Anti-proliferative effects of paroxetine alone or in combination with sorafenib in HepG2 cells

Abstract Hepatocellular carcinoma (HCC) is a common cause of cancer-related death. Sorafenib is the first approved drug for the treatment of advanced HCC. Depression is frequent in cancer patients. Moreover, sorafenib might exert depression as an adverse drug reaction and paroxetine, a selective serotonin reuptake inhibitor, is a recommended pharmacotherapy. This study aimed to investigate the potential synergistic effects of paroxetine and sorafenib on HepG2 cell proliferation and death. Paroxetine and sorafenib were administered to HepG2 cells as single-agents or in combination. Cell viability was determined with XTT cell viability assay. Cellular apoptosis and DNA content were assessed by flow cytometry. The expression of anti-apoptotic Bcl-2 was examined by immunofluorescence confocal microscopy. A lower dose of sorafenib was found to be required to inhibit cell proliferation when in combination with paroxetine. Similarly, the coadministration enhanced cellular apoptosis and resulted in cell cycle arrest. Confocal imaging revealed a remarkably lower cell density and increased expression of Bcl-2 following combined treatment of paroxetine with sorafenib. To our knowledge, this is the first study demonstrating the synergistic effect of paroxetine and sorafenib in HCC and might provide a potentially promising therapeutic strategy.

HepG2.2.15 as a model for studying cell protrusion and migration regulated by S100 proteins.

Much of the difficulty in elucidating the precise function of S100 protein family has been attributed to functional redundancy and compensation by its conserved family members. In this study, we showed that seven S100 family members were almost totally undetectable in HepG2.2.15 cells, while all of them were highly expressed in its parental HepG2 cells. Re-expression of S100 proteins in HepG2.2.15 cells can partially rescue their defects in cell protrusion and migration through the regulation of cytoskeletons and adhesions. Thus, HepG2.2.15 can serve as a useful model for studying cell protrusion and migration regulated by S100 proteins.

Benchmarking HEp-2 cells classification methods.

In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.