Evaluating proxies for motion sickness in rodent.

Motions sickness (MS) occurs when the brain receives conflicting sensory signals from vestibular, visual and proprioceptive systems about a person's ongoing position and/or motion in relation to space. MS is typified by symptoms such as nausea and emesis and implicates complex physiological aspects of sensations and sensorimotor reflexes. Use of animal models has been integral to unraveling the physiological causality of MS. The commonly used rodents (rat and mouse), albeit lacking vomiting reflex, reliably display phenotypic behaviors of pica (eating of non-nutritive substance) and conditioned taste aversion (CTAver) or avoidance (CTAvoi) which utilize neural substrates with pathways that cause gastrointestinal malaise akin to nausea/emesis. As such, rodent pica and CTAver/CTAvoi have been widely used as proxies for nausea/emesis in studies dealing with neural mechanisms of nausea/emesis and MS, as well as for evaluating therapeutics. This review presents the rationale and experimental evidence that support the use of pica and CTAver/CTAvoi as indices for nausea and emesis. Key experimental steps and cautions required when using rodent MS models are also discussed. Finally, future directions are suggested for studying MS with rodent pica and CTAver/CTAvoi models.

Osteopontin improves sensitivity to tyrosine kinase inhibitor in lung adenocarcinoma in vitro by promoting epidermal growth factor receptor phosphorylation.

PURPOSE: Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) improve the prognosis of lung adenocarcinoma (LUAD). However, the factors affecting its clinical efficacy remain unclear. This study aimed to determine the correlation between Osteopontin (OPN) and EGFR, and explore the inhibitory effect of first-generation TKI gefitinib on LUAD cells. METHODS: The correlation between OPN and EGFR was determined through bioinformatics technology, and the clinical information as well as samples of related patients were collected to verify the relationship between them. Using three different NSCLC cell lines A549, H1299 and PC9, we studied the effects of OPN expression and EGFR phosphorylation on the first-generation TKI's efficacy in vitro. RESULTS: Our data revealed that OPN staining positively linked to a more advanced clinical stage. Compared with the control group, LUAD cells with elevated OPN levels are more sensitive to the growth inhibitory effect of TKI. Knocking down of OPN decreased the response of cells to gefitinib. Besides, OPN also upregulated the phosphorylation of EGFR, thereby affecting the effect of TKI. CONCLUSION: OPN enhanced the sensitivity of LUAD cells to gefitinib by promoting EGFR phosphorylation. OPN may be a potential target for evaluating TKI efficacy and a potential target for molecular therapy.

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning.

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = -0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportional hazard model. Prognostic nomogram was performed and its predictive performance was validated via the modeling and validating cohort data. Additionally, propensity score matching (PSM) was also applied to evaluate whether surgery affected the OS of this study population. 5411 eligible DPLC patients were included in this study cohort, with 41.0% of 3-year OS rate and 27.7% of 5-year OS rate. Age, sex, race, grade, stage, lymph node (LN) metastasis, histological type, primary site, and surgery were considered to be prognostic factors of OS. The C-indexes of the established nomogram were 0.70 (95% CI (0.69, 0.71)) in the modeling group and 0.70 (95% CI (0.68, 0.72)) in the validation group, which showed an ideal model discrimination ability. AUC and calibration plots of 3- and 5-year OS also proved the good performance of the established nomogram. After 1 : 1 PSM, surgery can potentially reduce the risk of OS (HR = 0.63, 95% CI: 0.56-0.72) of DPLC. The prognostic nomogram with reliable performance was developed to predict 3- and 5-year OS rates, which could assist clinicians to make more reasonable survival prediction for DPLC patients. For patients without absolute surgical contraindications, surgery should be actively considered.

Identification of Hub Genes Associated With Progression and Prognosis in Patients With Bladder Cancer.

Given that most bladder cancers (BCs) are diagnosed in advanced stages with poor prognosis, this study aims to find novel biomarkers associated with the progression and prognosis in patients with BC. 1,779 differentially expressed genes (DEGs) between BC samples and normal bladder tissues were identified in total. Then, 24 DEGs were regarded as candidate hub genes by constructing a protein-protein interaction (PPI) network and a random forest model. Among them, six genes (BUB1B, CCNB1, CDK1, ISG15, KIF15, and RAD54L) were eventually identified by using five analysis methods (one-way Analysis of Variance analysis, spearman correlation analysis, distance correlation analysis, receiver operating characteristic curve, and expression values comparison), which were correlated with the progression and prognosis of BC. Moreover, the validation of hub genes was conducted based on GSE13507, Oncomine, and CBioPortal. Results of univariate Cox regression analysis showed that the expression levels of all the hub genes were influence features of overall survival (OS) and cancer specific survival (CSS) based on GSE13507, and we further established a six-gene signature based on the expression levels of the six genes and their Cox regression coefficients. This signature showed good potential for clinical application suggested by survival analysis (OS: Hazard Ratio = 0.484, 95%CI: 0.298-0.786; P = 0.0034; CSS: Hazard Ratio = 0.244, 95%CI: 0.121-0.493, P < 0.0001) and decision curve analysis. In conclusion, our study indicates that six hub genes have great predictive value for the prognosis and progression of BC and may contribute to the exploration of further basic and clinical research of BC.

Four novel biomarkers for bladder cancer identified by weighted gene coexpression network analysis.

Bladder cancer (BC) is one of the most malignancies in terms of incidence and recurrence worldwide. The aim of this study is to find out novel and prognostic biomarkers for patients with BC. First, we identified 258 differentially expressed genes by using GSE19915 from Gene Expression Omnibus database. Second, a total of 33 modules were identified by constructing a coexpression network by using weighted gene coexpression network analysis and yellow module was regarded as the key module. Furthermore, by constructing protein-protein interaction networks, we preliminarily picked out 13 genes. Among them, four hub genes (CCNB1, KIF4A, TPX2, and TRIP13) were eventually identified by using five different methods (survival analysis, one-way analysis of variance, the Spearman correlation analysis, receiver operating characteristic curve, and expression value comparison), which were significantly correlated with the prognosis of BC. The validation of transcriptional and translational levels made sense (based on Oncomine and The Human Protein Atlas database). Moreover, functional enrichment analysis suggested that all the hub genes played crucial roles in chromosome segregation, sister chromatid segregation, nuclear chromosome segregation, mitotic nuclear division, nuclear division, and organelle fission during cell mitosis. In addition, three of the hub genes (KIF4A, TPX2, and TRIP13) might be potential targets of cancer drugs according to the results of the genetical alteration. In conclusion, this study indicates that four hub genes have great predictive value for the prognosis of BC, and may contribute to the exploration of the further and more in-depth research of BC.