Integrating molecular pathway with genome-wide association data for causality identification in breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38954227", "kind": "science-record", "revision": 2, "content_hash": "sha256:fff9ea79b9d77c3628f32e9a75ed7cc74c886599ab015e89852d28494be8c282", "created_at": "2026-11-01T20:29:01Z", "updated_at": "2026-11-01T21:33:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating molecular pathway with genome-wide association data for causality identification in breast cancer.", "doi": "10.1007/s12672-024-01125-7", "pmid": "38954227", "authors": ["Yan-Shuang Li", "Hong-Chuan Jiang"], "journal": "Discover oncology", "year": 2024, "abstract": "OBJECTIVE: The study purpose was to explore the causal association between pyruvate metabolism and breast cancer (BC), as well as the molecular role of key metabolic genes, by using bioinformatics and Mendelian randomization (MR) analysis.\nMETHODS: We retrieved and examined diverse datasets from the GEO database to ascertain differentially acting genes (DAGs) in BC via differential expression analysis. Following this, we performed functional and pathway enrichment analyses to ascertain noteworthy molecular functions and metabolic pathways in BC. Employing MR analysis, we established a causal association between pyruvate metabolism and the susceptibility to BC. Additionally, utilizing the DGIdb database, we identified potential targeted medications that act on genes implicated in the pyruvate metabolic pathway and formulated a competing endogenous RNA (ceRNA) regulatory network in BC.\nRESULTS: We collected the datasets GSE54002, GSE70947, and GSE22820, and identified a total of 1127 DEGs between the BC and NC groups. GO and KEGG enrichment analysis showed that the molecular functions of these DEGs mainly included mitotic nuclear division, extracellular matrix, signaling receptor activator activity, etc. Metabolic pathways were mainly concentrated in PI3K-Akt signaling pathway, Cytokine-cytokine receptor binding and Pyruvate, Tyrosine, Propanoate and Phenylalanine metabolism, etc. In addition, MR analysis demonstrated a causal relationship between pyruvate metabolism and BC risk. Finally, we constructed a regulatory network between pathway genes (ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C) and targeted drugs, as well as a ceRNA (lncRNA-miRNA-mRNA) regulatory network for BC, further revealing their interactions.\nCONCLUSIONS: Our research revealed a causal association between pyruvate metabolism and BC risk, found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C takes place an important part in the development of BC in the molecular mechanisms related to pyruvate metabolism, and identified some potential targeted small molecule drugs.", "claims": [{"id": "claim-method", "statement": "The MR analysis was conducted using the \"TwoSampleMR\" package, with the inverse variance weighted (IVW) method employed to examine the association among the activity level of Pyruvate metabolism and the risk of BC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38954227", "locator": "sections[7] \"Mendelian randomization analysis\" paragraph 2", "quote": "The MR analysis was conducted using the \"TwoSampleMR\" package, with the inverse variance weighted (IVW) method employed to examine the association among the activity level of Pyruvate metabolism and the risk of BC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data are available at the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (GSE42568), TCGA database (http://cancergenome.nih.gov/), and TCIA database (https://tcia.at/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38954227", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The data are available at the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (GSE42568), TCGA database (http://cancergenome.nih.gov/), and TCIA database (https://tcia.at/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:38954227"], "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:GSE22820"], "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:GSE22820"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38954227", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01125-7", "sha256": 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