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pmid:38297226

Lipid metabolism-related genes are involved in the occurrence of asthma and regulate the immune microenvironment.

Yuanmin Jia · Haixia Wang · Bin Ma · Zeyi Zhang · Jingjing Wang · Jin Wang · Ou Chen

BMC genomics · 2024 doi:10.1186/s12864-023-09795-3

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:38297226
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science-record
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sha256:a50c8705022401831e4cd05cee700ec0247684673fc1b2962458c9090644a5ef
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T19:20:14Z
updated_at
2026-11-01T22:53:35Z
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paper object · 10 fields
state
complete
title
Lipid metabolism-related genes are involved in the occurrence of asthma and regulate the immune microenvironment.
doi
10.1186/s12864-023-09795-3
pmid
38297226
authors array · 7 items
[0]
Yuanmin Jia
[1]
Haixia Wang
[2]
Bin Ma
[3]
Zeyi Zhang
[4]
Jingjing Wang
[5]
Jin Wang
[6]
Ou Chen
journal
BMC genomics
year
2024
abstract
BACKGROUND: Lipid metabolism plays a pivotal role in asthma pathogenesis. However, a comprehensive analysis of the importance of lipid metabolism-related genes (LMRGs) in regulating the immune microenvironment in asthma remains lacking. The transcriptome matrix was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed analysis and weighted gene coexpression network analysis (WGCNA) were conducted on the GSE74986 dataset to select hub LMRGs, and gene set enrichment analysis (GSEA) was conducted to explore their biological functions. The CIBERSORT algorithm was used to determine immune infiltration in the asthma and control groups, and the correlation of diagnostic biomarkers and immune cells was performed via Spearman correlation analysis. Subsequently, a competitive endogenous RNA (ceRNA) network was constructed to investigate the hidden molecular mechanism of asthma. The expression levels of the hub genes were further validated in the GSE143192 dataset, and RT‒qPCR and immunofluorescence were performed to verify the reliability of the results in the OVA asthma model. Lastly, the ceRNA network was confirmed by qRT-PCR and RNAi experiments in the characteristic cytokine (IL-13)-induced asthma cellular model. RESULTS: ASAH1, ACER3 and SGPP1 were identified as hub LMRGs and were mainly involved in protein secretion, mTORC1 signaling, and fatty acid metabolism. We found more infiltration of CD8+ T cells, activated NK cells, and monocytes and less M0 macrophage infiltration in the asthma group than in the healthy control group. In addition, ASAH1, ACER3, and SGPP1 were negatively correlated with CD8+ T cells and activated NK cells, but positively correlated with M0 macrophages. Within the ceRNA network, SNHG9-hsa-miR-615-3p-ACER3, hsa-miR-212-5p and hsa-miR-5682 may play crucial roles in asthma pathogenesis. The low expression of ASAH1 and SGPP1 in asthma was also validated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upregulated, while that of ACER3 was significantly downregulated. CONCLUSION: ASAH1, ACER3 and SGPP1 might be diagnostic biomarkers for asthma, and are associated with increased immune system activation. In addition, SNHG9-hsa-miR-615-3p-ACER3 may be viewed as effective therapeutic targets for asthma. Our findings might provide a novel perspective for future research on asthma.
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Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. The Benjamini–Hochberg (FDR) [19] procedure was used to correct for multiple testing. FDR < 0.05 and |log2(fold change, FC)|> 1 were set as cut-off thresholds for statistical significance.
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sections[4] "Differentially expressed genes identification" paragraph 2
quote
Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. The Benjamini–Hochberg (FDR) [19] procedure was used to correct for multiple testing. FDR < 0.05 and |log2(fold change, FC)|> 1 were set as cut-off thresholds for statistical significance.
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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sections[31] "Availability of data and materials" paragraph 2
quote
The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated.
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sections[17] "Identification of DEGs" paragraph 2
quote
A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated (Additional file 3: Table S4 and Additional file 4: Table S5).
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A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated.
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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microRNA 27b-3p modulates SYK in Pediatric Asthma Induced by Dust Mites
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2026-11-01T19:09:46Z
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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Differentially expressed profile of LncRNA in PBMCs (peripheral blood mononuclear cells) of asthma patients
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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Changes in Expression of Genes Regulating Airway Inflammation Following a High-Fat Mixed Meal in Asthmatics
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).
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Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. The Benjamini–Hochberg (FDR) [19] procedure was used to correct for multiple testing. FDR < 0.05 and |log2(fold change, FC)|> 1 were set as cut-off thresholds for statistical significance.
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lidated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upr
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tee of Shandong University. Cell culture and transfection BEAS-2B cells were purchased from the Cell B
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methods Animals and grouping C57BL/6 J male mice were provided by Vital River Laboratories (Beijin
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y Vital River Laboratories (Beijing, China). Mice were allowed tap water and rodent chow and were m
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Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. The Benjamini–Hochberg (FDR) [19] procedure was used to correct for multiple testing. FDR < 0.05 and |log2(fold change, FC)|> 1 were set as cut-off thresholds for statistical significance.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38297226", "kind": "science-record", "revision": 4, "content_hash": "sha256:a50c8705022401831e4cd05cee700ec0247684673fc1b2962458c9090644a5ef", "created_at": "2026-11-01T19:20:14Z", "updated_at": "2026-11-01T22:53:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lipid metabolism-related genes are involved in the occurrence of asthma and regulate the immune microenvironment.", "doi": "10.1186/s12864-023-09795-3", "pmid": "38297226", "authors": ["Yuanmin Jia", "Haixia Wang", "Bin Ma", "Zeyi Zhang", "Jingjing Wang", "Jin Wang", "Ou Chen"], "journal": "BMC genomics", "year": 2024, "abstract": "BACKGROUND: Lipid metabolism plays a pivotal role in asthma pathogenesis. However, a comprehensive analysis of the importance of lipid metabolism-related genes (LMRGs) in regulating the immune microenvironment in asthma remains lacking. The transcriptome matrix was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed analysis and weighted gene coexpression network analysis (WGCNA) were conducted on the GSE74986 dataset to select hub LMRGs, and gene set enrichment analysis (GSEA) was conducted to explore their biological functions. The CIBERSORT algorithm was used to determine immune infiltration in the asthma and control groups, and the correlation of diagnostic biomarkers and immune cells was performed via Spearman correlation analysis. Subsequently, a competitive endogenous RNA (ceRNA) network was constructed to investigate the hidden molecular mechanism of asthma. The expression levels of the hub genes were further validated in the GSE143192 dataset, and RT‒qPCR and immunofluorescence were performed to verify the reliability of the results in the OVA asthma model. Lastly, the ceRNA network was confirmed by qRT-PCR and RNAi experiments in the characteristic cytokine (IL-13)-induced asthma cellular model.\nRESULTS: ASAH1, ACER3 and SGPP1 were identified as hub LMRGs and were mainly involved in protein secretion, mTORC1 signaling, and fatty acid metabolism. We found more infiltration of CD8+ T cells, activated NK cells, and monocytes and less M0 macrophage infiltration in the asthma group than in the healthy control group. In addition, ASAH1, ACER3, and SGPP1 were negatively correlated with CD8+ T cells and activated NK cells, but positively correlated with M0 macrophages. Within the ceRNA network, SNHG9-hsa-miR-615-3p-ACER3, hsa-miR-212-5p and hsa-miR-5682 may play crucial roles in asthma pathogenesis. The low expression of ASAH1 and SGPP1 in asthma was also validated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upregulated, while that of ACER3 was significantly downregulated.\nCONCLUSION: ASAH1, ACER3 and SGPP1 might be diagnostic biomarkers for asthma, and are associated with increased immune system activation. In addition, SNHG9-hsa-miR-615-3p-ACER3 may be viewed as effective therapeutic targets for asthma. Our findings might provide a novel perspective for future research on asthma.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. The Benjamini–Hochberg (FDR) [19] procedure was used to correct for multiple testing. 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FDR < 0.05 and |log2(fold change, FC)|> 1 were set as cut-off thresholds for statistical significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38297226", "locator": "sections[31] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analysed during the current study are available in GEO datasets GSE74986 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74986), GSE74075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE74075), GSE120172 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE120172), and GSE143192 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE143192)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38297226", "locator": "sections[17] \"Identification of DEGs\" paragraph 2", "quote": "A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated (Additional file 3: Table S4 and Additional file 4: Table S5)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 520 DEGs between asthma and control samples were detected in the GSE74986 dataset under the cut-off thresholds of FDR < 0.05 and |log2(fold change, FC)|> 1, among which 64 genes were upregulated and 456 genes were downregulated.", "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:38297226"], "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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"platform": "6480", "taxon": "Homo sapiens", "title": "Analysis of gene expression in human Bronchial Alveolar Lavage Cell (BAL) cell pellets", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE120172 not downloaded", "asset:data:geo:GSE120172 licence not read", "asset:data:geo:GSE143192 not downloaded", "asset:data:geo:GSE143192 licence not read", "asset:data:geo:GSE74075 not downloaded", "asset:data:geo:GSE74075 licence not read", "asset:data:geo:GSE74986 not downloaded", "asset:data:geo:GSE74986 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, 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After SNHG9 knockdown, miR-615-3p expression was significantly upr", "tee of Shandong University. Cell culture and transfection BEAS-2B cells were purchased from the Cell B", "d for antibiotic-free media. Cells were then transfected with si-SNHG9 (General Biol, Chuzhou, China)", "ence: 5'-CCCGAAGAGUGGCUAUAAATT-3') using the transfection reagent (OGTR(C)20131001, Obio, Shanghai, Ch"], "order_basis": "paper_text_order:2038", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["methods Animals and grouping C57BL/6 J male mice were provided by Vital River Laboratories (Beijin", "y Vital River Laboratories (Beijing, China). Mice were allowed tap water and rodent chow and were m", "After 1 week of acclimatization, the C57BL/6 mice were randomly divided into two groups (n = 4 per"], "order_basis": "paper_text_order:2529", "id": "assay:2"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["of the Chinese Academy of Sciences and were cultured in DMEM high glucose medium (CM15019, Macgene)"], "order_basis": "paper_text_order:3525", "id": "assay:3"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differentially expressed genes (DEGs) between asthmatic patients and healthy controls from the GSE74986 dataset were selected utilizing the “limma” package in R [18]. 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