A Four-Cell-Senescence-Regulator-Gene Prognostic Index Verified by Genome-Wide CRISPR Can Depict the Tumor Microenvironment and Guide Clinical Treatment of Bladder Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35898492", "kind": "science-record", "revision": 4, "content_hash": "sha256:56e08cd7fdf971a71218d9ab787b72b28c006074c6058b1c9da0bb83f8ce6e2d", "created_at": "2026-11-01T18:14:59Z", "updated_at": "2026-11-01T22:07:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Four-Cell-Senescence-Regulator-Gene Prognostic Index Verified by Genome-Wide CRISPR Can Depict the Tumor Microenvironment and Guide Clinical Treatment of Bladder Cancer.", "doi": "10.3389/fimmu.2022.908068", "pmid": "35898492", "authors": ["Jian-Xuan Sun", "Chen-Qian Liu", "Jin-Zhou Xu", "Ye An", "Meng-Yao Xu", "Xing-Yu Zhong", "Na Zeng", "Si-Yang Ma", "Hao-Dong He", "Zong-Biao Zhang", "Shao-Gang Wang", "Qi-Dong Xia"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Bladder cancer (BCa) is the 10th most commonly diagnosed cancer worldwide, and cellular senescence is defined as a state of permanent cell cycle arrest and considered to play important roles in the development and progression of tumor. However, the comprehensive effect of senescence in BCa has not ever been systematically evaluated. Using the genome-wide CRISPR screening data acquired from DepMap (Cancer Dependency Map), senescence genes from the CellAge database, and gene expression data from The Cancer Genome Atlas (TCGA), we screened out 12 senescence genes which might play critical roles in BCa. A four-cell-senescence-regulator-gene prognostic index was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression model. The transcriptomic data and clinical information of BCa patients were downloaded from TCGA and Gene Expression Omnibus (GEO). We randomly divided the patients in TCGA cohort into training and testing cohorts and calculated the risk score according to the expression of the four senescence genes. The validity of this risk score was validated in the testing cohort (TCGA) and validation cohort (GSE13507). The Kaplan-Meier curves revealed a significant difference in the survival outcome between the high- and low-risk score groups. A nomogram including the risk score and other clinical factors (age, gender, stage, and grade) was established with better predictive capacity of OS in 1, 3, and 5 years. Besides, we found that patients in the high-risk group had higher tumor mutation burden (TMB); lower immune, stroma, and ESTIMATE scores; higher tumor purity; aberrant immune functions; and lower expression of immune checkpoints. We also performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to investigate the interaction between risk score and hallmark pathways and found that a high risk score was connected with activation of senescence-related pathways. Furthermore, we found that a high risk score was related to better response to immunotherapy and chemotherapy. In conclusion, we identified a four-cell-senescence-regulator-gene prognostic index in BCa and investigated its relationship with TMB, the immune landscape of tumor microenvironment (TME), and response to immunotherapy and chemotherapy, and we also established a nomogram to predict the prognosis of patients with BCa.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) method was conducted in the training group to screen the appropriate variables of the above 11 ECSRGs. Following this, the multivariate cox regression of the left variables was performed in the training group to establish a prognostic cell senescence index (PCSI).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35898492", "locator": "sections[5] \"Elimination of the Batch Effects, Sample Random Grouping, Construction, and Verification of the Prognostic Cell Senescence Index\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) method was conducted in the training group to screen the appropriate variables of the above 11 ECSRGs. Following this, the multivariate cox regression of the left variables was performed in the training group to establish a prognostic cell senescence index (PCSI)."}], "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:35898492", "locator": "sections[17] \"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": "Meta-analysis across the training, testing, and validation cohorts showed that the four-gene risk score had a pooled hazard ratio of 1.33 (95% CI = 1.07–1.66, P = 0.06).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Besides, the result of meta-analysis based on the three cohorts illustrated that the risk score was in good validity (HR = 1.33, 95% CI = 1.07–1.66, P = 0.06) ( Figure 3N )."}, "evidence": [{"asset": "source:pmid:35898492", "locator": "sections[10] \"Construction and Verification of the Prognostic Cell Senescence Index\" paragraph 2", "quote": "Besides, the result of meta-analysis based on the three cohorts illustrated that the risk score was in good validity (HR = 1.33, 95% CI = 1.07–1.66, P = 0.06) ( Figure 3N )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Meta-analysis across the training, testing, and validation cohorts showed that the four-gene risk score had a pooled hazard ratio of 1.33 (95% CI = 1.07–1.66, P = 0.06).", "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:35898492"], "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:35898492", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.908068", "sha256": "sha256:5a42460989e61d4f3287fc2ccefdc58335c6a88bd7f3e6c9d90bad30b4e0ff01", "bytes": 49312, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.908068", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9312376", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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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