Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38903815", "kind": "science-record", "revision": 4, "content_hash": "sha256:2afa24d8efac458ec6affaa0f22f23c4776b1beb15ce58cf4ba6c8aee7d080ec", "created_at": "2026-11-01T20:28:58Z", "updated_at": "2026-11-01T22:54:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.", "doi": "10.3389/fmed.2024.1309510", "pmid": "38903815", "authors": ["Zixuan Wu", "Ling Li", "Tingting Xu", "Yi Hu", "Xin Peng", "Zheyuan Zhang", "Xiaolei Yao", "Qinghua Peng"], "journal": "Frontiers in medicine", "year": 2024, "abstract": "Background: Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated.\nMethods: We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset.\nResults: Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI.\nConclusion: Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.", "claims": [{"id": "claim-method", "statement": "Differential expression analyses between NSOI and control groups were conducted utilizing the Linear Models for Microarray Data (limma) package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38903815", "locator": "sections[4] \"2.2 Transcriptomic data refinement and pre-processing\" paragraph 2", "quote": "Differential expression analyses between NSOI and control groups were conducted utilizing the Linear Models for Microarray Data (limma) package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are publicly available. This data can be found here: Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/, GSE58331 and GSE105149.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38903815", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are publicly available. This data can be found here: Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/, GSE58331 and GSE105149."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Integration of the GSE58331 and GSE105149 datasets identified 314 differentially expressed genes between NSOI and control groups.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38903815", "locator": "sections[14] \"3.1 Identification of DEGs and principal component analysis\" paragraph 2", "quote": "Of the identified 314 Differential Expressed Genes (DEGs), a subset demonstrated significant variations."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Integration of the GSE58331 and GSE105149 datasets identified 314 differentially expressed genes between NSOI and control groups.", "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:38903815"], "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:GSE105149"], "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:GSE105149"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38903815", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2024.1309510", "sha256": "sha256:bada2f96004afacb93c2184d5893122a620d3c66fa01c4d624f62cff36a31890", "bytes": 53680, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2024.1309510", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11188444", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE105149", "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=GSE105149", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE105149", "version": null, "note": "The original contributions presented in the study are publicly available. 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