Metastasis-associated lung adenocarcinoma transcript 1(MALAT1), a long non-coding RNA (lncRNA), is up-regulated in many solid tumors and associated with cancer metastasis and recurrence. However, its role in hepatocellular carcinoma (HCC) remains poorly understood. In the present study, we evaluated the expression of MALAT1 by quantitative real-time PCR in 9 liver cancer cell lines and 112 HCC cases including 60 cases who received liver transplantation (LT) with complete follow-up data. Moreover, small interfering RNA (siRNA) was used to inhibit MALAT1 expression to investigate its biological role in tumor progression. We found that MALAT1 was up-regulated in both cell lines and clinical tissue samples. Patients with high expression level of MALAT1 had a significantly increased risk of tumor recurrence after LT, particularly in patients who exceeded the Milan criteria. On multivariate analysis, MALAT1 was an independent prognostic factor for predicting HCC recurrence (hazard ratio, 3.280, P = 0.003).In addition, inhibition of MALAT1 in HepG2 cells could effectively reduce cell viability, motility, invasiveness, and increase the sensitivity to apoptosis. Our data suggest that lncRNA MALAT1 play an important role in tumor progression and could be a novel biomarker for predicting tumor recurrence after LT and serve as a promising therapeutic target.

The dissemination, seeding, and colonization of circulating tumor cells (CTCs) serve as the root of distant metastasis. As a key step in the early stage of metastasis formation, colonization of CTCs in the (pre-)metastatic niche appears to be a valuable target. Evidence showed that inflammatory neutrophils possess both a CTC- and niche-targeting property by the intrinsic cell adhesion molecules on neutrophils. Inspired by this mechanism, we developed a nanosize neutrophil-mimicking drug delivery system (NM-NP) by coating neutrophils membranes on the surface of poly(latic-co-glycolic acid) nanoparticles (NPs). The membrane-associated protein cocktails on neutrophils membrane were mostly translocated to the surface of NM-NP via a nondisruptive approach, and the biobinding activity of neutrophils was highly preserved. Compared with uncoated NP, NM-NP exhibited enhanced cellular association in 4T1 cell models under shear flow in vitro, much higher CTC-capture efficiency in vivo, and improved homing to the premetastatic niche. Following loading with carfilzomib, a second generation of proteasome inhibitor, the NM-NP-based nanoformulation (NM-NP-CFZ) selectively depleted CTCs in the blood, prevented early metastasis and potentially inhibited the progress of already-formed metastasis. Our NP design can neutralize CTCs in the circulation and inhibit the formation of a metastatic niche.

We developed a knowledge-based statistical energy function for protein-ligand, protein-protein, and protein-DNA complexes by using 19 atom types and a distance-scale finite ideal-gas reference (DFIRE) state. The correlation coefficients between experimentally measured protein-ligand binding affinities and those predicted by the DFIRE energy function are around 0.63 for one training set and two testing sets. The energy function also makes highly accurate predictions of binding affinities of protein-protein and protein-DNA complexes. Correlation coefficients between theoretical and experimental results are 0.73 for 82 protein-protein (peptide) complexes and 0.83 for 45 protein-DNA complexes, despite the fact that the structures of protein-protein (peptide) and protein-DNA complexes were not used in training the energy function. The results of the DFIRE energy function on protein-ligand complexes are compared to the published results of 12 other scoring functions generated from either physical-based, knowledge-based, or empirical methods. They include AutoDock, X-Score, DrugScore, four scoring functions in Cerius 2 (LigScore, PLP, PMF, and LUDI), four scoring functions in SYBYL (F-Score, G-Score, D-Score, and ChemScore), and BLEEP. While the DFIRE energy function is only moderately successful in ranking native or near native conformations, it yields the strongest correlation between theoretical and experimental binding affinities of the testing sets and between rmsd values and energy scores of docking decoys in a benchmark of 100 protein-ligand complexes. The parameters and the program of the all-atom DFIRE energy function are freely available for academic users at http://theory.med.buffalo.edu.