, play important roles in tumor development and progression. In this study, we measured the serum HGF levels in patients with esophageal squamous cell carcinoma (ESCC) to evaluate its relationships with clinicopathologic features and the role of HGF in ESCC. Experimental Design: One hundred and forty-nine patients with ESCC were studied. Pretherapy serum was collected and ELISA was used to detect the concentrations of HGF, vascular endothelial growth factor (VEGF), and interleukin 8 (IL-8). The function of HGF was shown by invasion chamber assay. Results: Pretherapy serum HGF was found to be significantly higher in patients with ESCC than in control subjects. The levels of HGF correlated significantly with advanced tumor metastasis stage and survival. Multivariate analyses showed that serum HGF level in cell migration was an independent prognostic factor. Increased HGF serum levels correlated positively with serum levels of VEGF and IL-8. Our results also showed that HGF was overexpressed in ESCC tissues and cell lines. In vitro study showed that HGF could stimulate ESCC cell to express VEGF and IL-8 and markedly enhance invasion and migration of ESCC cells. Furthermore, HGF-induced IL-8 and VEGF expression was dependent on extracellular signal-regulated kinase signaling pathways. The inhibition of extracellular signal-regulated kinase activation reduced HGF-mediated IL-8 and VEGF expression. Conclusions: Our results suggest that serum HGF may be a useful biomarker of tumor progression and a valuable independent prognostic factor in patients with ESCC. HGF may be involved in the progression of ESCC as an autocrine/paracrine factor via enhancing angiogenesis and tumor cell invasion and migration.

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Purpose: Lymph node status is a strong predictor of outcome for lung cancer patients. Recently, several reports have hinted that gene expression profiles of primary tumor may be able to predict node status. The goals of this study were to determine if microarray data could be used to accurately classify patients with regard to pathologic lymph node status, and to determine if this analysis could identify patients at risk for occult disease and worse survival. Experimental Design: Two previously published lung adenocarcinoma microarray data sets were reanalyzed. Patients were separated into two groups based on pathologic lymph node positive (pN+) or negative (pN0) status, and prediction analysis of microarray (PAM) was used for training and validation to classify nodal status. Overall survival analysis was performed based on PAM classifications. Results: In the training phase, a 318-gene set gave classification accuracy of 88.4% when compared with pathology. Survival was significantly worse in PAM-positive compared with PAM-negative patients overall (P < 0.0001) and also when confined to pN0 patients only (P = 0.0037). In the validation set, classification accuracy was again 94.1% in the pN+ patients but only 21.2% in the pN0 patients. However, among the pN0 patients, recurrence rates and overall survival were significantly worse in the PAM-positive compared with PAM-negative patients (P = 0.0258 and 0.0507). Conclusions: Analysis of gene expression profiles from primary tumor may predict lymph node status but frequently misclassifies pN0 patients as node positive. Recurrence rates and overall survival are worse in these “misclassified” patients, implying that they may in fact have occult disease spread.

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Mass spectrometry (MS) driven metabolomics is af requently used tool in various areas of life sciences; however,t he analysis of polar metabolites is less commonly included. In general,m etabolomic analyses lead to the detection of the total amount of all covered metabolites. This is currently am ajor limitation with respect to metabolites showing high turnover rates, but no changes in their concentration.S uch metabolites and pathways could be crucial metabolic nodes (e.g.,p otential drug targets in cancer metabolism). As table-isotope tracing capillary electrophoresis-mass spectrometry( CE-MS) metabolomic approach wasdeveloped to cover both polar metabolites and isotopologues in an on-targeted way.A ni nhouse developed software enables high throughput processing of complex multidimensionald ata. The practicability is demonstrated analyzing[ U-13 C]-glucose exposed prostate cancera nd non-cancer cells. This CE-MS-driven analytical strategy complements polarm etabolite profiles throughi sotopologue labeling patterns, thereby improving not only the metabolomic coverage, but also the understanding of metabolism.[a] Dr.

Early diagnosis of hepatocellular carcinoma (HCC) remains challenging to date. Characteristic metabolic deregulations of HCC may enable novel biomarkers discovery for early diagnosis. A capillary electrophoresis-time of flight mass spectrometry (CE-TOF/MS)-based metabolomics approach was performed to discover and validate potential biomarkers for HCC from the diethylnitrosamine-induced rat hepatocarcinogenesis model to human subjects. Time series sera from the animal model were evaluated using multivariate and univariate analyses to reveal dynamic metabolic changes. Two independent human cohorts (populations I and II) containing 122 human serum specimens were enrolled for validations. A novel biomarker pattern of ratio creatine/betaine which reflects the balance of methylation was identified. This biomarker pattern achieved effective classification of pre-HCC and HCC stages in animal model. It was still effective in the diagnosis of HCC from high-risk patients with cirrhotic nodules, achieving AUC values of 0.865 and 0.905 for two validation cohorts, respectively. The diagnosis of small HCC from cirrhosis with an AUC of 0.928 highlighted the potential for early diagnosis. This ratio biomarker can also improve the diagnostic performance of α-fetoprotein (AFP). This study demonstrates the efficacy of present strategy for biomarker discovery, and the potential of metabolomics approach to provide novel insights for disease study.