CircANXA4 (hsa_circ_0055087) regulates the miR-1256/PRM1 axis to promote tumor progression in colorectal cancer.

Colorectal cancer (CRC) incidence ranks third among malignant cancers with a high propensity for distant metastasis. Despite continuous efforts to improve treatment, the prognosis especially in patients with advanced distant metastasis is low. The mechanism of development and progression of CRC is not fully understood. Non-coding RNAs (ncRNAs) have emerged as essential regulators in cancer progression. Here, we aim to dissect the role of one critical ncRNA, circANXA4, in CRC progression. CircANXA4 expression was analyzed by the GEO database. Differentially expressed circRNAs were identified by the Limma package R software. Expression of circANXA4 and miR-1256 was detected by qRT-PCR. The regulation of circANXA4 on cell proliferation and progression was confirmed with the cell viability assay using cell counting kit-8 (CCK-8) and transwell migration assay. RNA pull-down assay, RNA immunoprecipitation (RIP), and western blot were used to determine the interaction between circANXA4, miR-1256, and protamine1 (PRM1). CircANXA4 was upregulated in both CRC tissues and cell lines. Knockdown of circANXA4 effectively reduced cell proliferation, progression, and migration. Additionally, silencing circANXA4 remarkably increased miR-1256 expression, while reducing PRM1 expression, thereby demonstrating that circANXA4 downregulates miR-1256 expression through a complementary binding site. Rescue experiments revealed the interactions between circANXA4, miR-1256, and PRM1. Pearson correlation analysis revealed that circANXA4 expression positively correlated with PRM1 expression and miR-1256 expression inversely correlated with PRM1 expression. In sum, we demonstrated that circANXA4 promotes cancer cell proliferation and progression by sponging miR-1256 and upregulating PRM1 in CRC.

A risk model to predict the mental health of older people in Chinese communities based on machine learning.

Background: Anxiety, depression, and dementia are important issues affecting the mental health of the older population. Given the relationship between mental health and physical disorders, it is particularly important to diagnose and identify these psychological problems in older people. Methods: Psychological data of 15,173 older people living in various districts and counties of Shanxi province, China, were extracted from data collected through the '13th Five-Year Plan for Healthy Aging-Psychological Care for the Elderly Project' of the National Health Commission of China in 2019. Three different ensemble learning classifiers [random forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)] were evaluated, and the best classifier with the selected feature set was selected. The ratio of training to testing cases was 8:2. The predictive performance of the three classifiers was evaluated by calculating the area under the receiver operating characteristic curve (AUC), accuracy, recall, and F measurement based on 10-fold cross-validation and ranked by AUC. Results: All the three classifiers have achieved good prediction results. In the test set, the AUC value range for the three classifiers was 0.79 to 0.85. The LightGBM algorithm showed higher accuracy than both the baseline and XGBoost. A new machine learning (ML)-based model to predict mental health problems in older people was constructed. The model was interpretative and could hierarchically predict psychological problems including anxiety, depression, and dementia in older people. Experimental results showed that the method could accurately identify those suffering from anxiety, depression, and dementia in different age groups. Conclusions: A simple method model was designed based on only eight problems, which had good accuracy and was widely applicable to the older of all ages. Overall, this research approach avoided the need to identify older people with poor mental health through the traditional standardized questionnaire approach.

Up-regulation of lncRNA NEAT1 in cerebral ischemic stroke promotes activation of astrocytes by modulation of miR-488-3p/RAC1.

We aim to research the molecular mechanism of lncRNA NEAT1 in the activation of astrocytes in a cerebral ischemia-reperfusion injury model. Mouse model of cerebral ischemia-reperfusion injury was constructed, and shNEAT1 was transfected. The infarct area, brain water content, and neurological deficiency were detected. Immunofluorescence detection and fluorescence in situ hybridization (FISH) assay were processed to detect glial fibrillary acidic protein (GFAP) expression. Astrocyte cells were cultured for oxygen-glucose deprivation/re-oxygenation (OGD)/re-oxygenation model construction. After treatment by shNEAT1, miR-488-3p mimic, miR-488-3p inhibitor, Q-PCR assay, western blot and ELISA were undertaken to detect the expressions of NEAT1, miR-488-3p, RAC1, inflammatory cytokines, RAC1 and GFAP. Dual luciferase reporter assay and RNA-binding protein immunoprecipitation (RIP) assay were used to verify the binding of NEAT1, miR-488-3p and RAC1. The expression of NEAT1 in brain tissue was significantly higher than that in Sham operation group. Knockdown of NEAT1 inhibited the brain damage caused by middle cerebral artery occlusion (MCAO) treatment, reduced the inflammatory response, and suppressed the activation of astrocytes. By constructing an in vitro OGD/R cell model, it was found that NEAT1 knockdown also inhibited the activation of astrocytes caused by OGD/R. Knockdown of NEAT1 caused the up-regulation of miR-488-3p and the down-regulation of RAC1. Knockdown of miR-488-3p or over-expression of RAC1 reversed the inhibitory effect of shNEAT1 on OGD/R-induced astrocyte activation. Over-expression of NEAT1 in cerebral ischemic stroke promotes activation of astrocytes by modulation miR-488-3p/RAC1, which is proved in vitro. Our study may provide a new idea for the diagnosis and treatment of MCAO.

miR­491­3p functions as a tumor suppressor in non­small cell lung cancer by targeting fibroblast growth factor 5.

The present study aimed to identify the function of miR­491­3p in regulating non­small cell lung cancer (NSCLC). Tumor tissues and adjacent normal tissues were collected from 43 patients with NSCLC. A549 and H1299 cells were transfected with microRNA (miR)­491­3p mimic, mimic negative control (NC), miR­491­3p inhibitor, inhibitor NC, pcDNA3.1­FGF5 vector and control vector. Cell counting kit­8 assay and Edu experiments were performed to assess cell viability and proliferation. Matrigel experiment, wound healing assay and flow cytometric analysis were performed to explore cell invasion, migration and apoptosis, respectively. A dual­luciferase reporter experiment was performed to identify the relationship between miR­491­3p and fibroblast growth factor 5 (FGF5). In vivo study was conducted by using nude mice. The miR­491­3p and FGF5 protein expression levels were investigated using reverse transcription­quantitative polymerase chain reaction and western blot analysis. In NSCLC tumor tissues, miR­491­3p was downregulated and FGF5 was upregulated (P<0.01). Low miR­491­3p expression and high FGF5 mRNA expression was associated with poor outcomes in patients, including advanced TNM stage and lymph node metastasis (P<0.05). upregulation of miR­491­3p suppressed viability, proliferation, invasion and migration of NSCLC cells; however, it promoted apoptosis (P<0.01). FGF5 was a target gene for miR­491­3p. miR­491­3p directly inhibited FGF5 expression. upregulation of FGF5 significantly reversed the inhibitory effects of miR­491­3p on malignant phenotypes of NSCLC cells (P<0.01). miR­491­3p overexpression suppressed the in vivo growth of NSCLC. Thus, it was identified that miR­491­3p functions as a tumor suppressor in NSCLC by directly targeting FGF5.