Integrated Analysis of DNA methylation and transcriptome profile to identify key features of age-related macular degeneration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34569899", "kind": "science-record", "revision": 5, "content_hash": "sha256:11806854bc1771887c04835969c26ec4777110efadd08e0f814b859a4dfafed1", "created_at": "2026-11-01T15:41:54Z", "updated_at": "2026-11-01T22:04:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Analysis of DNA methylation and transcriptome profile to identify key features of age-related macular degeneration.", "doi": "10.1080/21655979.2021.1976502", "pmid": "34569899", "authors": ["Zhijie Wang", "Yinhua Huang", "Feixue Chu", "Kai Liao", "Zekai Cui", "Jiansu Chen", "Shibo Tang"], "journal": "Bioengineered", "year": 2021, "abstract": "Age-related macular degeneration (AMD) is a common vision-threatening disease. The current study sought to integrate DNA methylation with transcriptome profile to explore key features in AMD. Gene expression data were obtained from the Gene Expression Omnibus (GEO, accession ID: GSE135092) and DNA methylation data were obtained from the ArrayExpress repository (E-MTAB-7183). A total of 456 differentially expressed genes (DEGs) and 4827 intragenic differentially methylated CpGs (DMCs) were identified between AMD and controls. DEGs and DMCs were intersected and 19 epigenetically induced (EI) genes and 15 epigenetically suppressed (ES) genes were identified. Immune cell infiltration analysis was performed to estimate the abundance of different types of immune cell in each sample. Enrichment scores of inflammatory response and tumor necrosis factor-alpha (TNFα) signaling via nuclear factor kappa B (NF-κb) were positively correlated with abundance of activated memory CD4 T cells and M1 macrophages. Subsequently, two significant random forest classifiers were constructed based on DNA methylation and transcriptome data. SMAD2 and NGFR were selected as key genes through functional epigenetic modules (FEM) analysis. Expression level of SMAD2, NGFR and their integrating proteins was validated in hydrogen peroxide (H2O2) and TNFα co-treated retinal pigment epithelium (RPE) in vitro. The findings of the current study showed that local inflammation and systemic inflammatory host response play key roles in pathogenesis of AMD. SMAD2 and NGFR provide new insight in understanding the molecular mechanism and are potential therapeutic targets for development of AMD therapy.", "claims": [{"id": "claim-data-access", "statement": "All data generated during this study are included in this article and its supplementary files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34569899", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "All data generated during this study are included in this article and its supplementary files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:34569899"], "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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"platform": "16791", "taxon": "Homo sapiens", "title": "Integration of eQTL and single-cell transcriptomics in the human eye identifies causal genes for age-related macular degeneration", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:34569899:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:bedfeaea327cf6e0f83676c58f4ae344bed70794c11ecd140e06e1202edb4511", "bytes": 1338, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[20]/table-wrap[1]#t0001", "version": null, "note": "Characteristics of the nine candidate key genes", "structure": {"pmcid": "PMC8806579", "section_index": 20, "section_title": "SMAD2 and NGFR were identified as hub gens through functional epigenetic module analysis", "row_count": 10, "column_count": 4}, "table": {"label": "Table 1.", "caption": "Characteristics of the nine candidate key genes", "rows": [["Gene", "Full name", "Main functions or pathways", "Associated diseases"], ["CD79B", "CD79b molecule", "B cell receptor signaling pathway", "Lymphoma[48,49]"], ["EED", "Embryonic ectoderm development", "Member of the epigenetic regulator gene family, transcriptional repression", "Weaver syndrome, colorectal cancer[50,51]"], ["LCN12", "Lipocalin 12", "Retinoic acid binding, long-chain fatty acid transport", "Keratoconus[52,53]"], ["MAPK12", "Mitogen-activated protein kinase 12", "MAPK signaling pathway", "Cancer[54,55]"], ["NGFR", "Nerve growth factor receptor", "Regulating apoptosis and activity of amyloid-beta", "Alzheimer’s Disease[43,44]"], ["PEX19", "Peroxisomal biogenesis factor 19", "Peroxisome biogenesis", "Peroxisome biogenesis disorders[56]"], ["SMAD2", "SMAD family member 2", "Transforming growth factor-beta signaling pathway", "Colorectal cancer, cardiovascular disease[57,58]"], ["SMURF2", "SMAD specific E3 ubiquitin-protein ligase 2", "SMAD-transforming growth factor-beta signaling", "Cancer[59,60]"], ["XYLT1", "Xylosyltransferase 1", "Biosynthesis of chondroitin sulfate and dermatan sulfate proteoglycans", "Desbuquois dysplasia type II [61]"]], "row_count": 10, "column_count": 4, "numeric_cells": 20, "header_rows": 1, "locator": "section[20]/table-wrap[1]#t0001"}}], "gaps": ["asset:data:geo:E-MTAB-7183 not downloaded", "asset:data:geo:E-MTAB-7183 licence not read", "asset:data:geo:GSE135092 not downloaded", "asset:data:geo:GSE135092 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random 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