Background/Aims: Hepatocellular carcinoma (HCC) is one of the most deadly diseases; metastasis and recurrence are the most important factors that affect the therapy of HCC chronically. Until now, the prognosis for the metastasis of HCC had not improved. Recently, several proteins that are related to metastasis and invasion of HCC were identified, but the effective markers still remain to be elucidated. Methods: In this study, comparative proteomics was used to study the differentially expressed proteins in two HCC cell lines MHCC97L and HCCLM9, which have low and high metastatic potentials, respectively. Results: Our findings indicated that filamin A (FLNA) and phosphoglycerate kinase 1 (PGK1) were two significantly differentially expressed proteins, with high expression in HCCLM9 cells, and may influence the metastasis of HCC cells. Conclusion: Taken together with the confirmation of expression on the mRNA level, we propose the use of FLNA and PGK1 as potential markers for the progression of HCC.

Both retrolinkin and its interaction partner endophilin A1 are required for BDNF-induced dendrite outgrowth of cultured hippocampal neurons. They function sequentially in an early endocytic trafficking pathway for BDNF-activated TrkB, which provides spatiotemporal control of downstream ERK signaling from endosomes.

Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.

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