Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Zhang, Feng; Zhang, Zhengwei; Li, Yapeng; Sun, Yi; Zhou, Xin; Chen, Xiaoning; Sun, Shibo

doi:10.3389/fgene.2022.942153

Cited by 5 publications

(4 citation statements)

References 45 publications

(46 reference statements)

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“…is nding -consistent with previous studies (Wen et al 2021;Zhang et al 2022) -suggests that immunity plays an essential role in NAFLD. Furthermore, based on 48 ERSR-DEGs, we identi ed two ERS-related clusters using unsupervised cluster analysis.…”

Section: Discussionsupporting

confidence: 92%

The role of endoplasmic reticulum stress-related genes in the diagnosis and subtyping of non-alcoholic fatty liver disease

Guo,

Yu,

Fang

et al. 2024

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Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Chronic activation of endoplasmic reticulum stress (ERS) in hepatocytes may promote the development of NAFLD, yet endoplasmic reticulum stress-related genes (ERSGs) have not been studied in NAFLD. Our aim is to study the relationship between ERSGs and the immune microenvironment of NAFLD patients and to construct predictive models. We screened 48 endoplasmic reticulum stress-related di erentially expressed genes (ERSR-DEGs) using data from two GEO datasets and the GeneCards database. Enrichment analysis revealed that ERSR-DEGs are closely associated with immune-related pathways and functions. e immune in ltration pro le of NAFLD was obtained by single sample gene set enrichment analysis (ssGSEA). ere were signi cant di erences in immune cell in ltration and immune function between NAFLD group and control group. Using 113 NAFLD samples, we explored two molecular clusters based on ERSR-DEGs. A ve-gene SVM model was selected as the best machine learning model, and a nomogram based on ve-gene SVM model showed good predictive e ciency. e mRNA expression levels of POR, PPP1R15A, FOS and FAS were signi cantly di erent between NAFLD mice and healthy mice. In conclusion, ERS is closely associated with the development of NAFLD. We established a promising and SVM-based predictive model to assess the risk of disease in patients with ERS subtypes and NAFLD.

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Section: Discussionsupporting

confidence: 92%

The role of endoplasmic reticulum stress-related genes in the diagnosis and subtyping of non-alcoholic fatty liver disease

Guo,

Yu,

Fang

et al. 2024

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“…Higher levels of M1 macrophage, M2 macrophage, resting dendritic cell, and resting mast cell infiltration were seen in patients with NAFLD, while lower levels of monocyte, activated dendritic cell, activated mast cell, eosinophil, and neutrophil infiltration were seen in these patients. These findings were consistent with previous studies that examined liver tissues (Wen et al, 2021;Zhang et al, 2022). In addition, we used unsupervised clustering analysis to illustrate the expression landscape of different disulfidptosis regulatory patterns underlying NAFLD patients based on DE-DRGs.…”

Section: Discussionsupporting

confidence: 87%

Identification and validation of disulfidptosis-associated molecular clusters in non-alcoholic fatty liver disease

Yu,

Guo,

Fang

et al. 2023

Front. Genet.

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Objective: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease in the world, and its pathogenesis is not fully understood. Disulfidptosis is the most recently reported form of cell death and may be associated with NAFLD progression. Our study aimed to explore the molecular clusters associated with disulfidptosis in NAFLD and to construct a predictive model.Methods: First, we analyzed the expression profile of the disulfidptosis regulators and immune characteristics in NAFLD. Using 104 NAFLD samples, we investigated molecular clusters based on differentially expressed disulfidptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were then identified by using the WGCNA method. We also evaluated the performance of four machine learning models before choosing the optimal machine model for diagnosis. Nomogram, calibration curves, decision curve analysis, and external datasets were used to confirm the prediction effectiveness. Finally, the expression levels of the biomarkers were assessed in a mouse model of a high-fat diet.Results: Two differentially expressed DRGs were identified between healthy and NAFLD patients. We revealed the expression profile of DRGs in NAFLD and the correlation with 22 immune cells. In NAFLD, two clusters of molecules connected to disulfidptosis were defined. Significant immunological heterogeneity was shown by immune infiltration analysis among the various clusters. A significant amount of immunological infiltration was seen in Cluster 1. Functional analysis revealed that Cluster 1 differentially expressed genes were strongly linked to energy metabolism and immune control. The highest discriminatory performance was demonstrated by the SVM model, which had a higher area under the curve, relatively small residual and root mean square errors. Nomograms, calibration curves, and decision curve analyses were used to show how accurate the prediction of NAFLD was. Further analysis revealed that the expression of three model-related genes was significantly associated with the level of multiple immune cells. In animal experiments, the expression trends of DDO, FRK and TMEM19 were consistent with the results of bioinformatics analysis.Conclusion: This study systematically elucidated the complex relationship between disulfidptosis and NAFLD and developed a promising predictive model to assess the risk of disease in patients with disulfidptosis subtypes and NAFLD.

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“…Therefore, it is necessary to move from the gene level to the pathway level, which can help to capture the heterogeneous patterns of tumors. Some studies have found patterns of mutations at the pathway level [ 3 , 4 ]. Most of these studies are based on known information about signaling pathways, and attempt to search pathway databases for pathways that drive cancer development.…”

Section: Introductionmentioning

confidence: 99%

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

Zhang,

Xiang,

Zhao

et al. 2023

Entropy

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Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.

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Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Cited by 5 publications

References 45 publications

The role of endoplasmic reticulum stress-related genes in the diagnosis and subtyping of non-alcoholic fatty liver disease

The role of endoplasmic reticulum stress-related genes in the diagnosis and subtyping of non-alcoholic fatty liver disease

Identification and validation of disulfidptosis-associated molecular clusters in non-alcoholic fatty liver disease

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

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