RNA modification regulator DDC in endometrial cancer affects the tumor microenvironment and patient prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37872211", "kind": "science-record", "revision": 2, "content_hash": "sha256:ccfe517358d4ceb64880ea4e26aee5b0961a7a65d6d1caba6808b63e3fe3ef5e", "created_at": "2026-11-01T19:02:43Z", "updated_at": "2026-11-01T21:31:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "RNA modification regulator DDC in endometrial cancer affects the tumor microenvironment and patient prognosis.", "doi": "10.1038/s41598-023-44269-2", "pmid": "37872211", "authors": ["Huai Zhao", "Chuang Shi", "Guoguang Zhao", "Jiamin Liu", "Xi Wang", "Jie Liang", "Fangmei Li"], "journal": "Scientific reports", "year": 2023, "abstract": "Uterine corpus endometrial carcinoma (UCEC) is infiltrated by immune cells, which are involved in the growth and proliferation of malignant tumors and resistance to immunotherapy. This study suggested that RNA modification regulators played an important role in the development and prognosis of UCEC. Many studies confirmed that RNA modification played an essential role in tumor immune regulation, and abnormal RNA modification contributed to tumorigenesis and cancer progression. Based on the RNA modification regulatory factors, the UCEC samples from TCGA (The Cancer Genome Atlas) were classified into two clusters, namely Cluster A and Cluster B, using unsupervised consensus clustering. We obtained DEG (differentially expressed genes) between the two clusters, and constructed a risk model of RNA modification-related genes using DEGs. Cluster A had lower RNA modification regulatory factors, richer immune cell infiltration, and better prognosis. The differentially expressed genes between the two clusters were obtained, and these genes were used for modeling. This model divided patients with UCEC into two groups. The low-risk group had better immune infiltration, and the ROC (receiver operating characteristic) curve showed that this model had good predictive efficacy. The low-risk group had a better response to immunotherapy by immune checkpoint prediction. We obtained the key gene L-dopa decarboxylase (DDC) through the intersection of LASSO model genes and GEO dataset GSE17025. We evaluated the potential biological functions of DDC. The differences in the expression of DDC were verified by immunohistochemistry. We evaluated the relationship between DDC and immune cell infiltration and verified this difference using immunofluorescence. Cluster A with low expression of RNA modification regulators has better prognosis and richer immune cell infiltration, therefore, we believed that RNA modification regulators in UCEC were closely related to the tumor microenvironment. Also, the risk score could well predict the prognosis of patients and guide immunotherapy, which might benefit patients with UCEC.", "claims": [{"id": "claim-method", "statement": "The LASSO Cox regression model was used to select the best prognostic genes from the key genes in the training group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37872211", "locator": "sections[17] \"Prediction of prognosis in endometrial cancer by RNA modification-associated risk models\" paragraph 2", "quote": "The LASSO Cox regression model was used to select the best prognostic genes from the key genes in the training group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37872211", "locator": "sections[33] \"notes\" paragraph 1", "quote": "All data generated or analysed during this study are included in this article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The RNA modification-associated risk model had good predictive power for prognosis in UCEC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37872211", "locator": "sections[17] \"Prediction of prognosis in endometrial cancer by RNA modification-associated risk models\" paragraph 2", "quote": "This indicated that the model could be extended to other UCEC data and had good predictive power."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The RNA modification-associated risk model had good predictive power for prognosis in UCEC.", "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:37872211"], "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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