Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37178414", "kind": "science-record", "revision": 2, "content_hash": "sha256:52f811c3b95ba77cdead7be601212f4d3565f014fcf7c6077d53ff5002436b44", "created_at": "2026-11-01T19:01:51Z", "updated_at": "2026-11-01T21:30:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation.", "doi": "10.1007/s12282-023-01458-1", "pmid": "37178414", "authors": ["Leilei Li", "Wenhui Yang", "Daqi Jia", "Shiqi Zheng", "Yuzhe Gao", "Guanghui Wang"], "journal": "Breast cancer (Tokyo, Japan)", "year": 2023, "abstract": "OBJECTIVES: Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct.\nMETHODS: The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).\nRESULTS: Eighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.\nCONCLUSIONS: An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.", "claims": [{"id": "claim-method", "statement": "Then, the risk model was built by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) logistic regression [35].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37178414", "locator": "sections[8] \"Construction and validation of the m1A-related prognosis model\" paragraph 2", "quote": "Then, the risk model was built by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) logistic regression [35]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available on request from the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37178414", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The data that support the findings of this study are available on request from the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37178414", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.", "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:37178414"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37178414", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12282-023-01458-1", "sha256": 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METHODS: The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleo", "ed genes in BRCA is indistinct. METHODS: The RNA sequencing (RNA-seq), copy-number variation (CNV), sing"], "order_basis": "paper_text_order:369", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ent between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1", "confirmed by quantitative real-time PCR (RT‒qPCR). RESULTS: Eighty-five differentially expres", "between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1", "mples were further confirmed by quantitative real-time PCR (RT‒qPCR). RESULTS: Eighty-five differential"], "order_basis": "paper_text_order:1667", "id": "assay:2"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, the risk model was built by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) logistic regression [35]."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}