Identification of Feature Genes of a Novel Neural Network Model for Bladder Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35719407", "kind": "science-record", "revision": 5, "content_hash": "sha256:15cbc082d39903594122176959354974bf8b959ac33550d088b118f9c97c1381", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:49:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Feature Genes of a Novel Neural Network Model for Bladder Cancer.", "doi": "10.3389/fgene.2022.912171", "pmid": "35719407", "authors": ["Yongqing Zhang", "Shan Hua", "Qiheng Jiang", "Zhiwen Xie", "Lei Wu", "Xinjie Wang", "Fei Shi", "Shengli Dong", "Juntao Jiang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: The combination of deep learning methods and oncogenomics can provide an effective diagnostic method for malignant tumors; thus, we attempted to construct a reliable artificial neural network model as a novel diagnostic tool for Bladder cancer (BLCA). Methods: Three expression profiling datasets (GSE61615, GSE65635, and GSE100926) were downloaded from the Gene Expression Omnibus (GEO) database. GSE61615 and GSE65635 were taken as the train group, while GSE100926 was set as the test group. Differentially expressed genes (DEGs) were filtered out based on the logFC and FDR values. We also performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the biological functions of the DEGs. Consequently, we utilized a random forest algorithm to identify feature genes and further constructed a neural network model. The test group was given the same procedures to validate the reliability of the model. We also explored immune cells' infiltration degree and correlation coefficients through the CiberSort algorithm and corrplot R package. The qRT-PCR assay was implemented to examine the expression level of the feature genes in vitro. Results: A total of 265 DEGs were filtered out and significantly enriched in muscle system processes, collagen-containing and focal adhesion signaling pathways. Based on the random forest algorithm, we selected 14 feature genes to construct the neural network model. The area under the curve (AUC) of the training group was 0.950 (95% CI: 0.850-1.000), and the AUC of the test group was 0.667 (95% CI: 0.333-1.000). Besides, we observed significant differences in the content of immune infiltrating cells and the expression levels of the feature genes. Conclusion: After repeated verification, our neural network model had clinical feasibility to identify bladder cancer patients and provided a potential target to improve the management of BLCA.", "claims": [{"id": "claim-method", "statement": "The criteria were formulated as logFC ≥2 or ≤ −2 and p value <0.05 to filter out DEGs through the limma R package (Version 3.30.7).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35719407", "locator": "sections[4] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "The criteria were formulated as logFC ≥2 or ≤ −2 and p value <0.05 to filter out DEGs through the limma R package (Version 3.30.7)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35719407", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 265 differentially expressed genes were identified and enriched in muscle system processes, collagen-containing and focal adhesion signaling pathways.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35719407", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 265 DEGs were filtered out and significantly enriched in muscle system processes, collagen-containing and focal adhesion signaling pathways."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 265 differentially expressed genes were identified and enriched in muscle system processes, collagen-containing and focal adhesion signaling pathways.", "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:35719407"], "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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