Identification and functional activity of Nik related kinase (NRK) in benign hyperplastic prostate.

OBJECTIVE: Benign prostatic hyperplasia (BPH) is common in elder men. The current study aims to identify differentially expressed genes (DEGs) in hyperplastic prostate and to explore the role of Nik related kinase (NRK) in BPH. METHODS: Four datasets including three bulk and one single cell RNA-seq (scRNA-seq) were obtained to perform integrated bioinformatics. Cell clusters and specific metabolism pathways were analyzed. The localization, expression and functional activity of NRK was investigated via RT-PCR, western-blot, immunohistochemical staining, flow cytometry, wound healing assay, transwell assay and CCK-8 assay. RESULTS: A total of 17 DEGs were identified by merging three bulk RNA-seq datasets. The findings of integrated single-cell analysis showed that NRK remarkably upregulated in fibroblasts and SM cells of hyperplasia prostate. Meanwhile, NRK was upregulated in BPH samples and localized almost in stroma. The expression level of NRK was significantly correlated with IPSS and Qmax of BPH patients. Silencing of NRK inhibited stromal cell proliferation, migration, fibrosis and EMT process, promoted apoptosis and induced cell cycle arrest, while overexpression of NRK in prostate epithelial cells showed opposite results. Meanwhile, induced fibrosis and EMT process were rescued by knockdown of NRK. Furthermore, expression level of NRK was positively correlated with that of α-SMA, collagen-I and N-cadherin, negatively correlated with that of E-cadherin. CONCLUSION: Our novel data identified NRK was upregulated in hyperplastic prostate and associated with prostatic stromal cell proliferation, apoptosis, cell cycle, migration, fibrosis and EMT process. NRK may play important roles in the development of BPH and may be a promising therapeutic target for BPH/LUTS.

Nomogram for predicting occurrence and prognosis of liver metastasis in colorectal cancer: a population-based study.

PURPOSE: This study aimed to investigate the prevalence, risk, and prognostic factors for synchronous liver metastasis (LM) in colorectal cancer (CRC) and to construct nomogram for predicting occurrence and prognosis of synchronous LM. METHODS: A total of 203,998 CRC patients who were registered in the SEER database between 2010 and 2016 were included. Logistic regression was used to analyze risk factors and Kaplan-Meier was used to estimate the overall survival of CRC patients with LM. Potential prognostic factors were identified by multivariable Cox regression. For predicting the risk for development and prognosis in CRC patients with LM, we constructed nomogram and the predictive performance was estimated by the receiver operating characteristics cure, the concordance index, and calibration curve. RESULTS: In total, 15.3% of the CRC patients (N = 31,288) had synchronous LM. Male gender, black, uninsured status, left colon, T4/T1, and bone and lung metastases were positively associated with synchronous LM risk. The 1-year, 3-year, and 5-year overall survival rate was 49.1%, 18.4%, and 9.2%, respectively. Older age, male gender, black, uninsured status, poor histological differentiation, lymphatic metastasis, T4/T1, positive carcinoembryonic antigen, and lung, bone, and brain metastases were associated with the overall survival. Nomogram was constructed to predict the development and prognosis of synchronous LM and both of them were proved to have good calibration and discrimination. CONCLUSION: LM is highly prevalent in CRC patients. Nomogram basing on the risk and prognostic factors for synchronous LM was proved to have good performance for predicting the probability of LM occurrence and prognosis.

[Knockdown of Fez family zinc finger protein 1 antisense ribonucleic acid 1 (FEZF1-AS1) inhibits invasion and migration of esophageal squamous cell carcinoma cells by blocking JAK2/STAT3 pathway].

Objective To investigate the relationship between long chain non-coding RNA Fez family zinc finger protein 1 antisense ribonucleic acid 1 (FEZF1-AS1) and invasion, migration and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signal pathway in esophageal squamous cell carcinoma (ESCC) cells. Methods The expression' of FEZF1-AS1 in ESCC cancer tissue and ESCC cell line was detected by real-time quantitative PCR. After knocking down the FEZF1-AS1 of KYSE150 and KYSE510 cells by the constructed lentiviral expression vector, the changes of cell cycle were detected by flow cytometry, the ability of cell proliferation by colony formation assay, the ability of cell invasion by TranswellTM invasion assay, the ability of cell migration by TranswellTM migration and scratch assay, and the protein levels of JAK2 and phosphorylated STAT3 (p-STAT3) by Western blotting. Results FEZF1-AS1 was highly expressed in ESCC cancer tissues, and FEZF1-AS1 expression in KYSE150 and KYSE510 cells were higher than that in normal esophageal squamous epithelial cells. After the knockdown of FEZF1-AS1 expression, the cell cycle and proliferation of ESCC cancer cells had no obvious change, but cell invasion and migration were significantly inhibited, and the level of JAK2 protein decreased and p-STAT3 protein increased. Conclusion The knockdown of FEZF1-AS1 blocks JAK2/STAT3 pathway and inhibits the invasion and migration of ESCC cells.

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors.

Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.