Comprehensive Analysis of NAFLD and the Therapeutic Target Identified

Objective: Non-alcoholic fatty liver disease (NAFLD) is a serious health threat worldwide. The aim of this study was to comprehensively describe the metabolic and immunologic characteristics of NAFLD, and to explore potential therapeutic drug targets for NAFLD.Methods: Six NAFLD datasets were downloaded from the Gene Expression Omnibus (GEO) database, including GSE48452, GSE63067, GSE66676, GSE89632, GSE24807, and GSE37031. The datasets we then used to identify and analyze genes that were differentially expressed in samples from patients with NAFLD and normal subjects, followed by analysis of the metabolic and immunologic characteristics of patients with NAFLD. We also identified potential therapeutic drugs for NAFLD using the Connectivity Map (CMAP) database. Moreover, we constructed a prediction model using minimum depth random forest analysis and screened for potential therapeutic targets. Finally, therapeutic targets were verified in a fatty liver model stimulated by palmitic acid (PA).Results: A total of 1,358 differentially expressed genes (DEGs) were obtained, which were mainly enriched in carbohydrate metabolism, lipid metabolism, and other metabolic pathways. Immune infiltration analysis showed that memory B cells, regulatory T cells and M1 macrophage were significantly up-regulated, while T cells follicular helper were down regulated in NAFLD. These may provide a reference for the immune-metabolism interaction in the pathogenesis of NAFLD. Digoxin and helveticoside were identified as potential therapeutic drugs for NAFLD via the CMAP database. In addition, a five-gene prediction model based on minimum depth random forest analysis was constructed, and the receiver operating characteristic (ROC) curves of both training and validation set reached 1. The five candidate therapeutic targets were ENO3, CXCL10, INHBE, LRRC31, and OPTN. Moreover, the efficiency of hepatocyte adipogenesis decreased after OPTN knockout, confirming the potential use of OPTN as a new therapeutic target for NAFLD.Conclusion: This study provides a deeper insight into the molecular pathogenesis of NAFLD. We used five key genes to construct a diagnostic model with a strong predictive effect. Therefore, these five key genes may play an important role in the diagnosis and treatment of NAFLD, particularly those with increased OPTN expression.

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In-depth immune cellular profiling reveals sex-specific associations with frailty

10.21203/rs.2.19660/v2 ◽

2020 ◽

Author(s):

Leonard Daniël Samson ◽

A. Mieke H. Boots ◽

José A. Ferreira ◽

H. Susan J. Picavet ◽

Lia G. H. De Rond ◽

...

Keyword(s):

T Cells ◽

Random Forest ◽

Peripheral Blood ◽

Immune Cell ◽

Cell Subpopulation ◽

Men And Women ◽

Random Forest Analysis ◽

Cell Numbers ◽

Immune Cell Subpopulations ◽

Cell Subpopulations

Abstract Background: With advancing age, the composition of leukocyte subpopulations in peripheral blood is known to change, but how this change differs between men and women and how it relates to frailty is poorly understood. Thus, our aim in this exploratory study was to investigate whether frailty is associated with changes in immune cell subpopulations and whether associations differ between men and women. Therefore, we performed in-depth immune cell phenotyping by enumerating a total of 37 subsets of T cells, B cells, NK cells, monocytes, and neutrophils in peripheral blood of 289 elderly people between 60-87 years of age. Associations between frailty and each immune cell subpopulation were tested separately in men and women and were adjusted for age and CMV serostatus. In addition, a random forest algorithm was used to predict a participant’s frailty score based on enumeration of immune cell subpopulations. Results: In an association study, frailty was observed to be associated with increases in numbers of neutrophils in both men and in women. Furthermore, sex-specific associations were found. Frailer women, but not men, showed higher numbers of total and CD16^-^ monocytes and lower numbers of CD56^+^ T cells. Overall, the accuracy of the predictions in the random forest analysis was low (9.2% explained variance in men and 12.2% in women). Yet, the random forest analysis confirmed all associations mentioned above, but did not confirm a possible negative association in women between frailty and late differentiated CD4^+^ TemRA cells. Moreover, the random forest analysis revealed additional relationships with frailty, with frailer men showing higher CD16^+^ monocyte and lower naïve B cell numbers. Other important variables for predicting frailty were plasmablast numbers in men and total T cell numbers in women. Conclusions: We report on observed associations of frailty with elevated myeloid cell numbers in men and women. In-depth immune cellular profiling also revealed sex-specific associations of frailty with several immune subpopulations. However, an expected positive association between frailty and memory T cells was not observed. We hope that our study will prompt further investigation into the immune mechanisms associated with the development of frailty in men and women.

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A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector

IEEE Access ◽

10.1109/access.2019.2914999 ◽

2019 ◽

Vol 7 ◽

pp. 60134-60149 ◽

Cited By ~ 15

Author(s):

Irfan Ullah ◽

Basit Raza ◽

Ahmad Kamran Malik ◽

Muhammad Imran ◽

Saif Ul Islam ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Prediction Model ◽

Machine Learning Techniques ◽

Churn Prediction ◽

Random Forest Analysis ◽

Learning Techniques ◽

Telecom Sector

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Factors regulating apoptosis and homeostasis of CD4+CD25highFOXP3+ regulatory T cells are new therapeutic targets

Frontiers in Bioscience ◽

10.2741/2775 ◽

2008 ◽

Vol 13 (13) ◽

pp. 1472 ◽

Cited By ~ 32

Author(s):

Xiao-Feng Yang

Keyword(s):

T Cells ◽

Regulatory T Cells ◽

Therapeutic Targets ◽

New Therapeutic Targets

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Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

Technology in Cancer Research & Treatment ◽

10.1177/15330338211024660 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110246

Author(s):

Jihwan Park ◽

Mi Jung Rho ◽

Hyong Woo Moon ◽

Jaewon Kim ◽

Chanjung Lee ◽

...

Keyword(s):

Prostate Cancer ◽

Random Forest ◽

Radical Prostatectomy ◽

Prediction Model ◽

Biochemical Recurrence ◽

Clinical Decision ◽

Outcome Variable ◽

Tertiary Hospitals ◽

Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

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