BackgroundWe conducted multiple microarray datasets analyses from clinical and xenograft tumor tissues to search for disease progression-driving oncogenes in prostate cancer (PCa). Sperm-associated antigen 5 (SPAG5) attracted our attention. SPAG5 was recently identified as an oncogene participating in lung cancer and cervical cancer progression. However, the roles of SPAG5 in PCa progression remain unknown.MethodsSPAG5 expression level in clinical primary PCa, metastatic PCa, castration resistant PCa, neuroendocrine PCa, and normal prostate tissues was investigated. We established multiple in vivo xenografts models using patient-derived tissues and investigated SPAG5 expression trend in these models. We also investigated the functions of SPAG5 in vivo and in vitro studies. Luciferase reporter assays were performed to investigate potential miRNAs that can regulate SPAG5.ResultsWe identified that SPAG5 expression was gradually increased in PCa progression and its level was significantly associated with lymph node metastasis, clinical stage, Gleason score, and biochemical recurrence. Our results indicated that SPAG5 knockdown can drastically inhibit PCa cell proliferation, migration, and invasion in vitro and supress tumor growth and metastasis in vivo. We identified that miR-539 can directly target SPAG5. Ectopic overexpression of miR-539 can drastically inhibit SPAG5 expression and the restoration of SPAG5 expression can reverse the inhibitory effects of miR-539 on PCa cell proliferation and metastasis.ConclusionOur results collectively showed a progression-driving role of SPAG5 in PCa which can be regulated by miR-539, suggesting that miR-539/SPAG5 can serve as a potential therapeutic target for PCa.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-016-0337-8) contains supplementary material, which is available to authorized users.

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BackgroundBladder transitional cell carcinoma greatly threatens human health all over the world. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) shows a strong apoptosis-inducing effect on a variety of cancer cells including bladder cancer. However, adenovirus-mediated TRAIL expression still showed cytotoxicity to normal cells mainly due to lack of tumor specificity.MethodsTo solve the problem, we applied miRNA response elements (MREs) of miR-1, miR-133 and miR-218 to confer TRAIL expression with specificity to bladder cancer cells.ResultsExpression of miR-1, miR-133 and miR-218 was greatly decreased in bladder cancer than normal bladder tissue. Luciferase assay showed that application of the 3 MREs was able to restrain exogenous gene expression to within bladder cancer cells. Subsequently, we constructed a recombinant adenovirus with TRAIL expression regulated by MREs of miR-1, miR-133 and miR-218, namely Ad-TRAIL-MRE-1-133-218. qPCR, immunoblotting and ELISA assays demonstrated that Ad-TRAIL-MRE-1-133-218 expressed in bladder cancer cells, rather than normal bladder cells. The differential TRAIL expression also led to selective apoptosis-inducing and growth-inhibiting effect of Ad-TRAIL-MRE-1-133-218 on bladder cancers. Finally, bladder cancer xenograft in mouse models further confirmed that Ad-TRAIL-MRE-1-133-218 effectively suppressed the growth of bladder cancers.ConclusionsCollectively, we demonstrated that MREs-based TRAIL delivery into bladder cancer cells was feasible and efficient for cancer gene therapy.

Background: Bladder cancer (BCa), among the world’s most common malignant tumors in the urinary system, has a high morbidity and mortality. Though cuproptosis is a new type of cell death mediated by lipoylated tricarboxylic acid (TCA) cycle proteins, the role of cuproptosis-related long noncoding RNAs (crlncRNAs) in bladder tumors awaits further elucidation. In this paper, we tried to explore how important crlncRNAs are for BCa.Methods: The crlncRNAs were first obtained through Pearson correlation analysis of the RNA-seq data and corresponding clinical data downloaded from The Cancer Genome Atlas (TCGA). Then, three lncRNAs were acquired by Cox regression and Lasso regression to build a prognostic model of crlncRNAs for verification. In the meantime, clinicopathological correlation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, principal component analysis (PCA), immunoassay, and half-maximal inhibitory concentration prediction (IC50) were carried out. Then, an entire tumor was classified into two clusters by crlncRNA expression to further discuss the differences in prognosis, immune status and drug susceptibility among different subgroups.Results: We obtained a total of 152 crlncRNAs and built a risk model for screened crlncRNAs. We validated the model and found that calibration charts feature a high consistency in verifying nomogram prediction. Receiver operating characteristic (ROC) curve and univariate and multivariate Cox regression suggested that this model can be applied as an independent prognostic factor of bladder cancer due to its high accuracy. According to KEGG analysis, high-risk groups were enriched in cancer and immune-related pathways. During tumor immunoassay, noticeable differences were observed in both immune infiltration and checkpoints between high- and low-risk patients. Of the two subgroups divided among patients by consensus clustering, cluster 2 had a better prognosis, whereas cluster 1 had higher immunoreactivity scores, more immune cell infiltrations and immune checkpoint expressions, and different sensitivities to drugs.Conclusion: The research findings demonstrate that crlncRNAs can be used to predict the prognosis and immune microenvironment of patients suffering from BCa, and differentiate between BCa subgroups to improve the individual therapy of BCa.

BackgroundAnoikis is a form of programmed cell death or programmed cell death(PCD) for short. Studies suggest that anoikis involves in the decisive steps of tumor progression and cancer cell metastasis and spread, but what part it plays in bladder cancer remains unclear. We sought to screen for anoikis-correlated long non-coding RNA (lncRNA) so that we can build a risk model to understand its ability to predict bladder cancer prognosis and the immune landscape.MethodsWe screened seven anoikis-related lncRNAs (arlncRNAs) from The Cancer Genome Atlas (TCGA) and designed a risk model. It was validated through ROC curves and clinicopathological correlation analysis, and demonstrated to be an independent factor of prognosis prediction by uni- and multi-COX regression. In the meantime, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, and half-maximal inhibitory concentration prediction (IC50) were implemented with the model. Moreover, we divided bladder cancer patients into three subtypes by consensus clustering analysis to further study the differences in prognosis, immune infiltration level, immune checkpoints, and drug susceptibility.ResultWe designed a risk model of seven arlncRNAs, and proved its accuracy using ROC curves. COX regression indicated that the model might be an independent prediction factor of bladder cancer prognosis. KEGG enrichment analysis showed it was enriched in tumors and immune-related pathways among the people at high risk. Immune correlation analysis and drug susceptibility results indicated that it had higher immune infiltration and might have a better immunotherapy efficacy for high-risk groups. Of the three subtypes classified by consensus clustering analysis, cluster 3 revealed a positive prognosis, and cluster 2 showed the highest level of immune infiltration and was sensitive to most chemistries. This is helpful for us to discover more precise immunotherapy for bladder cancer patients.ConclusionIn a nutshell, we found seven arlncRNAs and built a risk model that can identify different bladder cancer subtypes and predict the prognosis of bladder cancer patients. Immune-related and drug sensitivity researches demonstrate it can provide individual therapeutic schedule with greater precision for bladder cancer patients.

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