CXCR3 predicts the prognosis of endometrial adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36750966", "kind": "science-record", "revision": 3, "content_hash": "sha256:5a21c9456bff1be384ec9bf7be94a5e3b370405dd3f16c3d7ea58701b47f7f49", "created_at": "2026-11-01T19:01:19Z", "updated_at": "2026-11-01T22:08:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CXCR3 predicts the prognosis of endometrial adenocarcinoma.", "doi": "10.1186/s12920-023-01451-9", "pmid": "36750966", "authors": ["He Dong", "Mengzi Sun", "Hua Li", "Ying Yue"], "journal": "BMC medical genomics", "year": 2023, "abstract": "OBJECTIVES: Currently, endometrial adenocarcinoma lacks an effective prognostic indicator. This study was to develop and validate a gene biomarker and a nomogram to predict the survival of endometrial adenocarcinoma, explore potential mechanisms and select sensitive drugs.\nMETHODS: 425 endometrial adenocarcinoma cases with RNA sequencing data from TCGA were used to identify the most immune-related module by WGCNA. As an external test set, 103 cases from GSE17025 were used. Immune-related genes were downloaded from Innate DB. The three sets of data were used to identify the prognostic genes. Based on 397 cases with complete clinical data from TCGA, randomly divided into the training set (n = 199) and test set (n = 198), we identified CXCR3 as the prognostic gene biomarker. Age, grade, FIGO stage, and risk were used to develop and validate a predictive nomogram. AUC, C-index, calibration curve and K-M estimate evaluated the model's predictive performance. KEGG enrichment analysis, immune functions, TMB, the effectiveness of immunotherapy, and drug sensitivity between the high-risk and low-risk groups.\nRESULTS: CXCR3 was identified as a prognostic biomarker. We calculated the risk score and divided the cases into the high-risk and low-risk groups by the median value of the risk score. The OS of the high-risk group was better than the low-risk group. The risk was the prognostic indicator independent of age, grade, and FIGO stage. We constructed the nomogram including age, grade, FIGO stage, and risk to predict the prognosis of endometrial adenocarcinoma. The top five KEGG pathways enriched by the DEGs between the high- and low-risk groups were viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interaction, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and cell adhesion molecules. We analyzed the difference in immune cells and found that CD8+ T cells, activated CD4+ T cells, T helper cells, monocytes, and M1 macrophages were infiltrated more in the low-risk group. However, M0 macrophages and activated dendritic cells were more in the high-risk group. The immune function including APC coinhibition, APC costimulation, CCR, checkpoint, cytolytic activity, HLA, inflammation-promoting, MHC-I, parainflammation, T cell coinhibition, T cell costimulation, type I-IFN-response, and type II-IFN-response were better in the low-risk group. TMB and TIDE scores were both better in the low-risk group. By 'the pRRophetic' package, we found 56 sensitive drugs for different risk groups.\nCONCLUSION: We identified CXCR3 as the prognostic biomarker. We also developed and validated a predictive nomogram model combining CXCR3, age, histological grade, and FIGO stage for endometrial adenocarcinoma, which could help explore the precise treatment.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were successively used to obtain the prognostic gene biomarker on the training set and tested by the test set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36750966", "locator": "sections[5] \"Identification and validation of the most immune-related gene biomarker\" paragraph 2", "quote": "Univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were successively used to obtain the prognostic gene biomarker on the training set and tested by the test set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during this study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse17025).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36750966", "locator": "sections[31] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analyzed during this study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse17025)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified CXCR3 as the prognostic biomarker.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36750966", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We identified CXCR3 as the prognostic biomarker."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We identified CXCR3 as the prognostic biomarker.", "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:36750966"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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