Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33777111", "kind": "science-record", "revision": 7, "content_hash": "sha256:3636ceb97112e52693f5884dc80c5e7e8fd711e6be010540400c9fc4018fbf46", "created_at": "2026-11-01T15:41:30Z", "updated_at": "2026-11-01T22:03:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.", "doi": "10.3389/fgene.2021.648329", "pmid": "33777111", "authors": ["Wanchen Ning", "Aneesha Acharya", "Zhengyang Sun", "Anthony Chukwunonso Ogbuehi", "Cong Li", "Shiting Hua", "Qianhua Ou", "Muhui Zeng", "Xiangqiong Liu", "Yupei Deng", "Rainer Haak", "Dirk Ziebolz", "Gerhard Schmalz", "George Pelekos", "Yang Wang", "Xianda Hu"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "BACKGROUND: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.\nMETHODS: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.\nRESULTS: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three \"master\" immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.\nCONCLUSION: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.", "claims": [{"id": "claim-method", "statement": "A receiver operating characteristic (ROC) curve was plotted for the model using the “pROC” (Robin et al., 2011) and the “ggplot2” packages in R5.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33777111", "locator": "sections[10] \"Evaluation of the SVM Classifier\" paragraph 2", "quote": "A receiver operating characteristic (ROC) curve was plotted for the model using the “pROC” (Robin et al., 2011) and the “ggplot2” packages in R5."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33777111", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33777111"], "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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