Hepatocellular carcinoma (HCC) is the most prevalent subtype of liver cancer, and it is characterized by a high rate of recurrence and heterogeneity. Liver cancer stem cells (CSCs) may well contribute to both of these pathological properties, but the mechanisms underlying their self-renewal and maintenance are poorly understood. Here, using transcriptome microarray analysis, we identified a long noncoding RNA (lncRNA) termed lncTCF7 that is highly expressed in HCC tumors and liver CSCs. LncTCF7 is required for liver CSC self-renewal and tumor propagation. Mechanistically, lncTCF7 recruits the SWI/SNF complex to the promoter of TCF7 to regulate its expression, leading to activation of Wnt signaling. Our data suggest that lncTCF7-mediated Wnt signaling primes liver CSC self-renewal and tumor propagation. In sum, therefore, we have identified an lncRNA-based Wnt signaling regulatory circuit that promotes tumorigenic activity in liver cancer stem cells, highlighting the role that lncRNAs can play in tumor growth and propagation.

Understanding the genetic origin of cancer at the molecular level has facilitated the development of novel targeted therapies. Aberrant activation of the ErbB family of receptors is implicated in many human cancers and is already the target of several anticancer therapeutics. The use of mAbs specific for the extracellular domain of ErbB receptors was the first implementation of rational targeted therapy. The cytoplasmic tyrosine kinase domain is also a preferred target for small compounds that inhibit the kinase activity of these receptors. However, current therapy has not yet been optimized, allowing for opportunities for optimization of the next generation of targeted therapy, particularly with regards to inhibiting heteromeric ErbB family receptor complexes.

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Purpose To identify metabolic pathways that are perturbed in pancreatic ductal adenocarcinoma (PDAC), we investigated gene-metabolite networks with integration of metabolomics and transcriptomics. Experimental design We have performed global metabolite profiling analysis on two independent cohorts of resected PDAC cases to identify critical metabolites alteration that may contribute to the progression of pancreatic cancer. We then searched for gene surrogates that were significantly correlated with the key metabolites by integrating metabolite and gene expression profiles. Results 55 metabolites were consistently altered in tumors as compared with adjacent nontumor tissues in a test cohort (N=33) and an independent validation cohort (N=31). Weighted network analysis revealed a unique set of free fatty acids (FFAs) that were highly co-regulated and decreased in PDAC. Pathway analysis of 157 differentially expressed gene surrogates revealed a significantly altered lipid metabolism network, including key lipolytic enzymes PNLIP, CLPS, PNLIPRP1, and PNLIPRP2. Gene expressions of these lipases were significantly decreased in pancreatic tumors as compared with nontumor tissues, leading to reduced FFAs. More importantly, a lower gene expression of PNLIP in tumors was associated with poorer survival in two independent cohorts. We further demonstrated that two saturated FFAs, palmitate and stearate significantly induced TRAIL expression, triggered apoptosis, and inhibited proliferation in pancreatic cancer cells. Conclusions Our results suggest that impairment in a lipolytic pathway involving lipases and a unique set of FFAs, may play an important role in the development and progression of pancreatic cancer and provide potential targets for therapeutic intervention.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide. To identify biologically relevant genes with prognostic and therapeutic significance in PDAC, we first performed the microarray gene-expression profiling in 45 matching pairs of tumor and adjacent non-tumor tissues from resected PDAC cases. We identified 36 genes that were associated with patient outcome and also differentially expressed in tumors as compared with adjacent non-tumor tissues in microarray analysis. Further evaluation in an independent validation cohort (N = 27) confirmed that DPEP1 (dipeptidase 1) expression was decreased (T: N ratio ∼0.1, P<0.01) in tumors as compared with non-tumor tissues. DPEP1 gene expression was negatively correlated with histological grade (Spearman correlation coefficient = −0.35, P = 0.004). Lower expression of DPEP1 in tumors was associated with poor survival (Kaplan Meier log rank) in both test cohort (P = 0.035) and validation cohort (P = 0.016). DPEP1 expression was independently associated with cancer-specific mortality when adjusted for tumor stage and resection margin status in both univariate (hazard ratio = 0.43, 95%CI = 0.24–0.76, P = 0.004) and multivariate analyses (hazard ratio = 0.51, 95%CI = 0.27–0.94, P = 0.032). We further demonstrated that overexpression of DPEP1 suppressed tumor cells invasiveness and increased sensitivity to chemotherapeutic agent Gemcitabine. Our data also showed that growth factor EGF treatment decreased DPEP1 expression and MEK1/2 inhibitor AZD6244 increased DPEP1 expression in vitro, indicating a potential mechanism for DPEP1 gene regulation. Therefore, we provide evidence that DPEP1 plays a role in pancreatic cancer aggressiveness and predicts outcome in patients with resected PDAC. In view of these findings, we propose that DPEP1 may be a candidate target in PDAC for designing improved treatments.

Our goal is to segment a video sequence into moving objects and the world scene. In recent work, spectral embedding of point trajectories based on 2D motion cues accumulated from their lifespans, has shown to outperform factorization and per frame segmentation methods for video segmentation. The scale and kinematic nature of the moving objects and the background scene determine how close or far apart trajectories are placed in the spectral embedding. Such density variations may confuse clustering algorithms, causing over-fragmentation of object interiors. Therefore, instead of clustering in the spectral embedding, we propose detecting discontinuities of embedding density between spatially neighboring trajectories. Detected discontinuities are strong indicators of object boundaries and thus valuable for video segmentation. We propose a novel embedding discretization process that recovers from over-fragmentations by merging clusters according to discontinuity evidence along inter-cluster boundaries. For segmenting articulated objects, we combine motion grouping cues with a centersurround saliency operation, resulting in "context-aware", spatially coherent, saliency maps. Figure-ground segmentation obtained from saliency thresholding, provides object connectedness constraints that alter motion based trajectory affinities, by keeping articulated parts together and separating disconnected in time objects. Finally, we introduce Gabriel graphs as effective per frame superpixel maps for converting trajectory clustering to dense image segmentation. Gabriel edges bridge large contour gaps via geometric reasoning without over-segmenting coherent image regions. We present experimental results of our method that outperform the state-of-the-art in challenging motion segmentation datasets.

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