IFI44L and C1QTNF5 as promising biomarkers of proliferative diabetic retinopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36451477", "kind": "science-record", "revision": 4, "content_hash": "sha256:19b20ae3bf96c6466236b36d94362bffd40330d84259a6095e2b3a40d0ee530e", "created_at": "2026-11-01T19:00:55Z", "updated_at": "2026-11-01T22:50:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "IFI44L and C1QTNF5 as promising biomarkers of proliferative diabetic retinopathy.", "doi": "10.1097/MD.0000000000031961", "pmid": "36451477", "authors": ["Mingxin Shang", "Yao Zhang", "Tongtong Zhang"], "journal": "Medicine", "year": 2022, "abstract": "Proliferative diabetic retinopathy (PDR) is a world-wide leading cause of blindness among adults and may be associated with the influence of genetic factors. It is significant to search for genetic biomarkers of PDR. In our study, we collected genomic data about PDR from gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were carried out. The gene module with the highest gene significance (GS) was defined as the key module. Hub genes were identified by Venn diagram. Then we verified the expression of hub genes in validation data sets and built a diagnostic model by least absolute shrinkage and selection operator (LASSO) regression. Enrichment analysis, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA) and construction of a protein-protein interaction (PPI) network were conducted. In GSE60436, we identified 466 DEGs. WGCNA established 14 gene modules, and the blue module (GS = 0.64), was the key module. Interferon (IFN)-induced protein 44-like (IFI44L) and complement C1q tumor necrosis factor-related protein 5 (C1QTNF5) were identified as hub genes. The expression of hub genes in GEO datasets was verified and a diagnostic model was constructed by LASSO as follows: index = IFI44L * 0.0432 + C1QTNF5 * 0.11246. IFI44L and C1QTNF5 might affect the disease progression of PDR by regulating metabolism-related and inflammatory pathways. IFI44L and C1QTNF5 may play important roles in the disease process of PDR, and a LASSO regression model suggested that the 2 genes could serve as promising biomarkers of PDR.", "claims": [{"id": "claim-method", "statement": "We utilized the R package “limma” to identify the DEGs,[24] by setting the statistical threshold to P < .05 and the absolute value of fold change (FC) > 2.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36451477", "locator": "sections[4] \"2.2. Screening for differentially expressed genes (DEGs)\" paragraph 2", "quote": "We utilized the R package “limma” to identify the DEGs,[24] by setting the statistical threshold to P < .05 and the absolute value of fold change (FC) > 2."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article and its supplementary information files. The raw matrix datasets can be downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36451477", "locator": "sections[22] \"(untitled)\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article and its supplementary information files. The raw matrix datasets can be downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GSE60436 expression data, the study identified 466 differentially expressed genes, applied WGCNA to identify IFI44L and C1QTNF5 as hub genes, and constructed a two-gene LASSO diagnostic model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36451477", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In GSE60436, we identified 466 DEGs."}, {"asset": "source:pmid:36451477", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "WGCNA established 14 gene modules, and the blue module (GS = 0.64), was the key module."}, {"asset": "source:pmid:36451477", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Interferon (IFN)-induced protein 44-like (IFI44L) and complement C1q tumor necrosis factor-related protein 5 (C1QTNF5) were identified as hub genes."}, {"asset": "source:pmid:36451477", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "index = IFI44L * 0.0432 + C1QTNF5 * 0.11246"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using GSE60436 expression data, the study identified 466 differentially expressed genes, applied WGCNA to identify IFI44L and C1QTNF5 as hub genes, and constructed a two-gene LASSO diagnostic model.", "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:36451477"], "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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