Development of immune-related cell-based machine learning for disease progression and prognosis of alcoholic liver disease.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang X
PROVIDER: S-EPMC10307989 | biostudies-literature | 2023 Jul
REPOSITORIES: biostudies-literature
Publications
Development of immune-related cell-based machine learning for disease progression and prognosis of alcoholic liver disease.
Zhang Xu X Feng Xuezhe X Huai Qian Q Zhu Cheng C Sun Yingyin Y Li Xiaolei X Dai Hanren H Wang Honglin H Wang Hua H
Clinical and translational medicine 20230701 7
Similar Datasets
Project description:BackgroundNon-alcoholic fatty liver (NAFL) can progress to the severe subtype non-alcoholic steatohepatitis (NASH) and/or fibrosis, which are associated with increased morbidity, mortality, and healthcare costs. Current machine learning studies detect NASH; however, this study is unique in predicting the progression of NAFL patients to NASH or fibrosis.AimTo utilize clinical information from NAFL-diagnosed patients to predict the likelihood of progression to NASH or fibrosis.MethodsData were collected from electronic health records of patients receiving a first-time NAFL diagnosis. A gradient boosted machine learning algorithm (XGBoost) as well as logistic regression (LR) and multi-layer perceptron (MLP) models were developed. A five-fold cross-validation grid search was utilized for hyperparameter optimization of variables, including maximum tree depth, learning rate, and number of estimators. Predictions of patients likely to progress to NASH or fibrosis within 4 years of initial NAFL diagnosis were made using demographic features, vital signs, and laboratory measurements.ResultsThe XGBoost algorithm achieved area under the receiver operating characteristic (AUROC) values of 0.79 for prediction of progression to NASH and 0.87 for fibrosis on both hold-out and external validation test sets. The XGBoost algorithm outperformed the LR and MLP models for both NASH and fibrosis prediction on all metrics.ConclusionIt is possible to accurately identify newly diagnosed NAFL patients at high risk of progression to NASH or fibrosis. Early identification of these patients may allow for increased clinical monitoring, more aggressive preventative measures to slow the progression of NAFL and fibrosis, and efficient clinical trial enrollment.
| S-EPMC8938756 | biostudies-literature
Project description:OPINION STATEMENT:Many variables, aside from the amount and duration of alcohol consumption, play a role in the development and progression of alcoholic liver disease (ALD). One critical factor that can be modified is diet/nutrition. We have made major recent advances in our understanding of the interactions of nutrition and ALD. In this article, we review advances made in zinc metabolism/therapy for ALD. There is major zinc dyshomeostasis with ALD which is mediated, in part, by poor intake and absorption, increased excretion, and altered zinc transporters, especially ZIP14. Zinc deficiency plays an etiologic role in multiple mechanisms of ALD, ranging from intestinal barrier dysfunction to hepatocyte apoptosis. Zinc supplementation is highly effective at correcting these ALD mechanisms and preventing/treating experimental ALD. There is no Food and Drug Administration (FDA) approved therapy for any stage of ALD. Because animal and human data suggest that zinc deficiency occurs early in the course of ALD, we treat most ALD patients with daily oral zinc supplementation (220 mg zinc sulfate which contains 50 mg elemental zinc).
| S-EPMC6206836 | biostudies-other
Project description:Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.
| S-EPMC8615894 | biostudies-literature
Project description:BackgroundThe occurrence of ischemic stroke (IS) is associated with nonalcoholic fatty liver disease (NAFLD). The cancer burden of NAFLD complicated by IS also warrants attention. This study aimed to identify candidate immune biomarkers linked to NAFLD and IS and analyze their association with cancer.MethodsTwo of each of the NAFLD and IS datasets were downloaded, differentially expressed genes (DEGs) were identified, and module genes were screened via weighted gene coexpression network analysis (WGCNA). Subsequently, utilizing machine learning (least absolute shrinkage and selection operator regression, random forest and support vector machine-recursive feature elimination) and immune cell infiltration analysis, immune-related candidate biomarkers for NAFLD with IS were determined. Simultaneously, a nomogram was established, the diagnostic efficacy was assessed, and the role of candidate biomarkers in cancer was ascertained through pan-cancer analyses.ResultsIn this study, 117 and 98 DEGs were identified from the combined NAFLD and IS datasets, respectively, and 279 genes were obtained from the most significant modules of NAFLD. NAFLD module genes and IS DEGs were intersected to obtain nine genes, which were enriched in the inflammatory response and immune regulation. After overlapping the results of the three machine learning algorithms, six candidate genes were obtained, based on which a nomogram was constructed. The calibration curve demonstrated good accuracy, and the candidate genes had high diagnostic values. The genes were found to be related to the immune dysregulation of stroke, and RRS1 was strongly associated with the prognosis, immune cell infiltration, microsatellite instability (MSI), and tumor mutation burden (TMB).ConclusionSix common candidate immune-related genes (PTGS2, FCGR1A, MMP9, VNN3, S100A12, and RRS1) of NAFLD and IS were identified, and a nomogram for diagnosing NAFLD with IS was established. RRS1 may serve as a candidate gene for predicting the prognosis of patients with cancer who have NAFLD complicated by IS, which could aid in their diagnosis and treatment.
| S-EPMC10115222 | biostudies-literature
Project description:The liver is the site of first pass metabolism, detoxifying and metabolizing blood arriving from the hepatic portal vein and hepatic artery. It is made up of multiple cell types, including macrophages. These are either bona fide tissue-resident Kupffer cells (KC) of embryonic origin, or differentiated from circulating monocytes. KCs are the primary immune cells populating the liver under steady state. Liver macrophages interact with hepatocytes, hepatic stellate cells, and liver sinusoidal endothelial cells to maintain homeostasis, however they are also key contributors to disease progression. Generally tolerogenic, they physiologically phagocytose foreign particles and debris from portal circulation and participate in red blood cell clearance. However as immune cells, they retain the capacity to raise an alarm to recruit other immune cells. Their aberrant function leads to the development of non-alcoholic fatty liver disease (NAFLD). NAFLD refers to a spectrum of conditions ranging from benign steatosis of the liver to steatohepatitis and cirrhosis. In NAFLD, the multiple hit hypothesis proposes that simultaneous influences from the gut and adipose tissue (AT) generate hepatic fat deposition and that inflammation plays a key role in disease progression. KCs initiate the inflammatory response as resident immune effectors, they signal to neighbouring cells and recruit monocytes that differentiated into recruited macrophages in situ. Recruited macrophages are central to amplifying the inflammatory response and causing progression of NAFLD to its fibro-inflammatory stages. Given their phagocytic capacity and their being instrumental in maintaining tissue homeostasis, KCs and recruited macrophages are fast-becoming target cell types for therapeutic intervention. We review the literature in the field on the roles of these cells in the development and progression of NAFLD, the characteristics of patients with NAFLD, animal models used in research, as well as the emerging questions. These include the gut-liver-brain axis, which when disrupted can contribute to decline in function, and a discussion on therapeutic strategies that act on the macrophage-inflammatory axis.
| S-EPMC10291618 | biostudies-literature
Project description:Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.
| S-EPMC11387488 | biostudies-literature
Project description:INTRODUCTION:Non-alcoholic fatty liver disease (NAFLD) has a wide spectrum, eventually leading to cirrhosis and hepatic carcinogenesis. We previously reported that a series of microRNAs (miRNAs) mapped in the 14q32.2 maternally imprinted gene region (Dlk1-Dio3 mat) are related to NAFLD development and progression in a mouse model. We examined the suitability of miR-379, a circulating Dlk1-Dio3 mat miRNA, as a human NAFLD biomarker. METHODS:Eighty NAFLD patients were recruited for this study. miR-379 was selected from the putative Dlk1-Dio3 mat miRNA cluster because it exhibited the greatest expression difference between NAFLD and non-alcoholic steatohepatitis in our preliminary study. Real-time PCR was used to examine the expression levels of miR-379 and miR-16 as an internal control. One patient was excluded due to low RT-PCR signal. RESULTS:Compared to normal controls, serum miR-379 expression was significantly up-regulated in NAFLD patients. Receiver operating characteristic curve analysis suggested that miR-379 is a suitable marker for discriminating NAFLD patients from controls, with an area under the curve value of 0.72. Serum miR-379 exhibited positive correlations with alkaline phosphatase, total cholesterol, low-density-lipoprotein cholesterol and non-high-density-lipoprotein cholesterol levels in patients with early stage NAFLD (Brunt fibrosis stage 0 to 1). The correlation between serum miR-379 and cholesterol levels was lost in early stage NAFLD patients treated with statins. Software-based predictions indicated that various energy metabolism-related genes, including insulin-like growth factor-1 (IGF-1) and IGF-1 receptor, are potential targets of miR-379. CONCLUSIONS:Serum miR-379 exhibits high potential as a biomarker for NAFLD. miR-379 appears to increase cholesterol lipotoxicity, leading to the development and progression of NAFLD, via interference with the expression of target genes, including those related to the IGF-1 signaling pathway. Our results could facilitate future research into the pathogenesis, diagnosis, and treatment of NAFLD.
| S-EPMC7046274 | biostudies-literature
Project description:Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide with rising rates in parallel to obesity, type 2 diabetes, and metabolic syndrome. NAFLD includes pathologies ranging from simple steatosis (NAFL) to non-alcoholic steatohepatitis and cirrhosis (NASH), which may eventually develop into hepatocellular carcinoma (HCC). Mechanically, lipids accumulation and insulin resistance act as the first hit, inflammation and fibrosis serve as the second hit. Currently, the diagnosis of NAFLD mainly depends on pathology examination and medical imaging, whereas proper gene signature classifiers are necessary for the evaluation of disease status. Here, we developed three signature classifiers to distinguish different NAFLD disease states (NAFL and NASH). Moreover, we found that B cells, DCs, and MAIT cells are key deregulated immune cells in NAFLD, which are associated with NAFLD and NAFLD-HCC progression. Meanwhile, AKR1B10 and SPP1 are closely related to the above three immune cell infiltrations and immunosuppressive cytokines expressions in NAFLD and NAFLD-HCC. Subsequently, we screened out AKR1B10 and SPP1 sensitive molecules TGX-221, which may provide a possible therapy for NAFLD and NAFLD-HCC.
| S-EPMC7870871 | biostudies-literature
Project description:Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas.
| S-EPMC10043285 | biostudies-literature
Project description:BackgroundThe prevalence of nonalcoholic fatty liver disease (NAFLD) is a major form of chronic liver disease. This study aimed to scrutinize the diagnostic biomarkers of NAFLD and their correlation with the immune microenvironment through bioinformatic analysis.MethodsTo identify genes associated with nonalcoholic fatty liver disease (NAFLD), we obtained microarray datasets (GSE63067 and GSE89632) from the Gene Expression Omnibus (GEO) database. Machine learning techniques such as Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) were used to identify key genes. We performed gene ontology analysis to identify the driver pathways of NAFLD. External datasets (merging GSE48452, GSE66676 and GSE135251) were used to validate the identified genes and confirm protein levels by Western blotting. The CIBERSORT algorithm and immune-related techniques, such as ssGSEA, were used to assess the level of infiltration of different immune cell types and their functions. Finally, Spearman's analysis confirmed the relationship between pivotal genes and immune cells.ResultsHub genes (BBOX1, FOSB, NR4A2, RAB26 and SOCS2) were identified as potential biomarkers. This study demonstrates that these hub genes are significantly dysregulated in NAFLD, suggesting that they may be useful as diagnostic indicators and possible targets for treatment. Also covered are their possible effects on inflammation, immune cell activation, and liver damage in NAFLD. A better understanding of the intricate relationship between metabolic inefficiency, immunological response, and liver pathology in NAFLD may be gained from this work, which can lead to the development of new diagnostic tools and clinical treatments.ConclusionThe current study identified BBOX1, FOSB, NR4A2, RAB26 and SOCS2 as important diagnostic biomarkers for NAFLD. The study highlights the important function of immune cell infiltration in developing NAFLD. Their findings provide valuable molecular biological insights into the development of NAFLD and may lead to novel therapeutic strategies for treating this disease.
| S-EPMC11301246 | biostudies-literature