The cancer-associated fibroblast-related signature predicts prognosis and indicates immune microenvironment infiltration in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35967313", "kind": "science-record", "revision": 4, "content_hash": "sha256:a717a851d676eb8837567b1bbb07ebebe62a23b1b17824da61dafb35d04dce1f", "created_at": "2026-11-01T18:15:05Z", "updated_at": "2026-11-01T22:07:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The cancer-associated fibroblast-related signature predicts prognosis and indicates immune microenvironment infiltration in gastric cancer.", "doi": "10.3389/fimmu.2022.951214", "pmid": "35967313", "authors": ["Tsz Kin Mak", "Xing Li", "Huaping Huang", "Kaiming Wu", "Zhijian Huang", "Yulong He", "Changhua Zhang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Gastric cancer (GC) is one of the most common cancers, with a wide range of symptoms and outcomes. Cancer-associated fibroblasts (CAFs) are newly identified in the tumor microenvironment (TME) and associated with GC progression, prognosis, and treatment response. A novel CAF-associated prognostic model is urgently needed to improve treatment strategies.\nMethods: The detailed data of GC samples were downloaded from The Cancer Genome Atlas (TCGA), GSE62254, GSE26253, and GSE84437 datasets, then obtained 18 unique CAF-related genes from the research papers. Eight hundred eight individuals with GC were classified as TCGA or GSE84437 using consensus clustering by the selected CAF-related genes. The difference between the two subtypes revealed in this study was utilized to create the \"CAF-related signature score\" (CAFS-score) prognostic model and validated with the Gene Expression Omnibus (GEO) database.\nResults: We identified two CAF subtypes characterized by high and low CAFS-score in this study. GC patients in the low CAFS-score group had a better OS than those in the high CAFS-score group, and the cancer-related malignant pathways were more active in the high CAFS-score group, compared to the low CAFS-score group. We found that there was more early TNM stage in the low CAFS-score subgroup, while there was more advanced TNM stage in the high CAFS-score subgroup. The expression of TMB was significantly higher in the low CAFS-score subgroup than in the high CAFS-score subgroup. A low CAFS-score was linked to increased microsatellite instability-high (MSI-H), mutation load, and immunological activation. Furthermore, the CAFS-score was linked to the cancer stem cell (CSC) index as well as chemotherapeutic treatment sensitivity. The patients in the high CAFS-score subgroup had significantly higher proportions of monocytes, M2 macrophages, and resting mast cells, while plasma cells and follicular helper T cells were more abundant in the low-risk subgroup. The CAFS-score was also highly correlated with the sensitivity of chemotherapeutic drugs. The low CAFS-score group was more likely to have an immune response and respond to immunotherapy. We developed a nomogram to improve the CAFS-clinical score's usefulness.\nConclusion: The CAFS-score may have a significant role in the TME, clinicopathological characteristics, prognosis, CSC, MSI, and drug sensitivity, according to our investigation of CAFs in GC. We also analyzed the value of the CAFS-score in immune response and immunotherapy. This work provides a foundation for improving prognosis and responding to immunotherapy in patients with GC.", "claims": [{"id": "claim-method", "statement": "LASSO-Cox analysis was utilized to minimize the risk of over-fitting using the “glmnet” R package. Multivariate Cox analysis was used to select the candidate genes for establishing a prognostic model (CAFS-score) in the training cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35967313", "locator": "sections[7] \"Construction and validation of the prognostic model\" paragraph 2", "quote": "LASSO-Cox analysis was utilized to minimize the risk of over-fitting using the “glmnet” R package. Multivariate Cox analysis was used to select the candidate genes for establishing a prognostic model (CAFS-score) in the training cohort."}], "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:35967313", "locator": "sections[26] \"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": "The CAFS-score separated gastric cancer patients into low- and high-risk groups, with the low-risk group having better overall survival and the high-risk group showing more active cancer-related malignant pathways.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35967313", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "GC patients in the low CAFS-score group had a better OS than those in the high CAFS-score group, and the cancer-related malignant pathways were more active in the high CAFS-score group, compared to the low CAFS-score group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The CAFS-score separated gastric cancer patients into low- and high-risk groups, with the low-risk group having better overall survival and the high-risk group showing more active cancer-related malignant pathways.", "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:35967313"], "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:GSE26253"], "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:GSE26253"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35967313", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.951214", "sha256": "sha256:106956b64141760a00d85def173fdac847f311c23872c1a62aaa8ec813f0c9bd", "bytes": 48134, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.951214", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9372353", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE26253", "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=GSE26253", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE26253", "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. 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"sample_count": 483, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Molecular subtypes in gastric cancer.", "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:GSE26253 not downloaded", "asset:data:geo:GSE26253 licence not read", "asset:data:geo:GSE62254 not downloaded", "asset:data:geo:GSE62254 licence not read", "asset:data:geo:GSE84437 not downloaded", "asset:data:geo:GSE84437 licence not read"]}, "analysis": {"state": "partial", "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": "LASSO-Cox analysis was 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE84437", "GSE62254", "GSE26253"], "resolved_accessions": ["GSE84437", "GSE62254", "GSE26253"], "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 GSE84437, GSE62254, GSE26253 in Gene Expression Omnibus (GEO) 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:35967313", "data:geo:GSE26253"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE26253"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:59:59Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12781, "completion_tokens": 1677, "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:35967313", "type": "paper", "label": "pmid:35967313"}, {"id": "source:pmid:35967313", "type": "manuscript", "label": "PMID 35967313 full text"}, {"id": "data:geo:GSE26253", "type": "dataset", "label": "GEO GSE26253"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35967313", "type": "paper", "label": "pmid:35967313"}, {"id": "source:pmid:35967313", "type": "manuscript", "label": "PMID 35967313 full text"}, {"id": "data:geo:GSE26253", "type": "dataset", "label": "GEO GSE26253"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35967313", "type": "extraction", "used": ["source:pmid:35967313"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE26253", "type": "accession_resolution", "used": ["data:geo:GSE26253"], "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:35967313", "parent_hash": "sha256:8e09ea3c270e45d3287be35c3b65169d2f35a4ee1cf5e07a7d05a6877bc1ec93", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE26253 licence not read", "asset:data:geo:GSE26253 not downloaded", "asset:data:geo:GSE62254 licence not read", "asset:data:geo:GSE62254 not downloaded", "asset:data:geo:GSE84437 licence not read", "asset:data:geo:GSE84437 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "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": ["LASSO-Cox analysis was utilized to minimize the risk of over-fitting using the “glmnet” R package. Multivariate Cox analysis was used to select the candidate genes for establishing a prognostic model (CAFS-score) in the training cohort."], "order_basis": "analysis_only", "id": "assay:1"}]}