Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35003389", "kind": "science-record", "revision": 5, "content_hash": "sha256:a403c565c7488e430890d16c8c9e101fe55bbc8c002c453203acce6306b0ba09", "created_at": "2026-11-01T17:36:43Z", "updated_at": "2026-11-01T22:48:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis.", "doi": "10.1155/2021/5074305", "pmid": "35003389", "authors": ["Yongbin Jing", "Dong Han", "Chunyang Xi", "Jinglong Yan", "Jinpeng Zhuang"], "journal": "Disease markers", "year": 2021, "abstract": "BACKGROUND: The current study is aimed at identifying the cross-talk genes between periodontitis (PD) and rheumatoid arthritis (RA), as well as the potential relationship between cross-talk genes and pyroptosis-related genes.\nMETHODS: Datasets for the PD (GSE106090, GSE10334, GSE16134) and RA (GSE55235, GSE55457, GSE77298, and GSE1919) were downloaded from the GEO database. After batch correction and normalization of datasets, differential expression analysis was performed to identify the differentially expressed genes (DEGs). The cross-talk genes linking PD and RA were obtained by overlapping the DEGs dysregulated in PD and DEGs dysregulated in RA. Genes involved in pyroptosis were summarized by reviewing literatures, and the correlation between pyroptosis genes and cross-talk genes was investigated by Pearson correlation coefficient. Furthermore, the weighted gene coexpression network analysis (WGCNA) was carried out to identify the significant modules which contained both cross-talk genes and pyroptosis genes in both PD data and RA data. Thus, the core cross-talk genes were identified from the significant modules. Receiver-operating characteristic (ROC) curve analysis was performed to identify the predictive accuracy of these core cross-talk genes in diagnosing PD and RA. Based on the core cross-talk genes, the experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed.\nRESULTS: A total of 40 cross-talk genes were obtained. Most of the pyroptosis genes were not differentially expressed in disease and normal samples. By selecting the modules containing both cross-talk genes or pyroptosis genes, the blue module was identified to be significant module. Three genes, i.e., cross-talk genes (TIMP1, LGALS1) and pyroptosis gene-GPX4, existed in the blue module of PD network, while two genes (i.e., cross-talk gene-VOPP1 and pyroptosis gene-AIM2) existed in the blue module of RA network. ROC curve analysis showed that three genes (TIMP1, VOPP1, and AIM2) had better predictive accuracy in diagnosing disease compared with the other two genes (LGALS1 and GPX4).\nCONCLUSIONS: This study revealed shared mechanisms between RA and PD based on cross-talk and pyroptosis genes, supporting the relationship between the two diseases. Thereby, five modular genes (TIMP1, LGALS1, GPX4, VOPP1, and AIM2) could be of relevance and might serve as potential biomarkers. These findings are a basis for future research in the field.", "claims": [{"id": "claim-method", "statement": "After batching correction, the differential expression analysis (DEA) was performed on the corrected PD and RA data by using limma package (version 3.9) of R program (version 3.6.3) [24]. The Pearson correlation coefficient (r) values were calculated by using the “corrplot” package (version 0.90) [29] of R program (version 3.6.3). The weighted gene coexpression network analysis (WGCNA) was performed to identify the significant modules that contain both cross-talk genes and pyroptosis-related genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35003389", "locator": "sections[6] \"2.4. Differential Expression Analysis\" paragraph 2", "quote": "After batching correction, the differential expression analysis (DEA) was performed on the corrected PD and RA data by using limma package (version 3.9) of R program (version 3.6.3) [24]."}, {"asset": "source:pmid:35003389", "locator": "sections[8] \"2.6. Correlation between Cross-Talk Genes and Pyroptosis-Related Genes\" paragraph 4", "quote": "The Pearson correlation coefficient (r) values were calculated by using the “corrplot” package (version 0.90) [29] of R program (version 3.6.3)."}, {"asset": "source:pmid:35003389", "locator": "sections[10] \"2.8. Weighted Gene Coexpression Network Analysis\" paragraph 2", "quote": "The weighted gene coexpression network analysis (WGCNA) was performed to identify the significant modules that contain both cross-talk genes and pyroptosis-related genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35003389", "locator": "sections[28] \"Data Availability\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 40 cross-talk genes were obtained.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35003389", "locator": "sections[17] \"3.3. Identification of Cross-Talk Genes\" paragraph 2", "quote": "By obtaining the intersection of DEGs dysregulated between PD and RA, a total of 40 cross-talk genes were obtained."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 40 cross-talk genes were obtained.", "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:35003389"], "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:GSE10334"], "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:GSE10334"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35003389", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/5074305", "sha256": 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