Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36158806", "kind": "science-record", "revision": 5, "content_hash": "sha256:1daffdbcf983dc51868463b85f38c201d06598150e90a610140de4916324dd6b", "created_at": "2026-11-01T18:15:26Z", "updated_at": "2026-11-01T22:50:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis.", "doi": "10.3389/fcvm.2022.927397", "pmid": "36158806", "authors": ["Huiping Yang", "Bingquan Xiong", "Tianhua Xiong", "Dinghui Wang", "Wenlong Yu", "Bin Liu", "Qiang She"], "journal": "Frontiers in cardiovascular medicine", "year": 2022, "abstract": "Background: In recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.\nMethods: RNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from the public functional genomics database Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified using the R package DESeq2, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Next, a PPI (protein-protein interaction) network was constructed, and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cell infiltration, and key transcription factors were predicted based on ChEA3.\nResults: By comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. A total of 10 genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, and SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in the inflammatory response and cytokine activity. Immune cell infiltration analysis indicated that macrophage M2 and T cells CD4 memory resting accounted for the largest proportion of these immune cells. CSRNP1, RELB, NFKB2, SNAI1, and FOSB were detected as potential transcription factors.\nConclusion: Comprehensive bioinformatic analysis was used to compare the difference in EAT between diabetic and non-diabetic patients. Several hub genes, transcription factors, and immune cell infiltration were identified. Diabetic EAT is significantly different in the inflammatory response and cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reduce potential cardiovascular complications in diabetic patients through EAT modification.", "claims": [{"id": "claim-method", "statement": "The DEGs between diabetic and non-diabetic samples were identified using the “DESeq2” package in R using an expression count profile.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36158806", "locator": "sections[5] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The DEGs between diabetic and non-diabetic samples were identified using the “DESeq2” package in R using an expression count profile."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36158806", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes (Figures 2B,C).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36158806", "locator": "sections[12] \"Identification of differentially expressed genes in diabetic epicardial adipose tissue\" paragraph 2", "quote": "Finally, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes (Figures 2B,C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes (Figures 2B,C).", "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:36158806"], "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:GSE108971"], "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:GSE108971"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36158806", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2022.927397", "sha256": "sha256:a12ea8292b007b494ea2d1c46dc563cc40816ff34a327a40245838071342c32b", "bytes": 46736, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2022.927397", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9500152", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE108971", "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=GSE108971", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE108971", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2917168", "GSM2917169", "GSM2917170", "GSM2917171", "GSM2917172", "GSM2917173", "GSM2917174", "GSM2917175", "GSM2917176", "GSM2917177", "GSM2917178", "GSM2917179", "GSM2917180", "GSM2917181", "GSM2917182", "GSM2917183"], "sample_count": 16, "group_sizes": null, "group_sizes_basis": null, "platform": "11154", "taxon": "Homo sapiens", "title": "Novel Atherogenic Pathways from the Differential Transcriptiome Analysis of Diabetic Epicardial Adipose Tissue", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE179455", "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=GSE179455", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE179455", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5418456", "GSM5418457", "GSM5418458", "GSM5418459", "GSM5418460", "GSM5418461", "GSM5418462", "GSM5418463", "GSM5418464", "GSM5418465", "GSM5418466", "GSM5418467", "GSM5418468", "GSM5418469", "GSM5418470", "GSM5418471", "GSM5418472", "GSM5418473", "GSM5418474"], "sample_count": 19, "group_sizes": null, "group_sizes_basis": null, "platform": "20301;24676", "taxon": "Homo sapiens", "title": "Transcriptome analysis of Epicardial Adipose Tissue from obese, diabetic and lean individulas", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE108971 not downloaded", "asset:data:geo:GSE108971 licence not read", "asset:data:geo:GSE179455 not downloaded", "asset:data:geo:GSE179455 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The DEGs between diabetic and non-diabetic samples were identified using the “DESeq2” package in R using an expression count profile."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "PPI"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: deseq2."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE108971", "GSE179455"], "resolved_accessions": ["GSE108971", "GSE179455"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."], "basis": "The paper names GSE108971, GSE179455 in National Center for Biotechnology Information (NCBI) GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:36158806", "data:geo:GSE108971"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE108971"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:06:53Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13577, "completion_tokens": 1629, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36158806", "type": "paper", "label": "pmid:36158806"}, {"id": "source:pmid:36158806", "type": "manuscript", "label": "PMID 36158806 full text"}, {"id": "data:geo:GSE108971", "type": "dataset", "label": "GEO GSE108971"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36158806", "type": "paper", "label": "pmid:36158806"}, {"id": "source:pmid:36158806", "type": "manuscript", "label": "PMID 36158806 full text"}, {"id": "data:geo:GSE108971", "type": "dataset", "label": "GEO GSE108971"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36158806", "type": "extraction", "used": ["source:pmid:36158806"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE108971", "type": "accession_resolution", "used": ["data:geo:GSE108971"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36158806", "parent_hash": "sha256:91975bcefd339688563c6b95c3de29425979c4fe73d352843f2c3437977d74f8", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE108971 licence not read", "asset:data:geo:GSE108971 not downloaded", "asset:data:geo:GSE179455 licence not read", "asset:data:geo:GSE179455 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["using various bioinformatic tools. Methods: RNA-seq datasets GSE108971 and GSE179455 for EAT bet", "analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and"], "order_basis": "paper_text_order:312", "id": "assay:1"}, {"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": ["The DEGs between diabetic and non-diabetic samples were identified using the “DESeq2” package in R using an expression count profile."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}