Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34670584", "kind": "science-record", "revision": 5, "content_hash": "sha256:2ed85e85e685af7bd7c957b06eee6bdca9922b8653c171067410299caaa6e6f6", "created_at": "2026-11-01T17:36:20Z", "updated_at": "2026-11-01T22:47:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer.", "doi": "10.1186/s12935-021-02252-9", "pmid": "34670584", "authors": ["Hang Zheng", "Heshu Liu", "Yang Ge", "Xin Wang"], "journal": "Cancer cell international", "year": 2021, "abstract": "BACKGROUND: Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC.\nMETHODS: We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions.\nRESULTS: A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan-Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC.\nCONCLUSION: To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.", "claims": [{"id": "claim-method", "statement": "Genes with P < 0.1 in univariate Cox analysis were regarded as candidate prognostic genes. To minimize overfitting risk, we then applied the least absolute shrinkage and selection operator (LASSO) Cox regression model via glmnet R package [42], and the CAF signature was calculated as: CAF risk score = Ʃ(βi * Expi), where βi represented the LASSO coefficient of ith gene, and Expi was the ith candidate gene’s expression value. Subsequently, patients were classified into high- and low-CAF risk groups by their median CAF scores, and their relationship with OS was evaluated via Kaplan–Meier analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34670584", "locator": "sections[6] \"Construction and validation of an individualized CAF-related prognostic signature\" paragraph 2", "quote": "Genes with P < 0.1 in univariate Cox analysis were regarded as candidate prognostic genes. To minimize overfitting risk, we then applied the least absolute shrinkage and selection operator (LASSO) Cox regression model via glmnet R package [42], and the CAF signature was calculated as: CAF risk score = Ʃ(βi * Expi), where βi represented the LASSO coefficient of ith gene, and Expi was the ith candidate gene’s expression value. Subsequently, patients were classified into high- and low-CAF risk groups by their median CAF scores, and their relationship with OS was evaluated via Kaplan–Meier analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the findings of this study are available in the from The Cancer Genome Atlas (TCGA) (https://gdc.xenahubs.net) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34670584", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The datasets supporting the findings of this study are available in the from The Cancer Genome Atlas (TCGA) (https://gdc.xenahubs.net) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Kaplan–Meier curves revealed that high CAF risk group had significantly unfavorable survival outcomes compared with the low CAF risk group (TCGA training cohort, hazard ratio (HR) = 4.178, 95% CI: 2.222–7.854, log-rank P < 0.001, Fig. 5a; TCGA total cohort, HR = 3.272, 95% CI: 2.008–5.332, log-rank P < 0.001, Fig. 5c; GSE39582 cohort, HR = 1.496, 95% CI: 1.078–2.075, log-rank P = 0.016, Fig. 5d; GSE17536 cohort, HR = 1.924, 95% CI: 1.197–3.092, log-rank P = 0.007, Fig. 5e).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Kaplan–Meier curves revealed that high CAF risk group had significantly unfavorable survival outcomes compared with the low CAF risk group (TCGA training cohort, hazard ratio (HR) = 4.178, 95% CI: 2.222–7.854, log-rank P < 0.001, Fig. 5a; TCGA total cohort, HR = 3.272, 95% CI: 2.008–5.332, log-rank P < 0.001, Fig. 5c; GSE39582 cohort, HR = 1.496, 95% CI: 1.078–2.075, log-rank P = 0.016, Fig. 5d; GSE17536 cohort, HR = 1.924, 95% CI: 1.197–3.092, log-rank P = 0.007, Fig. 5e)."}, "evidence": [{"asset": "source:pmid:34670584", "locator": "sections[17] \"Nine-gene prognostic CAF signature construction and verification\" paragraph 2", "quote": "Kaplan–Meier curves revealed that high CAF risk group had significantly unfavorable survival outcomes compared with the low CAF risk group (TCGA training cohort, hazard ratio (HR) = 4.178, 95% CI: 2.222–7.854, log-rank P < 0.001, Fig. 5a; TCGA total cohort, HR = 3.272, 95% CI: 2.008–5.332, log-rank P < 0.001, Fig. 5c; GSE39582 cohort, HR = 1.496, 95% CI: 1.078–2.075, log-rank P = 0.016, Fig. 5d; GSE17536 cohort, HR = 1.924, 95% CI: 1.197–3.092, log-rank P = 0.007, Fig. 5e)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Kaplan–Meier curves revealed that high CAF risk group had significantly unfavorable survival outcomes compared with the low CAF risk group (TCGA training cohort, hazard ratio (HR) = 4.178, 95% CI: 2.222–7.854, log-rank P < 0.001, Fig. 5a; TCGA total cohort, HR = 3.272, 95% CI: 2.008–5.332, log-rank P < 0.001, Fig. 5c; GSE39582 cohort, HR = 1.496, 95% CI: 1.078–2.075, log-rank P = 0.016, Fig. 5d; GSE17536 cohort, HR = 1.924, 95% CI: 1.197–3.092, log-rank P = 0.007, Fig. 5e).", "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:34670584"], "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:GSE132257"], "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:GSE132257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34670584", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-021-02252-9", "sha256": "sha256:969e0073e926a87dd2c6e5ca1754bc546b9c010efa1a8d5b5a2ecf5c5f0cd383", "bytes": 47916, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12935-021-02252-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8529760", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE132257", "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=GSE132257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE132257", "version": null, "note": "The datasets supporting the findings of this study are available in the from The Cancer Genome Atlas (TCGA) (https://gdc.xenahubs.net) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets supporting the findings of this study are available in the from The Cancer Genome Atlas (TCGA) (https://gdc.xenahubs.net) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. 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molecular and survival characteristics [Expression]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE132257 not downloaded", "asset:data:geo:GSE132257 licence not read", "asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 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": "Genes with P < 0.1 in univariate Cox analysis were regarded as candidate prognostic genes. To minimize overfitting risk, we then applied the least absolute shrinkage and selection operator (LASSO) Cox regression model via glmnet R package [42], and the CAF signature was calculated as: CAF risk score = Ʃ(βi * Expi), where βi represented the LASSO coefficient of ith gene, and Expi was the ith candidate gene’s expression value. 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Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expression"], "order_basis": "paper_text_order:1576", "id": "assay:4"}, {"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": ["Genes with P < 0.1 in univariate Cox analysis were regarded as candidate prognostic genes. To minimize overfitting risk, we then applied the least absolute shrinkage and selection operator (LASSO) Cox regression model via glmnet R package [42], and the CAF signature was calculated as: CAF risk score = Ʃ(βi * Expi), where βi represented the LASSO coefficient of ith gene, and Expi was the ith candidate gene’s expression value. 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