Integrated analysis and exploration of potential shared gene signatures between carotid atherosclerosis and periodontitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36316672", "kind": "science-record", "revision": 5, "content_hash": "sha256:27d9a9b8e69effcd8a444d233a39b102d0fa828fb1ada738e7b3208e343aa551", "created_at": "2026-11-01T18:15:44Z", "updated_at": "2026-11-01T22:50:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis and exploration of potential shared gene signatures between carotid atherosclerosis and periodontitis.", "doi": "10.1186/s12920-022-01373-y", "pmid": "36316672", "authors": ["Youjie Zeng", "Si Cao", "Minghua Chen"], "journal": "BMC medical genomics", "year": 2022, "abstract": "BACKGROUND: Increasing evidence has suggested an association between carotid atherosclerosis (CAS) and periodontitis (PD); however, the mechanisms have not been fully understood. This study aims to investigate the shared genes and molecular mechanisms underlying the co-pathogenesis of CAS and PD.\nMETHODS: Gene Expression Omnibus (GEO) datasets GSE100927 and GSE10334 were downloaded, and differentially expressed genes (DEGs) shared by both datasets were identified. The functional enrichment analysis of these overlapping DEGs was then conducted. A protein-protein interaction (PPI) network was created using the STRING database and Cytoscape software, and PPI key genes were identified using the cytoHubba plugin. Then, weighted gene co-expression network analysis (WGCNA) was performed on GSE100927 and GSE10334, and the gene modules most correlated with CAS and PD were identified as key modules. The genes in key modules overlapping with PPI key genes were determined to be the key crosstalk genes. Subsequently, the key crosstalk genes were validated in three independent external datasets (GSE43292 [CAS microarray dataset], GSE16134 [PD microarray dataset], and GSE28829 [CAS microarray dataset]). In addition, the immune cell patterns of PD and CAS were evaluated by single-sample gene set enrichment analysis (ssGSEA), and the correlation of key crosstalk genes with each immune cell was calculated. Finally, we investigated the transcription factors (TFs) that regulate key crosstalk genes using NetworkAnalyst 3.0 platform.\nRESULTS: 355 overlapping DEGs of CAS and PD were identified. Functional enrichment analysis highlighted the vital role of immune and inflammatory pathways in CAS and PD. The PPI network was constructed, and eight PPI key genes were identified by cytoHubba, including CD4, FCGR2A, IL1B, ITGAM, ITGAX, LCK, PTPRC, and TNF. By WGCNA, the turquoise module was identified as the most correlated module with CAS, and the blue module was identified as the most correlated module with PD. Ultimately, ITGAM and LCK were identified as key crosstalk genes as they appeared both in key modules and PPI key genes. Expression levels of ITGAM and LCK were significantly elevated in the case groups of the test datasets (GSE100927 and GSE10334) and validation datasets (GSE43292, GSE16134, and GSE28829). In addition, the expression of multiple immune cells was significantly elevated in PD and CAS compared to controls, and the two key crosstalk genes were both significantly associated with CD4 T cells. Finally, SPI1 was identified as a potential key TF, which regulates the two key crosstalk genes.\nCONCLUSION: This study identified the key crosstalk genes and TF in PD and CAS, which provides new insights for further studies on the co-morbidity mechanisms of CAS and PD from an immune and inflammatory perspective.", "claims": [{"id": "claim-method", "statement": "DEGs were identified by the “Limma” R package [16].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36316672", "locator": "sections[4] \"Differentially expression analysis\" paragraph 2", "quote": "DEGs were identified by the “Limma” R package [16]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study are available for download in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo/, (GSE100927, GSE10334, GSE43292, GSE16134, and GSE28829).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36316672", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The datasets analyzed in this study are available for download in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo/, (GSE100927, GSE10334, GSE43292, GSE16134, and GSE28829)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ITGAM and LCK were identified as key crosstalk genes, SPI1 was identified as a potential key transcription factor regulating both genes, and their expression was significantly elevated in the case groups of the test and validation datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36316672", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Ultimately, ITGAM and LCK were identified as key crosstalk genes as they appeared both in key modules and PPI key genes."}, {"asset": "source:pmid:36316672", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Expression levels of ITGAM and LCK were significantly elevated in the case groups of the test datasets (GSE100927 and GSE10334) and validation datasets (GSE43292, GSE16134, and GSE28829)."}, {"asset": "source:pmid:36316672", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, SPI1 was identified as a potential key TF, which regulates the two key crosstalk genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ITGAM and LCK were identified as key crosstalk genes, SPI1 was identified as a potential key transcription factor regulating both genes, and their expression was significantly elevated in the case groups of the test and validation datasets.", "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:36316672"], "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:GSE100927"], "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36316672", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-022-01373-y", "sha256": 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