NEAT1/MALAT1/XIST/PKD--Hsa-Mir-101-3p--DLGAP5 Axis as a Novel Diagnostic and Prognostic Biomarker Associated With Immune Cell Infiltration in Bladder Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35873473", "kind": "science-record", "revision": 4, "content_hash": "sha256:a9636ae9994076e9d0b1af54a44e2419091b505044cc7a7a38af35600cbba511", "created_at": "2026-11-01T18:14:57Z", "updated_at": "2026-11-01T22:07:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "NEAT1/MALAT1/XIST/PKD--Hsa-Mir-101-3p--DLGAP5 Axis as a Novel Diagnostic and Prognostic Biomarker Associated With Immune Cell Infiltration in Bladder Cancer.", "doi": "10.3389/fgene.2022.892535", "pmid": "35873473", "authors": ["Xiaosheng Rao", "Haiyan Cao", "Qingfeng Yu", "Xiuyu Ou", "Ruiqi Deng", "Jinkun Huang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: The clinical value of the biomarkers of bladder cancer (BC) is limited due to their low sensitivity or specificity. As a biomarker, DLG associated protein 5 (DLGAP5) is a potential cell cycle regulator in cancer cell carcinogenesis. However, its functional part in BC remains unclear. Therefore, this study aims to identify DLGAP5 expression in BC and its potential diagnostic and prognostic values. Eventually, it predicts the possible RNA regulatory pathways of BC. Methods: Data on DLGAP5 expression levels in BC and normal bladder tissues were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The receiver operating characteristic (ROC), Kaplan-Meier survival curves, and the univariate and multivariate Cox regression analysis determined the diagnostic and prognostic values of DLGAP5 in BC patients. Finally, the StarBase predicted the target RNAs and constructed networks using Cytoscape. Results: DLGAP5 expression was significantly upregulated in BC tissue, verified by the TCGA (p < 0.001), GSE3167, GSE7476, and GSE65635 datasets (p < 0.01). BC patients with increased DLGAP5 had poor overall survival (OS) (p = 0.01), disease specific survival (DSS) (p = 0.006) and progress free interval (DFI) (p = 0.007). The area under the ROC curve (AUC) was 0.913. The multivariate Cox analysis identified that lymphovascular invasion (p = 0.007) and DLGAP5 (p = 0.002) were independent prognostic factors. Conclusion: Increased DLGAP5 expression was closely associated with a poor prognosis in BC patients. In this case, DLGAP5 might be a diagnostic and prognostic biomarker for BC. DLGAP5 expression might be regulated by NEAT1/MALAT1/XIST/PKD--Hsa-mir-101-3p pathways.", "claims": [{"id": "claim-method", "statement": "The Receiver Operating Characteristic (ROC) survival curve was constructed to analyze the diagnostic values of DLGAP5 in BC patients. Firstly, The Kaplan–Meier (KM) method constructed the overall survival (OS) curve, the disease specific survival (DSS) curve, and the progress-free interval (PFI) curve, illustrating the prognostic association between DLGAP5 expression level and OS, DSS, and PFI. Secondly, the univariate and multivariate Cox regression analysis analyzed the prognostic values of DLGAP5 in BC patients; when p < 0.05, the Cox regression analysis result is statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35873473", "locator": "sections[4] \"Diagnostic and Prognostic Analyses of DLGAP5\" paragraph 2", "quote": "The Receiver Operating Characteristic (ROC) survival curve was constructed to analyze the diagnostic values of DLGAP5 in BC patients. Firstly, The Kaplan–Meier (KM) method constructed the overall survival (OS) curve, the disease specific survival (DSS) curve, and the progress-free interval (PFI) curve, illustrating the prognostic association between DLGAP5 expression level and OS, DSS, and PFI. Secondly, the univariate and multivariate Cox regression analysis analyzed the prognostic values of DLGAP5 in BC patients; when p < 0.05, the Cox regression analysis result is statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35873473", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "High DLGAP5 expression was associated with poor prognosis in bladder cancer and showed strong diagnostic performance, with an ROC AUC of 0.913.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35873473", "locator": "sections[16] \"Diagnosis and Prognosis Analysis\" paragraph 2", "quote": "The KM survival curves of OS (p = 0.01), DSS (p = 0.006), and PFI (p = 0.007) depicted that the increased DLGAP5 expression was associated with poor prognosis for BC patients (Figures 2A–C). The ROC survival curve was constructed to analyze the diagnostic values of DLGAP5 in BC patients. The result identified that the ROC curve (AUC) area was 0.913 (Figure 2D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "High DLGAP5 expression was associated with poor prognosis in bladder cancer and showed strong diagnostic performance, with an ROC AUC of 0.913.", "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:35873473"], "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:GSE3167"], "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:GSE3167"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35873473", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.892535", "sha256": "sha256:aa3685096ffb75fed745c27d766cba56d676bf3025baac464934da690ee897e0", "bytes": 31427, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.892535", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9305813", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE3167", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3167", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE3167", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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Firstly, The Kaplan–Meier (KM) method constructed the overall survival (OS) curve, the disease specific survival (DSS) curve, and the progress-free interval (PFI) curve, illustrating the prognostic association between DLGAP5 expression level and OS, DSS, and PFI. 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