Background: Cervical cancer (CC) is a common cancer with a poor prognosis due to the chemoresistance of CC cells to cisplatin. This study aimed to investigate the biological significance of lncRNA prostate cancer-associated transcript 6 (PCAT6) in the carcinogenesis of CC. Materials and Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) was carried out to measure the abundance of PCAT6, miR-543 and zinc finger E-box binding protein 1 (ZEB1) in CC tissues and cells. The combination between miR-543 and lncRNA PCAT6 or ZEB1 was predicted by Starbase and was verified by dual-luciferase reporter assay, RNA-pull down assay and RNA immunoprecipitation (RIP) assay. Cell proliferation and chemoresistance to cisplatin were detected by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell apoptosis and metastasis were determined by flow cytometry, Western blot and transwell migration and invasion assays. Results: The abundance of ZEB1 protein was measured by Western blot assay. Murine xenograft model was established to confirm the function of lncRNA PCAT6 in vivo. The abundance of lncRNA PCAT6 was enhanced in CC tissues and cells compared with that in corresponding normal tissues and normal cervical epithelial cells Ect1/E6E7. MiR-543 was a target of PCAT6 and was negatively regulated by PCAT6. PCAT6 accelerated the proliferation, metastasis and the chemoresistance of CC cells to cisplatin while suppressed the apoptosis of CC cells. The overexpression of PCAT6 reversed the inhibitory effects of miR-543 accumulation on the proliferation, metastasis and chemoresistance of CC cells to cisplatin and the promoting impact on the apoptosis of CC cells. ZEB1 was a direct target of miR-543, and it functioned as the downstream gene of PCAT6/miR-543 to exert its oncogenic role in CC. PCAT6 promoted the growth of murine xenograft tumor through miR-543/ZEB1 axis in vivo. Conclusion: LncRNA PCAT6 facilitated the proliferation, metastasis and chemoresistance of CC cells to cisplatin while impeded the apoptosis of CC cells via PCAT6/miR-543/ZEB1 axis. PCAT6/miR-543/ZEB1 axis might be a promising target for CC therapy.

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We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.

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