BackgroundSRY (sex-determining region Y)-box 2 (SOX2) is a crucial transcription factor for the maintenance of embryonic stem cell pluripotency and the determination of cell fate. Previously, we demonstrated that SOX2 plays important roles in growth inhibition through cell cycle arrest and apoptosis, and that SOX2 expression is frequently down-regulated in gastric cancers. However, the mechanisms underlying loss of SOX2 expression and its target genes involved in gastric carcinogenesis remain largely unknown. Here, we assessed whether microRNAs (miRNAs) regulate SOX2 expression in gastric cancers. Furthermore, we attempted to find downstream target genes of SOX2 contributing to gastric carcinogenesis.Methodology/Principal FindingsWe performed in silico analysis and focused on miRNA-126 (miR-126) as a potential SOX2 regulator. Gain- and loss-of function experiments and luciferase assays revealed that miR-126 inhibited SOX2 expression by targeting two binding sites in the 3′-untranslated region (3′-UTR) of SOX2 mRNA in multiple cell lines. In addition, miR-126 was highly expressed in some cultured and primary gastric cancer cells with low SOX2 protein levels. Furthermore, exogenous miR-126 over-expression as well as siRNA-mediated knockdown of SOX2 significantly enhanced the anchorage-dependent and -independent growth of gastric cancer cell lines. We next performed microarray analysis after SOX2 over-expression in a gastric cancer cell line, and found that expression of the placenta-specific 1 (PLAC1) gene was significantly down-regulated by SOX2 over-expression. siRNA- and miR-126-mediated SOX2 knockdown experiments revealed that miR-126 positively regulated PLAC1 expression through suppression of SOX2 expression in gastric cancer cells.ConclusionsTaken together, our results indicate that miR-126 is a novel miRNA that targets SOX2, and PLAC1 may be a novel downstream target gene of SOX2 in gastric cancer cells. These findings suggest that aberrant over-expression of miR-126 and consequent SOX2 down-regulation may contribute to gastric carcinogenesis.
It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.
The neuropeptide precursor ProGRP is a distinct serum marker that is useful to know the NE milieu and provides prognostic information in patients with advanced prostate cancer. Standard therapy for metastatic prostate cancer may make progress when further studies will clarify the causative link between serum ProGRP level and androgen-independent disease progression.
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