Patients with low nicotinamide N-methyltransferase expression benefit significantly from bevacizumab treatment in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33446144", "kind": "science-record", "revision": 7, "content_hash": "sha256:714b961efd86611d948436a4f8af242e602254871bd5c63cc94b1ceb6f85de19", "created_at": "2026-11-01T15:41:22Z", "updated_at": "2026-11-01T22:02:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Patients with low nicotinamide N-methyltransferase expression benefit significantly from bevacizumab treatment in ovarian cancer.", "doi": "10.1186/s12885-021-07785-w", "pmid": "33446144", "authors": ["Jun Li", "Huiran Yue", "Hailin Yu", "Xin Lu", "Xiaohong Xue"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: The role of nicotinamide N-methyltransferase (NNMT) in ovarian cancer is still elusive. Our aim is to explore the expression of NNMT in ovarian cancer and to assess its association with patient prognosis and treatment response.\nMETHODS: We first analyzed the differential expression of NNMT among fallopian tube epithelium, primary ovarian cancers, metastatic ovarian cancers, and recurrent ovarian cancers using Gene Expression Ominus (GEO) database (GSE10971, GSE30587, GSE44104 and TCGA datasets). Then, we assessed the association of NNMT expression with clinical and molecular parameters using CSIOVDB database and GSE28739 dataset. Next, we evaluate the association of NNMT expression with the prognosis of ovarian cancer patients in both GSE9891 dataset and TCGA dataset. Finally, GSE140082 dataset was used to explore the association of NNMT expression with bevacizumab response.\nRESULTS: NNMT expression was significantly elevated in lymphovascular space invasion (LVSI)-positive ovarian cancers compared with that in LVSI-negative ovarian cancers (TCGA dataset, P < 0.05), Moreover, increased expression of NNMT was associated with increased tumor stage, grade, and mesenchymal molecular subtype (CSIOVDB database). Survival analysis indicated that increased expression of NNMT was associated with a reduced OS in both GSE9891 dataset (HR: 2.28, 95%CI: 1.51-3.43, Log-rank P < 0.001) and TCGA dataset (HR: 1.55, 95%CI: 1.02-2.36, Log-rank P = 0.039). Multivariate analysis further confirmed the negative impact of NNMT expression on OS in ovarian cancer patients in those two datasets. Furthermore, the NNMT-related nomogram showed that NNMT shared a larger contribution to OS, compared with debulking status. More interestingly, bevacizumab conferred significant improvements in OS for patients with low NNMT expression (HR: 0.56, 95%CI: 0.31-0.99, Log-rank P = 0.049). In contrast, patients with high NNMT expression didn't benefit from bevacizumab treatment significantly (HR: 0.85, 95%CI: 0.48-1.49, Log-rank P = 0.561). NNMT expression was positively correlated with the expression of genes, LDHA and PGAM1, involved in Warburg effect.\nCONCLUSIONS: In conclusion, NNMT expression is associated with the aggressive behavior of ovarian cancer, correlates with a poor prognosis, and is predictive of sensitivity to bevacizumab treatment.", "claims": [{"id": "claim-method", "statement": "Kaplan-Meier analysis of OS for combination therapy of standard treatment and bevacizumab vs. standard treatment alone in ovarian cancer patients stratified by NNMT expression was performed in GSE140082 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33446144", "locator": "sections[3] \"Database used in present study\" paragraph 5", "quote": "Kaplan-Meier analysis of OS for combination therapy of standard treatment and bevacizumab vs. standard treatment alone in ovarian cancer patients stratified by NNMT expression was performed in GSE140082 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in GEO database and TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33446144", "locator": "sections[17] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analysed during the current study are available in GEO database and TCGA database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "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:33446144"], "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:GSE10971"], "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:GSE10971"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33446144", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-07785-w", "sha256": 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