Identification of Potential Key Biomarkers and Immune Infiltration in Oral Lichen Planus.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35256894", "kind": "science-record", "revision": 6, "content_hash": "sha256:8b02a8f0f7d1deae33ef9f184e16e0cc2c2da4c18bd7dea8ca33c9afff1e2b98", "created_at": "2026-11-01T17:37:01Z", "updated_at": "2026-11-01T22:48:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Key Biomarkers and Immune Infiltration in Oral Lichen Planus.", "doi": "10.1155/2022/7386895", "pmid": "35256894", "authors": ["Lou Geng", "Xingming Zhang", "Yi Tang", "Wenli Gu"], "journal": "Disease markers", "year": 2022, "abstract": "Background: Oral lichen planus (OLP) is a chronic autoimmune oral mucosal disease that seriously affects the life quality of the patients. But till now, the exact etiology and pathogenesis of OLP remain unclear. Our study is aimed at finding the key molecules and pathways involved in the pathogenesis mechanisms of OLP, providing more effective therapeutic strategies for OLP.\nMethods: Data from GSE52130 were downloaded from GEO datasets for analysis. Then, we carried out enrichment analysis of the differentially expressed genes (DEGs) using Gene Ontology (GO) and KEGG pathway analyses. Next, the CIBERSORT algorithm was used to assess immune cell infiltration in OLP patients. Furthermore, we also constructed a protein-protein interaction network using STRING and Cytoscape and simultaneously sought potential transcription factors plug-in including MCODE CytoHubba and iRegulon. In addition, ROC analysis was employed to assess the diagnostic performance of these hub genes. Lastly, we identified 6 promising novel drugs to treat OLP through Connectivity Map.\nResults: We illustrated that 255 DEGs were mainly enriched in the focal adhesion pathway and metabolism pathways. Besides, Cibersort analysis showed that M1 macrophages, T follicular helper cells, and T regulatory cells are more infiltrated in OLP samples. In addition, ROC analysis demonstrated that these hub genes owned higher diagnostic value in OLP, in which SPRR1B had the highest diagnostic value. And we also predicted that SOX7 was the most relevant transcription factor of those hub genes. Lastly, through the CMap database, we identified 6 small molecules as possible treatment drugs of OLP.\nConclusion: Our research identified that SPRR1B could be used as potential biomarkers for the early diagnosis of OLP. In addition, as a chronic autoimmune oral mucosal disease, OLP has different infiltration types of immune cells. Furthermore, 6 small molecules were proposed as promising novel treatment drugs for OLP patients. Therefore, our research may provide new impetus for the development of effective OLP biological treatment options.", "claims": [{"id": "claim-method", "statement": "Next, we normalized and processed the original expression matrix using R. After that, we also selected out differentially expressed genes (DEGs) by the limma package [7], based on the criteria: at least a 1.5-fold change between COE samples and OLPE samples and with adjusted P value < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35256894", "locator": "sections[4] \"2.2. Data Further Process\" paragraph 2", "quote": "Next, we normalized and processed the original expression matrix using R. After that, we also selected out differentially expressed genes (DEGs) by the limma package [7], based on the criteria: at least a 1.5-fold change between COE samples and OLPE samples and with adjusted P value < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35256894", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SPRR1B had the highest diagnostic value among the 10 hub genes, with an AUC of 0.837.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35256894", "locator": "sections[20] \"3.8. Construction and Validation of the Prognostic Model\" paragraph 2", "quote": "It was shown that those hub genes owned higher diagnostic value in OLP, in which SPRR1B had the highest diagnostic value (AUC: 0.837)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "SPRR1B had the highest diagnostic value among the 10 hub genes, with an AUC of 0.837.", "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:35256894"], "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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