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pmid:40918148

Exploring the biological functions and immune regulatory roles of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM combined with MAFLD: integrated bioinformatics and single-cell analysis.

Qin Wang · Xiaoqi Li · Kaidierdan Wushoulaji · Jinyang Wang · Li Wan · Ye Yang · Xueli Gong

Frontiers in immunology · 2025 doi:10.3389/fimmu.2025.1587225

Paper

complete

Experiment

complete

Data

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Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40918148
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2026-11-01T20:31:01Z
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2026-11-01T22:57:09Z
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paper object · 10 fields
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title
Exploring the biological functions and immune regulatory roles of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM combined with MAFLD: integrated bioinformatics and single-cell analysis.
doi
10.3389/fimmu.2025.1587225
pmid
40918148
authors array · 7 items
[0]
Qin Wang
[1]
Xiaoqi Li
[2]
Kaidierdan Wushoulaji
[3]
Jinyang Wang
[4]
Li Wan
[5]
Ye Yang
[6]
Xueli Gong
journal
Frontiers in immunology
year
2025
abstract
Objective: Diabetes mellitus combined with nonalcoholic fatty liver disease is a prevalent and intricate metabolic disorder that presents a significant global health challenge, imposing economic and emotional burdens on society and families. An in-depth understanding of the disease pathogenesis is crucial for enhancing diagnostic and therapeutic efficacy. Therefore, the study aims to identify and validate autophagy-related diagnostic biomarkers associated with T2DM-associated MAFLD, investigate regulatory mechanisms in disease progression, and explore cellular diversity within the same tissue using single-cell sequencing data. Methods: This study utilized four datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE15653, GSE89632, GSE24807 and GSE23343. The analysis involved variance analysis, WGCNA analysis, PPI network construction, machine learning application, examination of autophagy-related gene sets, and diagnostic ROC analysis to identify and validate autophagy-related biomarkers in T2DM combined with MAFLD within an independent external dataset. Functional enrichment analysis, immune infiltration analysis, and validation of gene significance in T2DM combined with MAFLD progression were conducted using animal experiments to understand the biological functions and immunomodulatory roles of key biomarkers. Cellular diversity within liver tissues was characterized at the single-cell level, exploring interrelationships, differentiation, and developmental trajectories among cell populations through cellular communication and pseudo-temporal analyses. Results: The study identified four key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB). Real-time fluorescence quantitative PCR analysis in animal experiments demonstrated significantly higher mRNA expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM and MAFLD rat liver tissues compared to the control group. Quantitative immunohistochemical analysis revealed notably elevated protein expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in liver tissues of rats with T2DM and MAFLD when contrasted with the control group (P < 0.05). Enrichment analysis indicated associations of T2DM combined with MAFLD pathogenesis with pathways such as the NF-kappa B signaling pathway, MAPK signaling pathway, Fluid shear stress and atherosclerosis, Insulin resistance, and Cytokine-cytokine receptor interaction. Correlative analysis uncovered connections between immune infiltration and the identified genes. Single-cell transcriptomic analysis highlighted the differentiation of CX3CR1, JUNB, and TFRC in various single-cell-annotated populations. The pseudo-temporal analysis of epithelial cells identified enriched genes at crucial nodes related to "Leukocyte transendothelial migration", "Lipid and atherosclerosis", and "Type II diabetes mellitus" signaling pathways. Additionally, four cellular communication signaling pathways (TNF, CXCL, VEGF, and MIF) potentially significant in T2DM combined with MAFLD progression were identified through cell communication analysis. Conclusion: This study unveiled potential associations and key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB) concerning T2DM combined with MAFLD and relevant pathways, offering novel insights for the investigation of these two conditions.
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Difference analysis was performed separately to obtain the respective DEGs, using limma package (version 3.58.1), with screening criteria: |log2FC| > 0.5 and P < 0.05 (8, 9).
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sections[3] "Identification of differential expression genes" paragraph 2
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Difference analysis was performed separately to obtain the respective DEGs, using limma package (version 3.58.1), with screening criteria: |log2FC| > 0.5 and P < 0.05 (8, 9).
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sections[37] "Data availability statement" paragraph 2
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The datasets generated and/or analysed during the current study are available in the NCBI repository, and the data used to support the findings of this study have been deposited in the GEO repository (GSE15653, GSE89632, and GSE24807 and GSE136103).
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In the GSE15653 training dataset, receiver operating characteristic analysis showed AUCs above 0.8 for IRAK3, TFRC, TNFRSF1A, CX3CR1, and JUNB.
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sections[24] "Performance of five diagnostic biomarkers" paragraph 2
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In GSE15653, the AUCs for all five genes were above 0.8 ( Figure 4M ).
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In the GSE15653 training dataset, receiver operating characteristic analysis showed AUCs above 0.8 for IRAK3, TFRC, TNFRSF1A, CX3CR1, and JUNB.
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TNFRSF1A, CX3CR1, and JUNB in T2DM and MAFLD rat liver tissues compared to the control group.
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FRSF1A, CX3CR1, and JUNB in liver tissues of rats with T2DM and MAFLD when contrasted with the
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compared to the control group. Quantitative immunohistochemical analysis revealed notably elevated protein e
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dentities. Supplementary Table 1A summary of Microarray Information. Supplementary Table 2List of Pr
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40918148", "kind": "science-record", "revision": 4, "content_hash": "sha256:85fa2798382e7ec1e6b0048739d1ff5affd9eced0ad0b4ec78944d9368c84920", "created_at": "2026-11-01T20:31:01Z", "updated_at": "2026-11-01T22:57:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the biological functions and immune regulatory roles of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM combined with MAFLD: integrated bioinformatics and single-cell analysis.", "doi": "10.3389/fimmu.2025.1587225", "pmid": "40918148", "authors": ["Qin Wang", "Xiaoqi Li", "Kaidierdan Wushoulaji", "Jinyang Wang", "Li Wan", "Ye Yang", "Xueli Gong"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Objective: Diabetes mellitus combined with nonalcoholic fatty liver disease is a prevalent and intricate metabolic disorder that presents a significant global health challenge, imposing economic and emotional burdens on society and families. An in-depth understanding of the disease pathogenesis is crucial for enhancing diagnostic and therapeutic efficacy. Therefore, the study aims to identify and validate autophagy-related diagnostic biomarkers associated with T2DM-associated MAFLD, investigate regulatory mechanisms in disease progression, and explore cellular diversity within the same tissue using single-cell sequencing data.\nMethods: This study utilized four datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE15653, GSE89632, GSE24807 and GSE23343. The analysis involved variance analysis, WGCNA analysis, PPI network construction, machine learning application, examination of autophagy-related gene sets, and diagnostic ROC analysis to identify and validate autophagy-related biomarkers in T2DM combined with MAFLD within an independent external dataset. Functional enrichment analysis, immune infiltration analysis, and validation of gene significance in T2DM combined with MAFLD progression were conducted using animal experiments to understand the biological functions and immunomodulatory roles of key biomarkers. Cellular diversity within liver tissues was characterized at the single-cell level, exploring interrelationships, differentiation, and developmental trajectories among cell populations through cellular communication and pseudo-temporal analyses.\nResults: The study identified four key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB). Real-time fluorescence quantitative PCR analysis in animal experiments demonstrated significantly higher mRNA expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM and MAFLD rat liver tissues compared to the control group. Quantitative immunohistochemical analysis revealed notably elevated protein expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in liver tissues of rats with T2DM and MAFLD when contrasted with the control group (P < 0.05). Enrichment analysis indicated associations of T2DM combined with MAFLD pathogenesis with pathways such as the NF-kappa B signaling pathway, MAPK signaling pathway, Fluid shear stress and atherosclerosis, Insulin resistance, and Cytokine-cytokine receptor interaction. Correlative analysis uncovered connections between immune infiltration and the identified genes. Single-cell transcriptomic analysis highlighted the differentiation of CX3CR1, JUNB, and TFRC in various single-cell-annotated populations. The pseudo-temporal analysis of epithelial cells identified enriched genes at crucial nodes related to \"Leukocyte transendothelial migration\", \"Lipid and atherosclerosis\", and \"Type II diabetes mellitus\" signaling pathways. Additionally, four cellular communication signaling pathways (TNF, CXCL, VEGF, and MIF) potentially significant in T2DM combined with MAFLD progression were identified through cell communication analysis.\nConclusion: This study unveiled potential associations and key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB) concerning T2DM combined with MAFLD and relevant pathways, offering novel insights for the investigation of these two conditions.", "claims": [{"id": "claim-method", "statement": "Difference analysis was performed separately to obtain the respective DEGs, using limma package (version 3.58.1), with screening criteria: |log2FC| > 0.5 and P < 0.05 (8, 9).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40918148", "locator": "sections[3] \"Identification of differential expression genes\" paragraph 2", "quote": "Difference analysis was performed separately to obtain the respective DEGs, using limma package (version 3.58.1), with screening criteria: |log2FC| > 0.5 and P < 0.05 (8, 9)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the NCBI repository, and the data used to support the findings of this study have been deposited in the GEO repository (GSE15653, GSE89632, and GSE24807 and GSE136103).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40918148", "locator": "sections[37] \"Data availability statement\" paragraph 2", "quote": "The datasets generated and/or analysed during the current study are available in the NCBI repository, and the data used to support the findings of this study have been deposited in the GEO repository (GSE15653, GSE89632, and GSE24807 and GSE136103)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the GSE15653 training dataset, receiver operating characteristic analysis showed AUCs above 0.8 for IRAK3, TFRC, TNFRSF1A, CX3CR1, and JUNB.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40918148", "locator": "sections[24] \"Performance of five diagnostic biomarkers\" paragraph 2", "quote": "In GSE15653, the AUCs for all five genes were above 0.8 ( Figure 4M )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In the GSE15653 training dataset, receiver operating characteristic analysis showed AUCs above 0.8 for IRAK3, TFRC, TNFRSF1A, CX3CR1, and JUNB.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40918148"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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