Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36510567", "kind": "science-record", "revision": 3, "content_hash": "sha256:2755f4c90efc9c0b4d45db9ebda70c0cc7e4bb37a839d851876b86fc3b4f2785", "created_at": "2026-11-01T19:01:01Z", "updated_at": "2026-11-01T22:08:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.", "doi": "10.1155/2022/2955359", "pmid": "36510567", "authors": ["Jing Hu", "Yueqiang Jiang", "Qihao Wei", "Bin Li", "Sha Xu", "Guang Wei", "Pin Li", "Wei Chen", "Wenzhi Lv", "Xianjin Xiao", "Yaping Lu", "Xuan Huang"], "journal": "BioMed research international", "year": 2022, "abstract": "Background: The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model.\nMethods: We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and \"Seurat\" was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, \"singleR\" software was used to annotate cells. Seurat's \"FindAllMarkers\" program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The \"rms\" software was used to facilitate and simplify modeling.\nResults: Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines.\nConclusion: In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.", "claims": [{"id": "claim-method", "statement": "These candidate genes were subsequently evaluated using LASSO Cox regression analysis with 10-fold cross-validation, and “lambda.min” was determined to be the best lambda value (Figures 4(a) and 4(b)).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36510567", "locator": "sections[13] \"3.3. Prognostic Model Construction\" paragraph 2", "quote": "These candidate genes were subsequently evaluated using LASSO Cox regression analysis with 10-fold cross-validation, and “lambda.min” was determined to be the best lambda value (Figures 4(a) and 4(b))."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used to support the findings of this study are included within the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36510567", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "All data used to support the findings of this study are included within the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In addition, a five-gene predictive model associated to CAF was created in the training set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36510567", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In addition, a five-gene predictive model associated to CAF was created in the training set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In addition, a five-gene predictive model associated to CAF was created in the training set.", "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:36510567"], "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. 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