B-cell signatures characterize the immune landscape and predict LUAD prognosis via the integration of scRNA-seq and bulk RNA-seq.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39953119", "kind": "science-record", "revision": 3, "content_hash": "sha256:127bbe70c1c5ead7ebe61445d9b572704c45984ca91371f0bb3a273038144827", "created_at": "2026-11-01T20:30:12Z", "updated_at": "2026-11-01T22:13:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "B-cell signatures characterize the immune landscape and predict LUAD prognosis via the integration of scRNA-seq and bulk RNA-seq.", "doi": "10.1038/s41598-025-89213-8", "pmid": "39953119", "authors": ["Kexin Xu", "Di Han", "Zhengyuan Fan", "Ya Li", "Suxiao Liu", "Yixi Liao", "Hua Zhou", "Qibiao Wu", "Suyun Li"], "journal": "Scientific reports", "year": 2025, "abstract": "Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for approximately 35-40% of lung cancers, and the overall survival time of patients with LUAD is still very poor. B cells are important effector cells of adaptive immunity, and B-cell infiltration increases in various tumors. The role of B cells in LUAD is still largely unknown. Therefore, it is particularly important to clarify the role of B cells in LUAD. GSE164983, GSE50081, GSE37745 and GSE30219 were obtained from the GEO database. The TCGA-LUAD dataset was obtained from the TCGA database. UMAP was used to perform clustering descending and subgroup identification on single-cell RNA-sequencing (scRNA-seq) data to obtain B-cell markers. The TCGA cohort was used to obtain differentially expressed genes (DEGs). B-cell-related differentially expressed genes (BRGs) were identified through the intersection of B-cell markers and DEGs. The LASSO method was used to identify characteristic genes of BRGs and construct a prognostic risk model. LUAD patients were divided into high-risk and low-risk groups based on risk scores, and the immune landscape of the two groups was evaluated. We also analyzed the differences in clinical characteristics, mutations, immunotherapy, and drug sensitivity between the two groups. Thirty BRGs were obtained, and 6 characteristic genes were identified. Based on the characteristic genes, a prognostic risk model was constructed. According to the prognostic risk model, LUAD patients were divided into two groups: high-risk group and low-risk group. Patients in the high-risk group had worse outcomes and shorter survival times. Low-risk patients had better survival, while patients with high TNM stage accounted for a greater proportion of patients in the high-risk group. In addition, high-risk patients had a greater probability of mutation and worse immunotherapy response. Finally, we found different susceptibility profiles between the high-risk and low-risk groups. The prognostic risk model built based on the BRGs had good predictive performance, providing a new perspective on the prognosis and immunotherapy of LUAD patients and a new reference for LUAD research.", "claims": [{"id": "claim-method", "statement": "The LASSO method was used to identify characteristic genes of BRGs and construct a prognostic risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39953119", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The LASSO method was used to identify characteristic genes of BRGs and construct a prognostic risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and TCGA repository (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39953119", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and TCGA repository (https://portal.gdc.cancer.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Thirty BRGs were obtained, and 6 characteristic genes were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39953119", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Thirty BRGs were obtained, and 6 characteristic genes were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Thirty BRGs were obtained, and 6 characteristic genes were identified.", "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:39953119"], "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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