Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35741697", "kind": "science-record", "revision": 5, "content_hash": "sha256:8f37ee619608de4bfbbb82462b89b81db9761ad2ce23fcb8d0f273278948627e", "created_at": "2026-11-01T18:14:45Z", "updated_at": "2026-11-01T22:49:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases.", "doi": "10.3390/genes13060935", "pmid": "35741697", "authors": ["Dongli Zhao", "Zhe Zhang", "Zhonghuang Wang", "Zhenglin Du", "Meng Wu", "Tingting Zhang", "Jialu Zhou", "Wenming Zhao", "Yuanguang Meng"], "journal": "Genes", "year": 2022, "abstract": "Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.", "claims": [{"id": "claim-method", "statement": "The 96 DEGs across the EC and para-cancer samples in the training group were calculated using the R package “limma”, which employed the empirical Bayesian method and the moderated Wilcox test to assess differences in gene expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35741697", "locator": "sections[4] \"2.2. 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Data Collection and Pre-Processing\" paragraph 2", "quote": "The gene expression datasets GSE63678, GSE106191, and GSE115810 were obtained from GEO (https://www.ncbi.nlm.nih.gov/geo/) (accessed on 3 March 2022), merged, and corrected for the batch effect to constitute the training group."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35741697", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively.", "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:35741697"], "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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