Single-Cell Sequencing Analysis Based on Public Databases for Constructing a Metastasis-Related Prognostic Model for Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35528539", "kind": "science-record", "revision": 3, "content_hash": "sha256:8931a12dcbdf7c1b9a8559e352fcedf9738cad4fda3b4a6ab168e07164908c92", "created_at": "2026-11-01T18:14:25Z", "updated_at": "2026-11-01T21:27:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-Cell Sequencing Analysis Based on Public Databases for Constructing a Metastasis-Related Prognostic Model for Gastric Cancer.", "doi": "10.1155/2022/7061263", "pmid": "35528539", "authors": ["Rubin Xu", "Liang Chen", "Wei Wei", "Qikai Tang", "You Yu", "Yiming Hu", "Sultan Kadasah", "Jiaheng Xie", "Hongzhu Yu"], "journal": "Applied bionics and biomechanics", "year": 2022, "abstract": "Background: Although incidences of gastric cancer have decreased in recent years, the disease remains a significant danger to human health. Lack of early symptoms often leads to delayed diagnosis of gastric cancer, so that many patients miss the opportunity for surgery. Treatment for advanced gastric cancer is often limited. Immunotherapy, targeted therapy, and the mRNA vaccine have all emerged as potentially viable treatments for advanced gastric cancer. However, our understanding of the immune microenvironment of gastric cancer is far from sufficient; now is the time to explore this microenvironment.\nMethods: In our study, using TCGA dataset and the GEO dataset GSE62254, we performed in-depth transcriptome and single-cell sequencing analyses based on public databases. We analyzed differential gene expressions of immune cells in metastatic and nonmetastatic gastric cancer and constructed a prognostic model of gastric cancer patients based on these differential gene expressions. We also screened candidate vaccine genes for gastric cancer.\nResults: This prognostic model can accurately predict the prognosis of gastric cancer patients by dividing them into high-risk and low-risk groups. In addition to this, we identified a candidate vaccine gene for gastric cancer: PTPN6.\nConclusions: Our study could provide new ideas for the treatment of gastric cancer.", "claims": [{"id": "claim-method", "statement": "Then, through the univariate Cox regression analysis, we set the screening condition as P < 0.05 and preliminarily obtained genes related to prognosis. Next, these genes were obtained by the multivariate Cox regression. Finally, using the least absolute shrinkage and selection operator (Lasso) regression, we obtained the genes most associated with prognosis and built a model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35528539", "locator": "sections[6] \"2.4. Construction of the Prognostic Model\" paragraph 2", "quote": "Then, through the univariate Cox regression analysis, we set the screening condition as P < 0.05 and preliminarily obtained genes related to prognosis. Next, these genes were obtained by the multivariate Cox regression. Finally, using the least absolute shrinkage and selection operator (Lasso) regression, we obtained the genes most associated with prognosis and built a model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated and described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding authors on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35528539", "locator": "sections[33] \"Data Availability\" paragraph 2", "quote": "All data generated and described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding authors on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The area under the ROC curve for 5 years in TCGA dataset was 0.748, suggesting that the model has a high degree of accuracy and is relatively stable in evaluating the prognostic outcomes of gastric cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35528539", "locator": "sections[21] \"3.5. Evaluation of the Model\" paragraph 2", "quote": "We also found that the ROC curves for 1, 3, and 5 years were all greater than 0.65 in the two datasets, and the area under the ROC curve for 5 years in TCGA dataset was 0.748, suggesting that the model has a high degree of accuracy and is relatively stable in evaluating the prognostic outcomes of gastric cancer patients (Figures 4(c) and 4(d))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The area under the ROC curve for 5 years in TCGA dataset was 0.748, suggesting that the model has a high degree of accuracy and is relatively stable in evaluating the prognostic outcomes of gastric cancer patients.", "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:35528539"], "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:GSE62254"], "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:GSE62254"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35528539", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/7061263", "sha256": "sha256:0aab63f1fc062450a57bdbdc673a063c7b709bc6d322e07e562d3f078fa581f4", "bytes": 35773, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/7061263", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9068325", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "All data generated and described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding authors on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated and described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding authors on reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1523727", "GSM1523728", "GSM1523729", "GSM1523744", "GSM1523745", "GSM1523746", "GSM1523747", "GSM1523748", "GSM1523765", "GSM1523768", "GSM1523769", "GSM1523770", "GSM1523771", "GSM1523772", "GSM1523773", "GSM1523774", "GSM1523775", "GSM1523776", "GSM1523777", "GSM1523778", "GSM1523779", "GSM1523780", "GSM1523781", "GSM1523782", "GSM1523783", "GSM1523784", "GSM1523785", "GSM1523786", "GSM1523787", "GSM1523788", "GSM1523789", "GSM1523790", "GSM1523791", "GSM1523792", "GSM1523793", "GSM1523794", "GSM1523795", "GSM1523796", "GSM1523797", "GSM1523798", "GSM1523799", "GSM1523800", "GSM1523801", "GSM1523802", "GSM1523803", "GSM1523804", "GSM1523805", "GSM1523806", "GSM1523807", "GSM1523808", "GSM1523809", "GSM1523810", "GSM1523811", "GSM1523812", "GSM1523813", "GSM1523814", "GSM1523815", "GSM1523816", "GSM1523817", "GSM1523818", "GSM1523819", "GSM1523820", "GSM1523821", "GSM1523822", "GSM1523823", "GSM1523824", "GSM1523825", "GSM1523826", "GSM1523827", "GSM1523828", "GSM1523829", "GSM1523830", "GSM1523831", "GSM1523832", "GSM1523833", "GSM1523834", "GSM1523835", "GSM1523836", "GSM1523837", "GSM1523838", "GSM1523839", "GSM1523840", "GSM1523841", "GSM1523842", "GSM1523843", "GSM1523844", "GSM1523845", "GSM1523846", "GSM1523847", "GSM1523848", "GSM1523849", "GSM1523850", "GSM1523851", "GSM1523852", "GSM1523853", "GSM1523854", "GSM1523855", "GSM1523856", "GSM1523857", "GSM1523858", "GSM1523859", "GSM1523860", "GSM1523861", "GSM1523862", "GSM1523863", "GSM1523864", "GSM1523865", "GSM1523866", "GSM1523867", "GSM1523868", "GSM1523869", "GSM1523870", "GSM1523871", "GSM1523872", "GSM1523873", "GSM1523874", "GSM1523875", "GSM1523876", "GSM1523877", "GSM1523878", "GSM1523879", "GSM1523880", "GSM1523881", "GSM1523882", "GSM1523883", "GSM1523884", "GSM1523885", "GSM1523886", "GSM1523887", "GSM1523888", "GSM1523889", "GSM1523890", "GSM1523891", "GSM1523892", "GSM1523893", "GSM1523894", "GSM1523895", "GSM1523896", "GSM1523897", "GSM1523898", "GSM1523899", "GSM1523901", "GSM1523903", "GSM1523904", "GSM1523905", "GSM1523906", "GSM1523908", "GSM1523909", "GSM1523910", "GSM1523911", "GSM1523918", "GSM1523919", "GSM1523920", "GSM1523921", "GSM1523922", "GSM1523923", "GSM1523924", "GSM1523925", "GSM1523926", "GSM1523927", "GSM1523928", "GSM1523929", "GSM1523930", "GSM1523931", "GSM1523932", "GSM1523933", "GSM1523934", "GSM1523935", "GSM1523936", "GSM1523937", "GSM1523938", "GSM1523939", "GSM1523940", "GSM1523941", "GSM1523942", "GSM1523943", "GSM1523944", "GSM1523945", "GSM1523946", "GSM1523947", "GSM1523948", "GSM1523949", "GSM1523950", "GSM1523951", "GSM1523952", "GSM1523953", "GSM1523954", "GSM1523955", "GSM1523956", "GSM1523957", "GSM1523958", "GSM1523959", "GSM1523960", "GSM1523961", "GSM1523962", "GSM1523963", "GSM1523964", "GSM1523965", "GSM1523966", "GSM1523967", "GSM1523968", "GSM1523969", "GSM1523970", "GSM1523971", "GSM1523972", "GSM1523973", "GSM1523974", "GSM1523975", "GSM1523976", "GSM1523977", "GSM1523978", "GSM1523979", "GSM1523980", "GSM1523981", "GSM1523982", "GSM1523983", "GSM1523984", "GSM1523985", "GSM1523986", "GSM1523987", "GSM1523988", "GSM1523989", "GSM1523990", "GSM1523991", "GSM1523992", "GSM1523993", "GSM1523994", "GSM1523995", "GSM1523996", "GSM1523997", "GSM1523998", "GSM1523999", "GSM1524000", "GSM1524001", "GSM1524002", "GSM1524003", "GSM1524004", "GSM1524005", "GSM1524006", "GSM1524007", "GSM1524008", "GSM1524009", "GSM1524010", "GSM1524011", "GSM1524012", "GSM1524013", "GSM1524014", "GSM1524015", "GSM1524016", "GSM1524017", "GSM1524018", "GSM1524019", "GSM1524020", "GSM1524021", "GSM1524022", "GSM1524023", "GSM1524024", "GSM1524025", "GSM1524026", "GSM1524027", "GSM1524028", "GSM1524029", "GSM1524030", "GSM1524031", "GSM1524032", "GSM1524033", "GSM1524034", "GSM1524035", "GSM1524036", "GSM1524037", "GSM1524038", "GSM1524039", "GSM1524040", "GSM1524041", "GSM1524042", "GSM1524043", "GSM1524044", "GSM1524045", "GSM1524046", "GSM1524047", "GSM1524048", "GSM1524049", "GSM1524050", "GSM1524051", "GSM1524052", "GSM1524053", "GSM1524054", "GSM1524055", "GSM1524056", "GSM1524057", "GSM1524058", "GSM1524059", "GSM1524060", "GSM1524061", "GSM1524062", "GSM1524068", "GSM1524069", "GSM1524070", "GSM1524071", "GSM1524072"], "sample_count": 300, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study [gastric tumors]", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE62254 not downloaded", "asset:data:geo:GSE62254 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, through the univariate Cox regression analysis, we set the screening condition as P < 0.05 and preliminarily obtained genes related to prognosis. Next, these genes were obtained by the multivariate Cox regression. Finally, using the least absolute shrinkage and selection operator (Lasso) regression, we obtained the genes most associated with prognosis and built a model."}}], "targets": [], "gaps": []}, "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "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": ["GSE62254"], "resolved_accessions": ["GSE62254"], "statements": ["All data generated and described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding authors on reasonable request."], "basis": "The paper names GSE62254 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:35528539", "data:geo:GSE62254"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE62254"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:45:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10663, "completion_tokens": 1627, "cached_tokens": 128}}, "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:35528539", "type": "paper", "label": "pmid:35528539"}, {"id": "source:pmid:35528539", "type": "manuscript", "label": "PMID 35528539 full text"}, {"id": "data:geo:GSE62254", "type": "dataset", "label": "GEO GSE62254"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35528539", "type": "paper", "label": "pmid:35528539"}, {"id": "source:pmid:35528539", "type": "manuscript", "label": "PMID 35528539 full text"}, {"id": "data:geo:GSE62254", "type": "dataset", "label": "GEO GSE62254"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35528539", "type": "extraction", "used": ["source:pmid:35528539"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE62254", "type": "accession_resolution", "used": ["data:geo:GSE62254"], "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:35528539", "parent_hash": "sha256:2516cd1c0153d48d137da32d18290f78cdaf46f760df46029230a12932f05e77", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE62254 licence not read", "asset:data:geo:GSE62254 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, through the univariate Cox regression analysis, we set the screening condition as P < 0.05 and preliminarily obtained genes related to prognosis. Next, these genes were obtained by the multivariate Cox regression. Finally, using the least absolute shrinkage and selection operator (Lasso) regression, we obtained the genes most associated with prognosis and built a model."], "order_basis": "analysis_only", "id": "assay:1"}]}