Exploring the effect of NK-cell related molecules on the prognosis and tumor microenvironment of gastric cancer patients: Evidence from large sample populations.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39071629", "kind": "science-record", "revision": 2, "content_hash": "sha256:5640c72768001617fcfae117cdae33a657c0a07c1c0831aabe248f665bcdf9c9", "created_at": "2026-11-01T20:29:08Z", "updated_at": "2026-11-01T21:33:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the effect of NK-cell related molecules on the prognosis and tumor microenvironment of gastric cancer patients: Evidence from large sample populations.", "doi": "10.1016/j.heliyon.2024.e33759", "pmid": "39071629", "authors": ["Yuqin Li", "Dejun Wu", "Anjun Xu", "Ming Xu", "Baiqing Fu", "Wujun Xiong"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Natural killer (NK) cells play a significant role in anti-tumor immunity, and their involvement has been documented in various cancers. However, a deeper understanding of the mechanisms by which NK cells influence gastric cancer progression remains necessary.\nMethods: We utilized the Cancer Genome Atlas (TCGA) database to acquire transcriptional profiles, clinical information, and mutation data for gastric cancer patients. R software and associated packages were employed for all analyses of this publicly available data.\nResults: We used multiple algorithms to evaluate the tumor microenvironment in gastric cancer samples. We performed differential expression analysis to pinpoint genes related to NK cells. Utilizing this data, we developed a prognostic model featuring three crucial NK cell-related genes: MAB21L2, ARPP21, and MUCL1. This model showed strong predictive performance in the training and validation groups. Consistently, patients identified as high-risk according to our model had worse overall survival rates. To further elucidate the biological differences between high-risk and low-risk patients, we performed enrichment analyses focusing on biological pathways and immune-related factors. Additionally, we observed a correlation between higher risk scores and non-responsiveness to treatment. Interestingly, high-risk patients were found to be potentially more sensitive to axitinib. We selected MUCL1 for further investigation due to its potential role in the model. While MUCL1 mRNA levels were elevated in both gastric cancer and paired normal tissues, protein expression analysis using the Human Protein Atlas database revealed a decrease in MUCL1 protein levels within tumor tissues.\nConclusions: Our findings contribute to a more comprehensive understanding of the role of NK cells in gastric cancer and highlight MUCL1 as a promising therapeutic target.", "claims": [{"id": "claim-method", "statement": "Initially, a univariate Cox proportional hazards model assessed the influence of each factor on overall survival. Factors showing a statistically significant link (p-value <0.05) were chosen for further examination. To address the high dimensionality and possible multicollinearity in the data, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to refine our selection of prognostic factors. LASSO regression is beneficial as it minimizes the risk of model overfitting through the penalization of the coefficients' absolute values. A multivariate Cox regression model was subsequently developed with the factors identified by the LASSO regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071629", "locator": "sections[5] \"Prognosis-related analysis\" paragraph 2", "quote": "Initially, a univariate Cox proportional hazards model assessed the influence of each factor on overall survival. Factors showing a statistically significant link (p-value <0.05) were chosen for further examination. To address the high dimensionality and possible multicollinearity in the data, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to refine our selection of prognostic factors. LASSO regression is beneficial as it minimizes the risk of model overfitting through the penalization of the coefficients' absolute values. A multivariate Cox regression model was subsequently developed with the factors identified by the LASSO regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39071629", "locator": "sections[27] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The three-gene prognostic model had satisfactory predictive performance in the validation cohort, with 1-year, 3-year, and 5-year AUC values of 0.749, 0.729, and 0.708, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39071629", "locator": "sections[19] \"Evaluation of prognosis model and clinical correlation\" paragraph 2", "quote": "ROC curves further confirmed the model's satisfactory ability to predict patient survival (Fig. 4C–E), with 1-year, 3-year, and 5-year AUC values of 0.749, 0.729, and 0.708, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The three-gene prognostic model had satisfactory predictive performance in the validation cohort, with 1-year, 3-year, and 5-year AUC values of 0.749, 0.729, and 0.708, respectively.", "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:39071629"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39071629", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e33759", "sha256": "sha256:839a5d29fe19079315000957bcffa7a7477a5922ce43fcab7b69421ab3b34429", "bytes": 38054, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e33759", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11276922", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "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": "Initially, a univariate Cox proportional hazards model assessed the influence of each factor on overall survival. Factors showing a statistically significant link (p-value <0.05) were chosen for further examination. To address the high dimensionality and possible multicollinearity in the data, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to refine our selection of prognostic factors. LASSO regression is beneficial as it minimizes the risk of model overfitting through the penalization of the coefficients' absolute values. A multivariate Cox regression model was subsequently developed with the factors identified by the LASSO regression."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39071629"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:25:25Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9848, "completion_tokens": 1774, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39071629", "type": "paper", "label": "pmid:39071629"}, {"id": "source:pmid:39071629", "type": "manuscript", "label": "PMID 39071629 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39071629", "type": "paper", "label": "pmid:39071629"}, {"id": "source:pmid:39071629", "type": "manuscript", "label": "PMID 39071629 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39071629", "type": "extraction", "used": ["source:pmid:39071629"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39071629", "parent_hash": "sha256:75f9f6fa326e14dff74df423922f534c59bffc7efc3369a45d011d31da32f44b", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"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": ["Initially, a univariate Cox proportional hazards model assessed the influence of each factor on overall survival. Factors showing a statistically significant link (p-value <0.05) were chosen for further examination. To address the high dimensionality and possible multicollinearity in the data, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to refine our selection of prognostic factors. LASSO regression is beneficial as it minimizes the risk of model overfitting through the penalization of the coefficients' absolute values. A multivariate Cox regression model was subsequently developed with the factors identified by the LASSO regression."], "order_basis": "analysis_only", "id": "assay:1"}]}