Single-Cell Sequencing Reveals that DBI is the Key Gene and Potential Therapeutic Target in Quiescent Bladder Cancer Stem Cells.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35769986", "kind": "science-record", "revision": 2, "content_hash": "sha256:b71f86daec422dbb7a45d72128aa5acd2ee51aed8c9723cff172157f6a5d91b6", "created_at": "2026-11-01T18:59:25Z", "updated_at": "2026-11-01T21:28:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-Cell Sequencing Reveals that DBI is the Key Gene and Potential Therapeutic Target in Quiescent Bladder Cancer Stem Cells.", "doi": "10.3389/fgene.2022.904536", "pmid": "35769986", "authors": ["Jiaxi Yao", "Yue Liu", "Jitao Yang", "Mengling Li", "Simin Li", "Bo Zhang", "Rui Yang", "Yuchong Zhang", "Xiaoyu Cui", "ChunQing Feng"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Drug resistance and recurrence often develop during the treatment of muscle-invasive bladder cancer (MIBC). The existence of cancer stem cells (CSCs) in MIBC makes the formulation of effective treatment strategies extremely challenging. We aimed to use single-cell RNA sequencing approaches to identify CSCs and evaluate their molecular characteristics and to discover possible therapeutic measures. Methods: GEO data sets GSE130001 and GSE146137 were used to construct an expression matrix. After cells were identified by type, malignant epithelial cells inferred by InferCNV were extracted for stemness evaluation. The subset of cells with the highest stemness was subjected to weighted gene coexpression network analysis (WGCNA) and pseudotime analysis to identify key genes. In addition, we predicted drug sensitivity relationships for key genes in CTD and predicted the correlation between drugs and survival through siGDC. Results: We found that there were some CSCs in MIBC samples. The CSC population was heterogeneous during tumor development and was divided into quiescent and proliferating CSCs. We identified DBI as the key gene in quiescent CSCs. Analysis of a TCGA data set showed that higher DBI expression indicated higher histological grade. In addition, we predicted that acetaminophen can reduce DBI expression, thereby reducing the stemness of CSCs. Thus, we identified a potential new use of acetaminophen. Conclusion: We systematically explored CSCs in tumors and determined that DBI may be a key gene and potential therapeutic target in quiescent CSCs. In addition, we confirmed that acetaminophen may be a candidate drug targeting CSCs, improving our understanding of CSC-targeting therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "An innovative one-class logistic regression (OCLR) machine learning algorithm was used to perform multiplatform analysis of the transcriptome, methylome and transcription factor binding sites of stem cells; the mRNAsi, an independent stemness index, was thus obtained (Malta et al., 2018).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35769986", "locator": "sections[5] \"Calculation of the mRNA Expression-Based Stemness Index\" paragraph 2", "quote": "An innovative one-class logistic regression (OCLR) machine learning algorithm was used to perform multiplatform analysis of the transcriptome, methylome and transcription factor binding sites of stem cells; the mRNAsi, an independent stemness index, was thus obtained (Malta et al., 2018)."}], "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:35769986", "locator": "sections[19] \"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 DBI as the key gene in quiescent CSCs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35769986", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified DBI as the key gene in quiescent CSCs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified DBI as the key gene in quiescent CSCs.", "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:35769986"], "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:GSE130001"], "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:GSE130001"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35769986", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.904536", "sha256": "sha256:23f44ce219e242e86780148fd8cc9c393f937cc85c2947fae9613f4dd090f271", "bytes": 46925, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.904536", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9235029", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE130001", "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=GSE130001", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE130001", "version": null, "note": "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.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3729178", "GSM3729179"], "sample_count": 2, "group_sizes": null, "group_sizes_basis": null, "platform": "16791", "taxon": "Homo sapiens", "title": "Single cell RNA-seq profiling of two bladder cancer specimen", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE146137", "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=GSE146137", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE146137", "version": null, "note": "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.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4367143", "GSM4367144", "GSM4367145", "GSM4367146", "GSM4367147", "GSM4367148", "GSM4367149", "GSM4367150"], "sample_count": 8, "group_sizes": null, "group_sizes_basis": null, "platform": "19057;18573", "taxon": "Homo sapiens; Mus musculus", "title": "Multi Lineage Heterogeneity and Plasticity in Urothelial Carcinoma of the Bladder", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE130001 not downloaded", "asset:data:geo:GSE130001 licence not read", "asset:data:geo:GSE146137 not downloaded", "asset:data:geo:GSE146137 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "An innovative one-class logistic regression (OCLR) machine learning algorithm was used to perform multiplatform analysis of the transcriptome, methylome and transcription factor binding sites of stem cells; the mRNAsi, an independent stemness index, was thus obtained (Malta et al., 2018)."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE130001", "GSE146137"], "resolved_accessions": ["GSE130001", "GSE146137"], "statements": ["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."], "basis": "The paper names GSE130001, GSE146137 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:35769986", "data:geo:GSE130001"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE130001"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:31:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13951, "completion_tokens": 1567, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35769986", "type": "paper", "label": "pmid:35769986"}, {"id": "source:pmid:35769986", "type": "manuscript", "label": "PMID 35769986 full text"}, {"id": "data:geo:GSE130001", "type": "dataset", "label": "GEO GSE130001"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35769986", "type": "paper", "label": "pmid:35769986"}, {"id": "source:pmid:35769986", "type": "manuscript", "label": "PMID 35769986 full text"}, {"id": "data:geo:GSE130001", "type": "dataset", "label": "GEO GSE130001"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35769986", "type": "extraction", "used": ["source:pmid:35769986"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE130001", "type": "accession_resolution", "used": ["data:geo:GSE130001"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35769986", "parent_hash": "sha256:5f65cc59571f8abf773c6e580da38fc0872e0af7f03376c872ffbb7f818b46fc", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE130001 licence not read", "asset:data:geo:GSE130001 not downloaded", "asset:data:geo:GSE146137 licence not read", "asset:data:geo:GSE146137 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["egies extremely challenging. We aimed to use single-cell RNA sequencing approaches to identify CSCs and evaluate the", "cquisition, Quality Control, and Analysis of scRNA-Seq Data The data used for our research were dow"], "order_basis": "paper_text_order:349", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ely challenging. We aimed to use single-cell RNA sequencing approaches to identify CSCs and evaluate the"], "order_basis": "paper_text_order:361", "id": "assay:2"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["An innovative one-class logistic regression (OCLR) machine learning algorithm was used to perform multiplatform analysis of the transcriptome, methylome and transcription factor binding sites of stem cells; the mRNAsi, an independent stemness index, was thus obtained (Malta et al., 2018)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}