Relationship Between CNVs and Immune Cells Infiltration in Gastric Tumor Microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35754804", "kind": "science-record", "revision": 4, "content_hash": "sha256:ac811685547d4256bfdced0135d35a3d8a3f3e661069685b1e5f7d59a1e370c9", "created_at": "2026-11-01T18:14:46Z", "updated_at": "2026-11-01T22:06:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Relationship Between CNVs and Immune Cells Infiltration in Gastric Tumor Microenvironment.", "doi": "10.3389/fgene.2022.869967", "pmid": "35754804", "authors": ["Fazhan Li", "Huijuan Wen", "Ihtisham Bukhari", "Bin Liu", "Chenxu Guo", "FeiFei Ren", "Youcai Tang", "Yang Mi", "Pengyuan Zheng"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Gastric cancer (GC) is a highly fatal and common malignancy of the digestive system. Recent therapeutic advancements have significantly improved the clinical outcomes in GC, but due to the unavailability of suitable molecular targets, a large number of patients do not respond to the immune checkpoint inhibitors (ICI) therapy. To identify and validate potential therapeutic and prognostic targets of gastric cancer, we used the \"inferCNV\" R package for analyzing single-cell sequencing data (GSE112302) of GC and normal epithelial cells. First, by using LASSO, we screened genes that were highly correlated with copy number variations (CNVs). Therefrom, five gene signature (CPVL, DDC, GRTP1, ONECUT2, and PRSS21) was selected by cross-validating the prognosis and risk management with the GC RNA-seq data obtained from GEO and TCGA. Moreover, the correlation analyses between CNVs of these genes and immune cell infiltration in gastric cancer identified CPVL as a potential prognostic marker. Finally, CPVL showed high expression in gastric cancer samples and cell lines, then siRNA-mediated silencing of CPVL expression in gastric cancer cells showed significant proliferation arrest in MGC803 cells. Here, we conclude that CNVs are key regulators of the immune cells infiltration in gastric TME as well as cancer development, and CPVL could potentially be used as a prognostic and therapeutic marker in gastric cancer.", "claims": [{"id": "claim-method", "statement": "The differentially expressed genes (DEGs) in GC tissues were screened by the least absolute shrinkage and selection operator (LASSO) Cox regression model (McEligot et al., 2020).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35754804", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "The differentially expressed genes (DEGs) in GC tissues were screened by the least absolute shrinkage and selection operator (LASSO) Cox regression model (McEligot et al., 2020)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35754804", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on that we constructed and validated a prediction model for clinical prognosis, ROC curve (AUC = 0.626) (Figures 4F,G), and the relationship between multiple factors and survival rate was analyzed by nomogram (Figure 4H).", "kind": "quantitative", "target": {"value": 0.626, "unit": "auc", "tolerance": null, "source": "Based on that we constructed and validated a prediction model for clinical prognosis, ROC curve (AUC = 0.626) (Figures 4F,G), and the relationship between multiple factors and survival rate was analyzed by nomogram (Figure 4H)."}, "evidence": [{"asset": "source:pmid:35754804", "locator": "sections[17] \"Hub Genes Correlated With CNV and Prognosis\" paragraph 4", "quote": "Based on that we constructed and validated a prediction model for clinical prognosis, ROC curve (AUC = 0.626) (Figures 4F,G), and the relationship between multiple factors and survival rate was analyzed by nomogram (Figure 4H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on that we constructed and validated a prediction model for clinical prognosis, ROC curve (AUC = 0.626) (Figures 4F,G), and the relationship between multiple factors and survival rate was analyzed by nomogram (Figure 4H).", "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:35754804"], "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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No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "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. 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Moreover, t", "nv-scores. The expression levels of genes in transcriptome data were normalized by log2 transformation."], "order_basis": "paper_text_order:840", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["gastric cancer samples and cell lines, then siRNA-mediated silencing of CPVL expression in gas"], "order_basis": "paper_text_order:1126", "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 differentially expressed genes (DEGs) in GC tissues were screened by the least absolute shrinkage and selection operator (LASSO) Cox regression model (McEligot et al., 2020)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}