Prediction of prognosis and immunotherapy response in lung adenocarcinoma based on CD79A, DKK1 and VEGFC.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37534013", "kind": "science-record", "revision": 3, "content_hash": "sha256:235e4139ba93e253f357bfccc748e0ca535c4d83aba02d12e825993e015095c5", "created_at": "2026-11-01T19:02:18Z", "updated_at": "2026-11-01T22:10:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prediction of prognosis and immunotherapy response in lung adenocarcinoma based on CD79A, DKK1 and VEGFC.", "doi": "10.1016/j.heliyon.2023.e18503", "pmid": "37534013", "authors": ["Qilong Zhang", "Mingyuan Zhao", "Shuangyan Lin", "Qi Han", "He Ye", "Fang Peng", "Li Li"], "journal": "Heliyon", "year": 2023, "abstract": "Background: Tumor immune microenvironment (TIME) is crucial for tumor initiation, progression, and metastasis; however, its relationship with lung adenocarcinoma (LUAD) is unknown. Traditional predictive models screen for biomarkers that are too general and infrequently associated with immune genes.\nMethods: RNA sequencing data of LUAD patients and immune-related gene sets were retrieved from public databases. Using the common genes shared by The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), differential gene expression analysis, survival analysis, Lasso regression analysis, and univariate and multivariate Cox regression analyses were performed to generate a novel risk score model. LUAD cohort in International Cancer Genome Consortium (ICGC), GSE68465 cohort in Gene Expression Omnibus (GEO) and an immunohistochemical assay were used to validate the key genes constructed risk score. The LUAD-related prognosis, clinical indicators, immune infiltrate characteristics, response to immunotherapy, and response to chemotherapeutic agents in different risk groups were evaluated by CIBERSORT, ImmuCellAI, pRRophetic and other tools.\nResults: The risk score model was constructed using CD79a molecule (CD79A), Dickkopf WNT signaling pathway inhibitor 1 (DKK1), and vascular endothelial growth factor C (VEGFC). High risk score was identified as a negative predictor for overall survival (OS) in subgroup analyses with tumor stage, TNM classification, therapy outcome, and ESTIMATE scores (P < 0.05). Low risk score was positively associated with plasma cells, memory B cells, CD8 T cells, regulatory T cells and γδT cells (P < 0.05). In low-risk group, programmed cell death 1 receptor (PD1), cytotoxic T-lymphocyte associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3) and indoleamine 2,3-dioxygenase (IDO) were more robustly expressed (P < 0.05). The treatment responses of immune checkpoint blockade (ICB) therapy and chemotherapy were likewise superior in low-risk group (P < 0.05). In immunohistochemical analysis, the tumor group had significantly higher levels of CD79A, DKK1, and VEGFC than the adjacent normal group (P < 0.01).\nConclusions: CD79A, DKK1 and VEGFC are important differential genes related to LUAD, risk score could reliably predict prognosis, composition of TIME and immunotherapy responses in LUAD patients. The excellent performance of the risk model shows its strong and broad application potential.", "claims": [{"id": "claim-method", "statement": "The immune genes associated with prognosis were screened using the univariate Cox proportional hazards regression. To avoid overfitting, all genes were analyzed by the “glmnet” [22] package for Lasso regression analysis. After the Lasso model filtering, these genes were analyzed using the multivariable Cox proportional hazards regression to develop this immune-related risk score model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37534013", "locator": "sections[4] \"Establishment and verification of a risk score model for prognosis\" paragraph 2", "quote": "The immune genes associated with prognosis were screened using the univariate Cox proportional hazards regression. To avoid overfitting, all genes were analyzed by the “glmnet” [22] package for Lasso regression analysis. After the Lasso model filtering, these genes were analyzed using the multivariable Cox proportional hazards regression to develop this immune-related risk score model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data will be made available on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37534013", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "Data will be made available on request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:37534013"], "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:GSE68465"], "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:GSE68465"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37534013", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2023.e18503", "sha256": 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Methods: RNA sequencing data of LUAD patients and immune-related gen", "int inhibitor therapy was predicted by using RNA-Seq data and the ImmuCellAI network analysis pla"], "order_basis": "paper_text_order:371", "id": "assay:1"}, {"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": ["hort in Gene Expression Omnibus (GEO) and an immunohistochemical assay were used to validate the key genes co", "se superior in low-risk group (P < 0.05). In immunohistochemical analysis, the tumor group had significantly", "KEGG pathways of immune-related DEGs.Fig. 2 Immunohistochemical analysis of CD79A, DKK1, and VEGFC CD79A, DK", "resentative images of CD79A, DKK1, and VEGFC immunostaining in cancer tissues and paracancerous tissues"], "order_basis": "paper_text_order:906", "id": "assay:2"}, {"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": ["The immune genes associated with prognosis were screened using the univariate Cox proportional hazards regression. To avoid overfitting, all genes were analyzed by the “glmnet” [22] package for Lasso regression analysis. After the Lasso model filtering, these genes were analyzed using the multivariable Cox proportional hazards regression to develop this immune-related risk score model."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}