The effect of competing endogenous RNA (ceRNA) can regulate gene expression by competitively binding microRNAs. Fascin-1 (FSCN1) plays an important role in the regulation of cellular migration and invasion during tumor progression, but how its regulatory mechanism works through the ceRNA effect is still unclear in bladder cancer (BLCA). The role of fascin-1, miR-200b, and ZEB1-AS1 in BLCA was investigated in vitro and in vivo. The interaction between fascin-1, miR-200b, and ZEB1-AS1 was identified using bioinformatics analysis, luciferase activity assays, RNA-binding protein immunoprecipitation (RIP), quantitative PCR, and western blotting. Loss (or gain)-of-function experiments were performed to investigate the biological roles of miR-200b and ZEB1-AS1 on migration, invasion, proliferation, cell apoptosis, and cell cycle. ZEB1-AS1 functions as a competing endogenous RNA in BLCA to regulate the expression of fascin-1 through miR-200b. Moreover, the oncogenic long non-coding RNA ZEB1-AS1 was highly expressed in BLCA and positively correlated with high tumor grade, high TNM stage, and reduced survival of patients with BLCA. Moreover, ZEB1-AS1 downregulated the expression of miR-200b, promoted migration, invasion, and proliferation, and inhibited apoptosis in BLCA. Furthermore, we found TGF-β1 induced migration and invasion in BLCA by regulating the ZEB1-AS1/miR-200b/FSCN1 axis. The observations in this study identify an important regulatory mechanism of fascin-1 in BLCA, and the TGF-β1/ZEB1-AS1/miR-200b/FSCN1 axis may serve as a potential target for cancer therapeutic purposes.

The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear.Methods: Variation analysis ofGSE38241,GSE69223,GSE46602andGSE104749were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified inGSE21032and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer.Result: Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database andGSE21032.Conclusion: These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.

Background. Bladder cancer is the tenth most common cancer worldwide. Valuable biomarkers in the field of diagnostic bladder cancer are urgently required. Method. Here, the gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA), GSE13507, GSE32894, and Mariathasan et al. Five prognostic genes were identified by the univariate, robust, and multivariate Coxs regression and were used to develop a prognosis-related model. The KaplanMeier survival curves and receiver operating characteristics were used to evaluate the models effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. Subsequently, using the bladder cancer (BLCA) cell lines, the role of TNFRSF14 was determined by Western blotting, cell proliferation assay, and 5-ethynyl-20-deoxyuridine assay. Results. GSDMB, CLEC2D, APOL2, TNFRSF14, and GBP2 were selected as prognostic genes in bladder cancer patients. The models irreplaceable reliability was validated by the training and validation cohorts. CD8+ T cells were highly infiltrated in the high-TNFRSF14-expression group, and M2 macrophages were the opposite. Higher expression of TNFRSF14 was associated with higher expression levels of LCK, interferon, MHC-I, and MHC-II, while risk score was the opposite. Many compounds with higher sensitivity for treating bladder cancer patients in the low-TNFRSF14-expression group were identified, with obatoclax being a potential drug most likely to treat patients in the low-TNFRSF14-expression group. Finally, the proliferation of BLCA cell lines was increased in the TNFRSF14-reduced group, and the differential expression was identified. TNFRSF14 plays a role in bladder cancer progression through the Wnt/β-catenin-dependent pathway. TNFRSF14 is a potential protective biomarker involved in cell proliferation in BLCA. Conclusion. We conducted a study to establish a 5-gene score model, providing reliable prediction for the outcome of bladder cancer patients and therapeutic drugs to individualize therapy. Our findings provide a signature that might help determine the optimal treatment for individual patients with bladder cancer.

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem. In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis. Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients. In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results. FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC=0.6039) and GSE21032 GPL5188 (140 samples; AUC=0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion. A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

Background: The tachykinin receptor 2 (TACR2) is encoded by the tachykinin receptor correlation gene. Recent microarray analysis for prostate cancer suggests that TACR2 expression is associated with clinical phenotype and disease-free survival among patients with prostate cancer. Results: TACR2 protein levels were lower in prostate cancer tissues than in adjacent normal prostate tissue. TACR2 expression significantly correlated with clinical stage, Gleason scores, and survival outcomes. TACR2 expression positively correlated with mast cells and negatively correlated with M2 macrophages. Overexpression of TACR2 promoted the migration and proliferation of prostate cancer cells by regulating the Wnt signaling pathway. Conclusions: The TACR2-Wnt/β-catenin signaling pathway is critical in prostate cancer. TACR2 may affect tumor cells’ occurrence and development by changing the content of immune cells in the tumor microenvironment. These findings suggest that TACR2 may be a candidate molecular biomarker for prostate cancer therapy.