Deciphering the immunological and prognostic features of bladder cancer through platinum-resistance-related genes analysis and identifying potential therapeutic target P4HB.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37790935", "kind": "science-record", "revision": 3, "content_hash": "sha256:608d4cf938e400591016ed0a3724636a7a5ab597e63dc991b9dd73c680994947", "created_at": "2026-11-01T19:02:38Z", "updated_at": "2026-11-01T22:10:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deciphering the immunological and prognostic features of bladder cancer through platinum-resistance-related genes analysis and identifying potential therapeutic target P4HB.", "doi": "10.3389/fimmu.2023.1253586", "pmid": "37790935", "authors": ["Situ Xiong", "Sheng Li", "Jin Zeng", "Jianqiang Nie", "Taobin Liu", "Xiaoqiang Liu", "Luyao Chen", "Bin Fu", "Jun Deng", "Songhui Xu"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Objectives: To identify the molecular subtypes and develop a scoring system for the tumor immune microenvironment (TIME) and prognostic features of bladder cancer (BLCA) based on the platinum-resistance-related (PRR) genes analysis while identifying P4HB as a potential therapeutic target.\nMethods: In this study, we analyzed gene expression data and clinical information of 594 BLCA samples. We used unsupervised clustering to identify molecular subtypes based on the expression levels of PRR genes. Functional and pathway enrichment analyses were performed to understand the biological activities of these subtypes. We also assessed the TIME and developed a prognostic signature and scoring system. Moreover, we analyzed the efficacy of immune checkpoint inhibitors. Then we conducted real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) experiments to detect the expression level of prolyl 4-hydroxylase subunit beta (P4HB) in BLCA cell lines. Transfection of small interference ribonucleic acid (siRNA) was performed in 5637 and EJ cells to knock down P4HB, and the impact of P4HB on cellular functions was evaluated through wound-healing and transwell assays. Finally, siRNA transfection of P4HB was performed in the cisplatin-resistant T24 cell to assess its impact on the sensitivity of BLCA to platinum-based chemotherapy drugs.\nResults: In a cohort of 594 BLCA samples (TCGA-BLCA, n=406; GSE13507, n=188), 846 PRR-associated genes were identified by intersecting BLCA expression data from TCGA and GEO databases with the PRR genes from the HGSOC-Platinum database. Univariate Cox regression analysis revealed 264 PRR genes linked to BLCA prognosis. We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes. Cluster C exhibited a better prognosis and lower immune cell infiltration compared to the other Clusters A and B. The high PRR score group was significantly associated with an immunosuppressive tumor microenvironment, poor clinical-pathological features, and a poor prognosis. Furthermore, the high PRR group showed higher expression of immune checkpoint molecules and a poorer response to immune checkpoint inhibitors than the low PRR group. The key PRR gene P4HB was highly expressed in BLCA cell lines, and cellular functional experiments in vitro indicate that P4HB may be an important factor influencing BLCA migration and invasion.\nConclusion: Our study demonstrates that the PRR signatures are significantly associated with clinical-pathological features, the TIME, and prognostic features. The key PRR gene, P4HB, s a biomarker for the individualized treatment of BLCA patients.", "claims": [{"id": "claim-method", "statement": "We used consensus clustering algorithms to determine the number of clusters of the samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37790935", "locator": "sections[4] \"Identification of PRR molecular subtypes\" paragraph 2", "quote": "We used consensus clustering algorithms to determine the number of clusters of the samples."}], "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:37790935", "locator": "sections[25] \"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": "We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37790935", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes.", "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:37790935"], "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:GSE13507"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE13507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37790935", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1253586", "sha256": "sha256:e6148ce948b0f567fedee2992b66656bb9cd7746d33fdbb5e4d6785208bd1eaa", "bytes": 56518, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1253586", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10544894", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13507", "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=GSE13507", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13507", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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