<p>Supplementary Figure S2. The effects of metformin on prostate stroma cells. (A) The ratio of epithelial cells to stroma cells in the mice at week 12, 25 and 37. (B) WPMY-1 cells were seeded in 96-well plates with 0.5Ã-105 cells per well in growth media with or without metformin (20mM). Cell viabilities were estimated by CCK8 every other day. (C and D) WPMY-1 cells were treated with different concentrations of metformin and the numbers of cells at different stages of the cycle were analyzed by ï¬,ow cytometry (C), or stained with PI and FITC-labelled Annexin V and subsequently underwent ï¬,ow cytometry analysis to determine the percentage of apoptotic cells (D).</p>
A light source plays a pivotal role in a photofuel cell (PFC)-based self-powered biosensor. Although a visible light source has been extensively employed to drive a PFC, it still has some drawbacks for biosensing due to its relatively high energy. Herein we constructed a PFC-based aptasensor using near-infrared (NIR) light as the irradiation source. To achieve an efficient absorption of the NIR light, NaYF4:Yb,Er upconversion nanoparticles (UCNPs) that could convert low-energy incident light into high-energy radiation were combined with Bi2S3 nanorods (UCNPs/Bi2S3) to serve as the photoactive materials. The PFC was comprised of a UCNPs/Bi2S3 photoanode and a Pt cathode, which could generate electrical output under NIR light irradiation to provide the self-powered sensing signal without the supply from an external power source. The aflatoxin B1 (AFB1) binding aptamer was immobilized on the photoanode to serve as the recognition element. The detection of AFB1 was based on the competition between the interaction of aptamer with AFB1 analyte and the hybridization of aptamer with Au nanoparticles-labeled DNA sequence (AuNPs-cDNA). Under optimum conditions, the proposed aptasensor presented good sensitivity and high specificity for AFB1 detection in the concentration range from 0.01 to 100 ng·mL–1, with a detection limit of 7.9 pg·mL–1. Moreover, the developed sensor was applied to an assay of AFB1 in flour samples with a desirable accuracy and precision.
BACKGROUND: Increasing evidence reveals that aberrant microRNAs (miRNAs) expression play a crucial role in the tumorigenesis of cancers, including hepatocellular carcinoma (HCC), whereas the role of miR-654-3p in HCC remains unclear. This study aimed to investigate the role of miR-654-3p in HCC. ME THODS: Real-time quantitative PCR was performed to detect miR-654-3p expression in HCC tissues and cell lines. The association of miR-654-3p expression with clinical characteristics of HCC patients were analyzed. And the prognostic value of miR-654-3p was examined using Kaplan-Meier curve and Cox regression analysis. CCK-8 and Transwell assays were used to observe the effects of miR-654-3p on proliferation, migration, and invasion of HCC cells. RESULTS: The miR-654-3p expression was downregulated in both HCC tissues and cell lines, which was significantly associated with lymph node metastasis and TNM stage. Downregulation of miR-654-3p predicted poor prognosis of HCC patients. Overexpression of miR-654-3p inhibited HCC cell proliferation, migration, and invasion, while knockdown of miR-654-3p promoted these cellular behaviors in vitro. CONCLUSION: Our study suggested that miR-654-3p expression was downregulated in HCC and might serve as a potential prognostic marker and therapeutic target for the survival of HCC patients. miR-654-3p might exert a suppressor role in HCC through inhibiting tumor cell proliferation, migration, and invasion.
Abstract Background: While atypical expression of special AT-rich sequence-binding protein 2 (SATB2) has been approved associated with tumor progression, metastasis and unfavourable prognosis in various carcinomas. However, in oral squamous cell carcinoma (OSCC), both the expressive state and associated functions of SATB2’s are still undefined. Methods: Real-time PCR, western blotting, and immunohistochemistry were used to examine SATB2 expression. In vitro experiments including Flow Cytometry, CCK8 assay, migration assay, wound-healing assay were used to investigate the effects of SATB2 on HN4 cell proliferation, migration and invasion ability. Additionally, an orthotopic implantation assay was performed in nude mice to confirm the effects of SATB2 in vivo. Furthermore, a genome wide siRNA knockdown experiment was performed to explore the potential downstream regulatory mechanism of SATB2 in OSCC. Results: We found that , in clinical samples from a retrospective cohort of 58 OSCC patients, high expression of SATB2 is associated with poor prognosis of OSCC patients. In this study, we investigated SATB2 is highly expressed in OSCC tissues and cell lines ,which can promotes OSCC cells’ proliferation, migration, invasion and tumor growth. Following a genome wide siRNA knockdown experiment, we identified NOX4, a bona fide downstream target of SATB2, which can partially suppress OSCC proliferation. Furthermore, NOX4 knockdown inhibits tumorigenicity, which can be rescued partially by ectopic expression of SATB2. Conclusion: Our findings not only indicate overexpression of SATB2 triggers the proliferative, migratory and invasive mechanisms which are important in the malignant phenotype of OSCC, but also identify NOX4 as the downstream gene for SATB2. These findings indicate that SATB2 may play a key role in OSCC tumorigenicity and may be a future target for the development of new therapeutic regimens.
Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.
// Wuyuan Zhou 1 , Benkui Zou 2 , Lisheng Liu 3 , Kai Cui 1 , Jie Gao 1 , Shuanghu Yuan 4 , Ning Cong 5 1 Department of Hepatobillary Surgery, Shandong Cancer Hospital, Jinan, Shandong 250117, P.R. China 2 Department of Urology Surgery, Shandong Cancer Hospital, Jinan, Shandong 250117, P.R. China 3 Clinical Laboratory, Shandong Cancer Hospital, Jinan, Shandong 250117, P.R. China 4 Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong 250117, P.R. China 5 Department of Intervention Therapy, Shandong Cancer Hospital, Jinan, Shandong 250117, P.R. China Correspondence to: Ning Cong, email: doctorcongning@qq.com Keywords: hepatocellular carcinoma, microRNA-98, tumor suppressor, SALL4 Received: June 22, 2016 Accepted: August 16, 2016 Published: September 22, 2016 ABSTRACT MicroRNAs (miRs) are involved in the development and progression of hepatocellular carcinoma (HCC), but the regulatory mechanism of miR-98 in HCC still remains unclear. Here we found that miR-98 was significantly downregulated in HCC tissues compared to matched adjacent normal tissues (ANTs). Low miR-98 expression was associated with tumor size, metastasis, portal vein tumor embolus, and poor overall survival. Ectopic expression of miR-98 decreased the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) of HCC cells. SALL4 was identified as a novel target of miR-98, and the protein expression of SALL4 was inhibited by miR-98 in HCC cells. Overexpression of SALL4 reversed the suppressive effects of miR-98 on the malignant phenotypes of HCC cells. Besides, SALL4, upregulated in HCC tissues compared to the matched ANTs, was inversely correlated to the miR-98 levels in HCC tissues. In addition, overexpression of miR-98 markedly suppressed the tumor growth as well as tumor-induced death in nude mice. In summary, miR-98 plays a suppressive role in the proliferation, migration, invasion and EMT of HCC cells, partly at least, via directly inhibition of SALL4. Therefore, the miR-98/SALL4 axis may become a promising therapeutic target for HCC.
To study the degradation of lncRNAs in EPMI in rat brain tissue, this study provides a new direction for the estimation of EPMI. LncRNA high-throughput sequencing was performed on the brain tissues of hemorrhagic shock model rats at 0 h and 24 h, and the target lncRNAs were screened. Samples at 0, 1, 3, 6, 12, 18 and 24 h after death were collected, and miRNA-9 and miRNA-125b were used as reference genes. The relative expression levels of lncRNAs at each PMI were detected by RT–qPCR, and a functional model involving lncRNAs and EPMI was established. Samples were collected at 6, 9, 15, and 21 h after death for functional model verification. The expression of several lncRNAs decreased with the prolongation of EPMI, and the mathematical model established by several lncRNA indices exhibited good fit. The verification results of the multi-index joint function model are significantly better than those of the single-index function model, and the established model is more practical. There is a linear relationship between lncRNAs and EPMI, and the multi-index function model is significantly better than the single-index function model, which is important for EPMI inference in forensic pathology practice.
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