scholarly journals Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

2020 ◽  
Vol 17 (12) ◽  
pp. 4298 ◽  
Author(s):  
Shan-Han Huang ◽  
Ying-Chi Lin ◽  
Chun-Wei Tung
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
External Validation ◽  
Enrichment Analysis ◽  
Machine Learning Techniques ◽  
Early Assessment ◽  
Short Term ◽  
Short Term Exposure ◽  
Time Invariant

Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

scholarly journals Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shinje Moon ◽  
Ji-Yong Jang ◽  
Yumin Kim ◽  
Chang-Myung Oh
Keyword(s):  
Large Scale ◽  
Cox Regression ◽  
Characteristic Curve ◽  
External Validation ◽  
High Sensitivity ◽  
Population Based ◽  
Risk Classification ◽  
Machine Learning Techniques ◽  
Internal Validation ◽  
Cox Regression Analysis

AbstractIn this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013–16 Korea National Health and Nutrition Examination Survey (KNHANES), the 2017–18 KNHANES, and the Korean Genome and Epidemiology Study (KoGES), as the derivation, internal validation, and external validation sets, respectively. We constructed a new diabetes index using logistic regression (LR) and calculated the probability of diabetes in the validation sets. We used the area under the receiver operating characteristic curve (AUROC) and Cox regression analysis to measure the performance of the internal and external validation sets, respectively. We constructed a gender-specific diabetes prediction model, having a resultant AUROC of 0.93 and 0.94 for men and women, respectively. Based on this probability, we classified participants into five groups and analyzed cumulative incidence from the KoGES dataset. Group 5 demonstrated significantly worse outcomes than those in other groups. Our novel model for predicting diabetes, based on two large-scale population-based cohort studies, showed high sensitivity and selectivity. Therefore, our diabetes index can be used to classify individuals at high risk of diabetes.

scholarly journals Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yong Liu ◽  
Hui Liu ◽  
Queqiao Bian
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Characteristic Curve ◽  
External Validation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Microarray Datasets

Purpose. This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC). Methods. The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs. Result. In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals. Conclusion. Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.

scholarly journals An In Silico Model for Predicting Drug-Induced Hepatotoxicity

2019 ◽  
Vol 20 (8) ◽  
pp. 1897 ◽  
Author(s):  
Shuaibing He ◽  
Tianyuan Ye ◽  
Ruiying Wang ◽  
Chenyang Zhang ◽  
Xuelian Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Drug Induced ◽  
Drug Candidates ◽  
External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

A new method for early assessment of effects of exposing two non-target crustacean species, Asellus aquaticus and Gammarus fossarum, to pesticides, a laboratory study

2010 ◽  
Vol 26 (4) ◽  
pp. 217-228 ◽  
Author(s):  
Simon Lukančič ◽  
Uroš Žibrat ◽  
Tadej Mezek ◽  
Andreja Jerebic ◽  
Tatjana Simčič ◽  
...  
Keyword(s):  
Stress Factors ◽  
Toxicity Tests ◽  
Asellus Aquaticus ◽  
Early Assessment ◽  
Short Term ◽  
Aquatic Animals ◽  
Test Species ◽  
Gammarus Fossarum ◽  
Ets Activity ◽  
Short Term Exposure

A reliable method is needed for assessing the condition of aquatic animals and their resistance to toxic pollutants. The physiological responses of two freshwater crustaceans, Asellus aquaticus and Gammarus fossarum, following in vitro exposure to two pesticides (atrazine and imidacloprid), were measured by a combination of electron transport system (ETS) activity and respiration (R). Short-term exposure concentrations were selected according to standard toxicity tests and ranged from 0.01 mg L—1 to 10 mg L—1. When pesticide concentration was greater than 1 mg l— 1 (which is below the LC50 [48 hours] determined for both species), A. aquaticus and G. fossarum responded to short-term exposure with elevated levels of R and/or lower levels of ETS activity. One hour exposure to concentrations of up to 10 mg L—1 showed an effect in both test species. Laboratory tests confirmed that G. fossarum is more sensitive to short-term pesticide exposure than A. aquaticus. The combination of these two methods provides a useful and effective tool for assessing the general condition of aquatic animals. It also enables to determine toxic effects on freshwater biota of specific or combined pollutants. ETS/R ratio may be used as a quick predictor of effects on organisms exposed to pesticides and other stress factors such as changes in temperature, light, salinity, oxygen concentration and food.

scholarly journals Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Si Cheng ◽  
Zhe Li ◽  
Wenhao Zhang ◽  
Zhiqiang Sun ◽  
Zhigang Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metastatic Melanoma ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

scholarly journals Retinal Microvascular Response to Short-Term Exposure to Particulate Matters As an Indicator of Cardiovascular Effects in Work Environments

2021 ◽  
Author(s):  
Fatemeh Aminaei ◽  
Mohammad Javad Zare Sakhvidi ◽  
Hamideh Mihanpour ◽  
Mojtaba Moghaddasi ◽  
Mahdiyeh Shafieizadeh Bafghi
Keyword(s):  
Large Scale ◽  
Cardiovascular Effects ◽  
Particulate Matters ◽  
Short Term ◽  
Cardiovascular Parameters ◽  
Short Term Exposure ◽  
Significant Difference ◽  
Inhalable Dust ◽  
Kolmogorov Smirnov ◽  
Retinal Artery

Introduction: According to the literature, Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (CRVE), and Artemio Venule Ratio (AVR) are three important markers in determining the changes of retinal artery. These markers are employed as an indicator of cardiovascular effects in workers. This study aimed to evaluate the changes in retinal micro vascular responses in workers exposed to short-term exposure to particulate matters caused by occupational processes using CRAE, CRVE, and AVR markers. Materials and Methods: In this study, 96 workers exposed to job-related pollution in a tile and ceramic company were investigated. Fundus photos of these people were analyzed via ARIA software using the related protocols. The data were investigated employing Kolmogorov–Smirnov and Mann-Whitney tests as well as the correlation and regression tests. Results: No significant difference was observed in the ocular parameters of smokers and non-smokers. Cardiovascular parameters had no significant correlation with CRAE and CRVE as well as breathable and inhalable dust; however, these parameters had a direct and significant correlation with AVR, PM10, and PM2.5 particles. Conclusion: The results did not show any changes in the retinal artery in workers who were exposed to job-related pollution. Moreover, no significant relationship was found between retinal artery changes and cardiovascular parameters. However, more large-scale studies are needed to clarify the relationship of particulate air pollutants with CRAE, CRVE, and AVR changes.

scholarly journals Application of time to emesis data for primary triage of radiation accident victims

2021 ◽  
pp. 14-21
Author(s):  
V. Yu. Soloviev ◽  
A. S. Samoilov ◽  
A. O. Lebedev ◽  
M. K. Sedankin ◽  
E. A. Gudkov
Keyword(s):  
Large Scale ◽  
Radiation Injury ◽  
Prolonged Exposure ◽  
Dose Effect ◽  
Separate Analysis ◽  
Short Term ◽  
Radiation Accidents ◽  
Short Term Exposure ◽  
Severity Of Injury ◽  
Primary Triage

Relevance. The relevance of the study is due to the risk of developing large-scale radiation accidents with a large number of victims, who will need primary medical triage and early prediction of the severity of injury for correct routing from the source of sanitary losses.Intention. Validation of the method of estimating dose by time-to-emesis under various exposure conditions for pre-hospital triage of victims.Methodology. The object of the study is the data from State Research Center – Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency (Moscow) database of acute radiation injuries.Results and Discussion: We have analyzed individual data for the victims of the 1986 Chernobyl disaster (114 persons) with a separate analysis of the irradiation conditions (short-term exposure for less than 20 minutes and prolonged exposure in selected groups), as well as the data from victims of other radiation accidents in the former USSR (26 persons) and 8 patients with total body radiotherapy without the use of antiemetics. It was shown that for the equal time-to-emesis intervals, predicted radiation injury is more severe in case of prolonged exposure vs short-term exposure. This may be due to varying rate of dose accumulation and so-called “unnecessary dose” effect – when the biological mechanism of vomiting has already been triggered against ongoing exposure. Results are presented as interval estimates of radiation injury severity by time-to-emesis for both short-term and prolonged exposures. We also have formulated two criteria for primary triage purposes in case of moderate or large numbers of victims due to large-scale radiation accidents.Conclusion. Recommendations for pre-hospital triage of victims are proposed.

Influence of Hospital Outdoor Space on Physiological Electroencephalography (EEG) Feedback of Staff

2021 ◽  
pp. 193758672110307
Author(s):  
Weiyi Cui ◽  
Zao Li ◽  
Xiaodong Xuan ◽  
Chao Lu ◽  
Qiqiang Tang ◽  
...  
Keyword(s):  
Large Scale ◽  
Open Space ◽  
Opposite Effect ◽  
Hospital Staff ◽  
Short Term ◽  
Outdoor Spaces ◽  
Short Term Exposure ◽  
Eeg Data ◽  
Physiological Feedback ◽  
Outdoor Space

Objectives: To investigate the differences and relationships between different outdoor spaces of hospitals on the physiological electroencephalography (EEG) feedback (PEEGF) of staff. Background: Relieving the pressure of hospital staff is essential, and several studies have revealed that even short-term exposure to outdoor space has a decompression effect. Yet, the focus is scarcely centered on the differences and influential relationships between the PEEGF from different outdoor spaces where the staff spend time, particularly in large-scale hospitals in China. Methods: EEG measurement equipment was utilized to obtain the value of β wave (vβw) that represents the stress and anxiety of staff in three different outdoor spaces: open, traffic, and rest. On the basis of EEG data, correlation analysis was conducted in accordance with the proportion of space elements. Results: The proportion of natural elements, such as landscape ( r = −.800** p=.005) and waterscape ( r = −.782* p=.013), were negatively correlated with the vβw produced by staff, while the proportion of hard paving was positive ( r = .817** p=.004) with more vβw produced by staff. In other words, the percentage of landscape and waterscape can reduce stress, while hard paving has the opposite effect. Further, there was a difference in the amount of vβw generated between nurses and administrators in the open space at the entrance of the main building ( p = .043). Conclusions: The present study revealed the influence of different outdoor space elements of the hospital on the physiological feedback of staff, demonstrated the practical necessity of evidence-based design, and proposed relevant optimization suggestions.

scholarly journals Oxidative Stress Alters miRNA and Gene Expression Profiles in Villous First Trimester Trophoblasts

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Courtney E. Cross ◽  
Mai F. Tolba ◽  
Catherine M. Rondelli ◽  
Meixiang Xu ◽  
Sherif Z. Abdel-Rahman
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Mrna Expression ◽  
Expression Profiles ◽  
First Trimester ◽  
Mirna Profile ◽  
Trophoblast Cells ◽  
Placental Development ◽  
Short Term ◽  
Short Term Exposure

The relationship between oxidative stress and miRNA changes in placenta as a potential mechanism involved in preeclampsia (PE) is not fully elucidated. We investigated the impact of oxidative stress on miRNAs and mRNA expression profiles of genes associated with PE in villous 3A first trimester trophoblast cells exposed to H2O2at 12 different concentrations (0-1 mM) for 0.5, 4, 24, and 48 h. Cytotoxicity, determined using the SRB assay, was used to calculate the IC50of H2O2. RNA was extracted after 4 h exposure to H2O2for miRNA and gene expression profiling. H2O2exerted a concentration- and time-dependent cytotoxicity on 3A trophoblast cells. Short-term exposure of 3A cells to low concentration of H2O2(5% of IC50) significantly altered miRNA profile as evidenced by significant changes in 195 out of 595 evaluable miRNAs. Tool for annotations of microRNAs (TAM) analysis indicated that these altered miRNAs fall into 43 clusters and 34 families, with 41 functions identified. Exposure to H2O2altered mRNA expression of 22 out of 84 key genes involved in dysregulation of placental development. In conclusion, short-term exposure of villous first trimester trophoblasts to low concentrations of H2O2significantly alters miRNA profile and expression of genes implicated in placental development.

scholarly journals Comprehensive Bioinformatics Identifies Key microRNA Players in ATG7-Deficient Lung Fibroblasts

2020 ◽  
Vol 21 (11) ◽  
pp. 4126 ◽  
Author(s):  
Stevan D. Stojanović ◽  
Maximilian Fuchs ◽  
Jan Fiedler ◽  
Ke Xiao ◽  
Anna Meinecke ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Human Fibroblasts ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Lung Fibroblasts ◽  
Specific Cell ◽  
Proteome Profiling

Background: Deficient autophagy has been recently implicated as a driver of pulmonary fibrosis, yet bioinformatics approaches to study this cellular process are lacking. Autophagy-related 5 and 7 (ATG5/ATG7) are critical elements of macro-autophagy. However, an alternative ATG5/ATG7-independent macro-autophagy pathway was recently discovered, its regulation being unknown. Using a bioinformatics proteome profiling analysis of ATG7-deficient human fibroblasts, we aimed to identify key microRNA (miR) regulators in autophagy. Method: We have generated ATG7-knockout MRC-5 fibroblasts and performed mass spectrometry to generate a large-scale proteomics dataset. We further quantified the interactions between various proteins combining bioinformatics molecular network reconstruction and functional enrichment analysis. The predicted key regulatory miRs were validated via quantitative polymerase chain reaction. Results: The functional enrichment analysis of the 26 deregulated proteins showed decreased cellular trafficking, increased mitophagy and senescence as the major overarching processes in ATG7-deficient lung fibroblasts. The 26 proteins reconstitute a protein interactome of 46 nodes and miR-regulated interactome of 834 nodes. The miR network shows three functional cluster modules around miR-16-5p, miR-17-5p and let-7a-5p related to multiple deregulated proteins. Confirming these results in a biological setting, serially passaged wild-type and autophagy-deficient fibroblasts displayed senescence-dependent expression profiles of miR-16-5p and miR-17-5p. Conclusions: We have developed a bioinformatics proteome profiling approach that successfully identifies biologically relevant miR regulators from a proteomics dataset of the ATG-7-deficient milieu in lung fibroblasts, and thus may be used to elucidate key molecular players in complex fibrotic pathological processes. The approach is not limited to a specific cell-type and disease, thus highlighting its high relevance in proteome and non-coding RNA research.

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