Integrated single-cell and bulk RNA sequencing unravels neutrophil heterogeneity and validates SPP1 as a prognostic biomarker in cervical cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41612311", "kind": "science-record", "revision": 3, "content_hash": "sha256:b9087c78446631af999afa00cc3c819fa8e53caf2ad363b7fbe7d90ff4a47e1d", "created_at": "2026-11-01T20:31:42Z", "updated_at": "2026-11-01T22:15:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated single-cell and bulk RNA sequencing unravels neutrophil heterogeneity and validates SPP1 as a prognostic biomarker in cervical cancer.", "doi": "10.1186/s12885-026-15636-9", "pmid": "41612311", "authors": ["Siting Lin", "Junlin Zhong", "Shengjin Yuan", "Manting Su", "Yiwang Zhang", "Xinling Zhang"], "journal": "BMC cancer", "year": 2026, "abstract": "BACKGROUND: Cervical cancer (CC) remains a major global health burden, with tumor microenvironment (TME) plasticity and immune evasion driving its progression. The intricate heterogeneity of neutrophils and their complex crosstalk within the TME remain poorly understood, thereby limiting the development of targeted therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) provides an unprecedented level of resolution for dissecting neutrophil subpopulations and elucidating their roles in CC pathogenesis.\nMETHODS: We integrated scRNA-seq data from CC tissues (GSE208653, n = 5) with bulk RNA-seq cohorts from The Cancer Genome Atlas - Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and Genotype-Tissue Expression (GTEx) database. Using Seurat-based clustering, pseudotime trajectory analysis and CellChat, we mapped neutrophil dynamics and intercellular communication networks. Differentially expressed genes (DEGs) were analyzed using the limma package, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. A prognostic model was subsequently constructed via LASSO-Cox regression. Key targets were further validated through both in vitro functional assays, immunohistochemical (IHC) and immunofluorescence (IF) analyses of human pathological specimens.\nRESULTS: Four functionally distinct neutrophil subtypes, including immature, mature, antitumor, and interferon-stimulated populations, were characterized in CC, demonstrating dynamic heterogeneity during tumorigenesis. Secreted Phosphoprotein 1 (SPP1) was identified as a critical differentially expressed gene, with the SPP1-CD44 axis serving as a key mediator of neutrophil-tumor cell crosstalk. Downregulation of SPP1 markedly suppressed CC cell migration, invasion, and proliferation. Furthermore, a prognostic signature based on risk stratification efficiently categorized patients into high- and low-risk cohorts, with validated clinical utility in predicting survival outcomes.\nCONCLUSIONS: Our study comprehensively characterized the TME in CC through single-cell transcriptomics. Integrated analysis with bulk RNA-seq established and validated a robust prognostic signature, identifying SPP1 as a key oncogenic driver. The SPP1-centric model demonstrates significant clinical utility for risk stratification. These findings provide new insights into neutrophil heterogeneity and establish a mechanistic foundation for precision therapeutics in CC.", "claims": [{"id": "claim-method", "statement": "A prognostic model was subsequently constructed via LASSO-Cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41612311", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A prognostic model was subsequently constructed via LASSO-Cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The cervical cancer (CC) scRNA-seq dataset file of GSE208653 was downloaded from the Gene Expression Omnibus (GEO) database ( [https://www.ncbi.nlm.nih.gov/geo/] ). The bulk RNA-seq cohorts were obtained from the TCGA database ([https://portal.gdc.cancer.gov/] ) and the Genotype-Tissue Expression (GTEx) database ( [http://gtexportal.org/home/] ). All data supporting the conclusions are available from the authors on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41612311", "locator": "sections[31] \"Data availability\" paragraph 2", "quote": "The cervical cancer (CC) scRNA-seq dataset file of GSE208653 was downloaded from the Gene Expression Omnibus (GEO) database ( [https://www.ncbi.nlm.nih.gov/geo/] ). The bulk RNA-seq cohorts were obtained from the TCGA database ([https://portal.gdc.cancer.gov/] ) and the Genotype-Tissue Expression (GTEx) database ( [http://gtexportal.org/home/] ). All data supporting the conclusions are available from the authors on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, a prognostic signature based on risk stratification efficiently categorized patients into high- and low-risk cohorts, with validated clinical utility in predicting survival outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41612311", "locator": "sections[21] \"Prediction and validation of prognostic gene signatures\" paragraph 2", "quote": "Time-dependent ROC curve analysis validated the signature’s predictive efficacy for overall survival, with training cohort AUCs of 0.62 (3-year), 0.62 (4-year) and 0.66 (5-year), and internal validation cohort AUCs of 0.64 (3-year), 0.73 (4-year) and 0.68 (5-year) (Fig. 6J-K)."}, {"asset": "source:pmid:41612311", "locator": "sections[21] \"Prediction and validation of prognostic gene signatures\" paragraph 2", "quote": "The AUCs for 3-, 4-, and 5-year survival in the external validation cohort were 0.71, 0.68, and 0.63, respectively (Fig. 6L)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, a prognostic signature based on risk stratification efficiently categorized patients into high- and low-risk cohorts, with validated clinical utility in predicting survival outcomes.", "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:41612311"], "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:GSE208653"], "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:GSE208653"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41612311", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-026-15636-9", "sha256": "sha256:607496b3f735ed4af3551e7805e3663d01659d2696cc88a7202aee1ea9430174", "bytes": 61239, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-026-15636-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12930679", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE208653", "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=GSE208653", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE208653", "version": null, "note": "The cervical cancer (CC) scRNA-seq dataset file of GSE208653 was downloaded from the Gene Expression Omnibus (GEO) database ( [https://www.ncbi.nlm.nih.gov/geo/] ). 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Integrated analysis with bulk RNA-seq established and validated a robust prognosti", "targeted therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) provides an unprecedented level", "from CC tissues (GSE208653, n = 5) with bulk RNA-seq cohorts from The Cancer Genome Atlas - Cervi"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["elopment of targeted therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) provides an unprecedented level", "ensively characterized the TME in CC through single-cell transcriptomics. Integrated analysis with bulk RNA-seq estab", "utic strategies. Single-cell RNA sequencing (scRNA-seq) provides an unprecedented level of resoluti", "s in CC pathogenesis. METHODS: We integrated scRNA-seq data from CC tissues (GSE208653, n = 5) with"], "order_basis": "paper_text_order:442", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ted through both in vitro functional assays, immunohistochemical (IHC) and immunofluorescence (IF) analyses o", "tional assays, immunohistochemical (IHC) and immunofluorescence (IF) analyses of human pathological specimen", "itro functional assays, immunohistochemical (IHC) and immunofluorescence (IF) analyses of hum", "on of Helsinki. Hematoxylin and eosin (H&E), immunohistochemistry (IHC) and immunofluorescence (IF) analyses w"], "order_basis": "paper_text_order:1349", "id": "assay:3"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["lyses were performed in tumor tissues from 6 patients with CC. The tissue samples were collected after"], "order_basis": "paper_text_order:2991", "id": "assay:4"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["embedded in paraffin. Cell culture and cell transfection Human SiHa cells were obtained from Procell", "of the cell line reached 50–60%, cells were transfected with 50 pmol/mL small interfering RNA (siRNA", "/mL small interfering RNA (siRNA) using RNAi transfection reagent (D-Nano Therapeutics, Beijing, China", "ected with 50 pmol/mL small interfering RNA (siRNA) using RNAi transfection reagent (D-Nano The"], "order_basis": "paper_text_order:3195", "id": "assay:5"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["m Procell Company (Wuhan, China). Cells were cultured in Minimum Essential Medium (MEM, Procell) supp"], "order_basis": "paper_text_order:3286", "id": "assay:6"}, {"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": ["A prognostic model was subsequently constructed via LASSO-Cox regression."], "order_basis": "consumes_earlier_output", "id": "assay:7"}]}