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pmid:39095691
Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.
Qian Xie · Xuehe Zhang · Fen Liu · Junyi Luo · Chang Liu · Zhiyang Zhang · Yining Yang · Xiaomei Li
BMC cardiovascular disorders · 2024 doi:10.1186/s12872-024-04026-3
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Experiment
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Data
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Analysis
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Complete file
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In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases.\nMETHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes.\nRESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples.\nCONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.", "claims": [{"id": "claim-method", "statement": "Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[7] \"Immune infiltration and immune correlation analysis of HUB genes\" paragraph 2", "quote": "Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05)."}, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."}, {"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05)."}, {"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.", "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:39095691"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE28829"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE28829"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39095691", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12872-024-04026-3", "sha256": "sha256:97666c9947cfb1eab8854774dcec9160f140bee2c2d1ed7c1e7ed571cce9b832", "bytes": 35127, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12872-024-04026-3", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11295872", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE28829", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28829", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28829", "version": null, "note": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? 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