Long non-coding RNA (lncRNA) is a new class of regulative non-coding RNA, with a length larger than 200 nucleotides. Recent studies found that there are close relations between disregulative lncRNAs and human tumors. However, the clinical significances are largely unknown. In this study, we investigated the lncRNA-Fer-1-like protein 4 (FER1L4) level in gastric cancer tissues and plasma. The FER1L4 level in human tissues and plasma were measured by real-time quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR). Then, the correlations between the tissue or plasma FER1L4 levels and clinicopathological factors were assessed. A receiver operating characteristic (ROC) curve was constructed for differentiating GC patients from controls. Compared to matched adjacent non-tumorous tissues, FER1L4 expression levels in 91.80 % (56/61) of gastric cancer tissues are significantly decreased. The low FER1L4 level were associated with tumor size (p < 0.001), histologic grade (p = 0.001), general classification (p < 0.001), depth of invasion (p < 0.001), lymphatic metastasis (p < 0.001), distant metastasis (p = 0.003), TNM stage (p < 0.001), vessel or nerve invasion (p < 0.001 or p = 0.003), and serum CA72-4 (p < 0.001). The area under the ROC curve (AUC) was up to 0.778 (p < 0.001) and the sensitivity and specificity were 67.2 and 80.3 %, respectively. Also, plasma FER1L4 was detected by qRT-PCR. Our data show that there was no difference of plasma FER1L4 level between healthy person and preoperative gastric cancer patients, with a sharp decline in 63.9 % (53/83) of gastric cancer patients 2 weeks after surgery (p = 0.028). Taken together, FER1L4 might play a crucial role in human gastric cancer and may be a new potential biomarker for clinical prognosis evaluation.

BackgroundTripartite Motif Containing 11 (TRIM11), a member of TRIM proteins, is overexpressed in high-grade gliomas and plays an oncogenic function in glioma biology. However, little is known about the role of TRIM11 in lung cancer.MethodsWe analyzed TRIM11 mRNA expression in lung cancer tissues and adjacent non-neoplastic tissues by real-time PCR. We then explored the function of TRIM11 in lung cancer cells by small interfering RNA-mediated downregulation of this protein followed by analyses of cell proliferation, migration and invasion.ResultsTRIM11 was highly expressed in lung cancer tissues and lung cancer cell lines. The higher expression of TRIM11 was correlated with the poorer prognosis of patients. Suppressing of TRIM11 expression in lung cancer cells with higher expression of TRIM11 (A549 and NCI-H446 cells) significantly reduced cell growth, motility and invasiveness. We further demonstrated that knockdown of TRIM11 affected the expression of cell proliferation-related proteins (Cyclin D1 and PCNA), and epithelial-mesenchymal transformation-related proteins (VEGF, MMP-2, MMP-9, Twist1, Snail and E-cadherin). The activity of ERK and PI3K/AKT was also suppressed in TRIM11 knocked down cells. Further experiments in lung cells with lower expression of TRIM11 (NCI-H460 and NCI-H1975 cells) with AKT inhibitor suggested that TRIM11 may promote cell motility and invasiveness through AKT pathway.ConclusionsOur results indicate that TRIM11 acts as an oncogene in lung cancer through promoting cell growth, migration and invasion. Our findings may have important implication for the detection and treatment of lung cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-016-0379-y) contains supplementary material, which is available to authorized users.

Bone marrow mesenchymal stem cells (BMSCs) have great potential in tissue engineering and clinical therapy, and various methods for isolation and cultivation of BMSCs have been reported. However, the best techniques are still uncertain. Therefore, we sought the most suitable among the four most common methods for BMSC separation from rabbits. BMSCs were obtained from untreated whole bone marrow (BM) adherent cultures, 3 volumes of red blood cells (RBC) lysed with ammonium chloride, 6 volumes of RBC lysed with ammonium chloride, and Ficoll density gradient centrifugation. Then, isolated BMSCs were evaluated with respect to primary cell yield, number of CFU-F colonies, proliferative capacity, cell phenotype, and chondrogenic differentiation potential. Our data show that BMSCs were successfully isolated by all four methods, and each method was similar with regard to cell morphology, phenotype, and differentiation potential. However, BMSCs from untreated whole BM adherent cultures had greater primary cell yields, larger colonies, and the shortest primary culture time (P<0.05). Moreover, the 4th generation of cultured cells had the strongest proliferative activity, the fastest growth rate and the most numerous cells compared with other cell passage generations (P<0.05). In conclusion, untreated whole BM adherent cultures are best for rabbit BMSC isolation and the 4th generation of cells has the strongest proliferation capacity.

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Long segment trachea defects are repaired by tracheal substitution, while the decellularized technology has been effectively employed to prepare tissue engineering trachea (TET). However, its clinical application is restrictied by...

MDR-related proteins PGP, GST-pi, Topo-II alpha and LRP are involved in multiple mechanisms of drug resistance in PGCA. Combined determination of PGP, GST-pi, Topo-II and LRP may be prospectively valuable for optimizing the chemotherapy regimes, developing high quality anti-cancer drugs, and further predicting the outcomes of those patients with PGCA.

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.

Lung cancer is the most commonly diagnosed type of cancer worldwide. Although TRIM65 is an important protein involved in white matter lesion, the role of TRIM65 in human cancer remains less understood. Here, we reported that TRIM65 was significantly overexpressed in lung cancer tissues compared with adjacent normal lung tissues. Furthermore, TRIM65 expression was closely related to overall survival of patients with lung cancer. Knock down of TRIM65 in two lung cancer cell lines, SPC-A-1 and NCI-H358, resulted in a significant reduction in cell proliferation, migration, invasion and adhesion and a dramatic increase in G0-G1 phase arrest and apoptosis. In vivo tumorigenesis experiment also revealed that depletion of TRIM65 expression inhibited NCI-H358 cell growth. Moreover, based on gene set enrichment analysis (GSEA) with The Cancer Genome Atlas (TCGA) dataset, we found that TRIM65 was positive related to cell cycle, metastasis up and RHOA-REG pathways, which was further validated by RT-PCR and Western blot in TRIM65 knockdown lung cancer cells and indicated a possible mechanism underlying its effects on lung cancer. In summary, our study suggests that TRIM65 may work as an oncogene and a new effective therapeutic target for lung cancer treatment.