Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38711158", "kind": "science-record", "revision": 3, "content_hash": "sha256:0425ca77ddf79ef184454bbbb1fd2bc21c3bc714bfaee1cfb1d4574c1c7cfe62", "created_at": "2026-11-01T19:20:40Z", "updated_at": "2026-11-01T22:11:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma.", "doi": "10.1186/s12967-024-05146-2", "pmid": "38711158", "authors": ["Junwen Gan", "Meng Huang", "Weishi Wang", "Guining Fu", "Mingyuan Hu", "Hongcheng Zhong", "Xin Ye", "Qingdong Cao"], "journal": "Journal of translational medicine", "year": 2024, "abstract": "BACKGROUND: Lung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis.\nMETHODS: Enzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed.\nRESULTS: Methylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD.\nCONCLUSIONS: This study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression analyses was performed to construct and evaluate the prognostic predicated models using “glmnet(v4.1-7)” [21, 22] and “survival(v3.5-5) [23]” packages of the R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38711158", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "LASSO Cox regression analyses was performed to construct and evaluate the prognostic predicated models using “glmnet(v4.1-7)” [21, 22] and “survival(v3.5-5) [23]” packages of the R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Methylation data (single-site beta value) and RNA-seq data (expression count) with clinical information from TCGA program were downloaded from Genomic Data Commons data portal (https://portal.gdc.cancer.gov/). 15 patients from TCGA-LUAD cohort with both primary tumor as well as solid normal tissue samples and without any treatment or therapy were selected for our study as a comparison of our in-house methylation dataset. The clinical features of the 503 patients used to train the prognostic model, which includes 270 stage I LUAD patients. GSE42127 and GSE13213 dataset from Gene Expression Omnibus (GEO) were used as the validation data, which contained 87 and 79 stage I LUAD patients (Supplementary: Table S1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38711158", "locator": "sections[27] \"Data availability\" paragraph 2", "quote": "Methylation data (single-site beta value) and RNA-seq data (expression count) with clinical information from TCGA program were downloaded from Genomic Data Commons data portal (https://portal.gdc.cancer.gov/). 15 patients from TCGA-LUAD cohort with both primary tumor as well as solid normal tissue samples and without any treatment or therapy were selected for our study as a comparison of our in-house methylation dataset. The clinical features of the 503 patients used to train the prognostic model, which includes 270 stage I LUAD patients. GSE42127 and GSE13213 dataset from Gene Expression Omnibus (GEO) were used as the validation data, which contained 87 and 79 stage I LUAD patients (Supplementary: Table S1)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38711158", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset.", "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:38711158"], "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:GSE13213"], "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:GSE13213"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38711158", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-024-05146-2", "sha256": "sha256:5dba4d2975ea397dd9790c266577466ffada2815917911407c031d0b97b0b5c5", "bytes": 59519, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-024-05146-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11075300", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13213", "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=GSE13213", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13213", "version": null, "note": "Methylation data (single-site beta value) and RNA-seq data (expression count) with clinical information from TCGA program were downloaded from Genomic Data Commons data portal (https://portal.gdc.cancer.gov/). 15 patients from TCGA-LUAD cohort with both primary tumor as well as solid normal tissue samples and without any treatment or therapy were selected for our study as a comparison of our in-house methylation dataset. The clinical features of the 503 patients used to train the prognostic model, which includes 270 stage I LUAD patients. GSE42127 and GSE13213 dataset from Gene Expression Omnibus (GEO) were used as the validation data, which contained 87 and 79 stage I LUAD patients (Supplementary: Table S1).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Methylation data (single-site beta value) and RNA-seq data (expression count) with clinical information from TCGA program were downloaded from Genomic Data Commons data portal (https://portal.gdc.cancer.gov/). 15 patients from TCGA-LUAD cohort with both primary tumor as well as solid normal tissue samples and without any treatment or therapy were selected for our study as a comparison of our in-house methylation dataset. The clinical features of the 503 patients used to train the prognostic model, which includes 270 stage I LUAD patients. 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Then, an integr", "s used for the follow-up analyses about TCGA 450K data processing. First, probes that belong t", "number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentiall", "nd functional analysis of MEGs in EM-seq and 450K microarray After DMR-related gene annotation"], "order_basis": "paper_text_order:636", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was perf", "r of CpG sites and DMRs compared to the 450K microarray. 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