A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35669725", "kind": "science-record", "revision": 4, "content_hash": "sha256:5219d8953043cbbd8fe649d32e2b2ba53d30016a5336310364c1a0ed672772d0", "created_at": "2026-11-01T18:14:37Z", "updated_at": "2026-11-01T22:06:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA.", "doi": "10.1155/2022/2449449", "pmid": "35669725", "authors": ["Hanwen Li", "Shaohua Chen", "Hua Mi"], "journal": "BioMed research international", "year": 2022, "abstract": "Background: Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and \"CancerSubtypes\" package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment.\nMethod: BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. \"CancerSubtypes\" classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm.\nResults: In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: \"GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN\" and \"MODULE_253.\" Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers.\nConclusions: In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.", "claims": [{"id": "claim-method", "statement": "Univariate Cox analysis and Kaplan–Meier survival analysis were performed in R software using the “survival” and “survminer” packages to obtain prognostic DEGSs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35669725", "locator": "sections[6] \"2.4. Construction of BLCA Prognostic Signature\" paragraph 2", "quote": "Univariate Cox analysis and Kaplan–Meier survival analysis were performed in R software using the “survival” and “survminer” packages to obtain prognostic DEGSs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data can be obtained from public TCGA database (https://portal.gdc.cancer.gov/), GEO database (GSE13507) (https://www.ncbi.nlm.nih.gov/geo/), HPA database (https://www.proteinatlas.org/), and GSVA database (http://www.gsea-msigdb.org/gsea/index.jsp). Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35669725", "locator": "sections[24] \"Data Availability\" paragraph 2", "quote": "The raw data can be obtained from public TCGA database (https://portal.gdc.cancer.gov/), GEO database (GSE13507) (https://www.ncbi.nlm.nih.gov/geo/), HPA database (https://www.proteinatlas.org/), and GSVA database (http://www.gsea-msigdb.org/gsea/index.jsp). Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, we discovered two DEGSs linked to prognosis: “GSE1460_ CD4_THYMOCYTE_ VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.”", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35669725", "locator": "sections[16] \"3.4. Construction of Prognostic Signature\" paragraph 2", "quote": "Finally, we discovered two DEGSs linked to prognosis: “GSE1460_ CD4_THYMOCYTE_ VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.”"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, we discovered two DEGSs linked to prognosis: “GSE1460_ CD4_THYMOCYTE_ VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.”", "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:35669725"], "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": "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:GSE13507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35669725", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/2449449", "sha256": "sha256:24230bd76bd3a20beea0069371fa74e4b8115a0a611ef5d1236ffb5e9a00c8ef", "bytes": 39163, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/2449449", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9165618", "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 raw data can be obtained from public TCGA database (https://portal.gdc.cancer.gov/), GEO database (GSE13507) (https://www.ncbi.nlm.nih.gov/geo/), HPA database (https://www.proteinatlas.org/), and GSVA database (http://www.gsea-msigdb.org/gsea/index.jsp). 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