AVEN: a novel oncogenic biomarker with prognostic significance and implications of AVEN-associated immunophenotypes in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37908231", "kind": "science-record", "revision": 2, "content_hash": "sha256:8075e860b68d998997c80ef13603f3a5cb19cebac512005666926e528820f6b1", "created_at": "2026-11-01T19:02:47Z", "updated_at": "2026-11-01T21:31:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "AVEN: a novel oncogenic biomarker with prognostic significance and implications of AVEN-associated immunophenotypes in lung adenocarcinoma.", "doi": "10.3389/fmolb.2023.1265359", "pmid": "37908231", "authors": ["Dengxia Fan", "Moses Yang", "Hye Jung Lee", "Jeong Hee Lee", "Hong Sook Kim"], "journal": "Frontiers in molecular biosciences", "year": 2023, "abstract": "Introduction: AVEN, an apoptosis and caspase activation inhibitor, has been associated with adverse clinical outcomes and poor prognosis in Acute myeloid leukemia (AML). Targeting AVEN in AML improves apoptosis sensitivity and chemotherapy efficacy, making it a promising therapeutic target. However, AVEN's role has not been studied in solid tumors. Therefore, our study investigated AVEN as a prognostic biomarker in a more comprehensive manner and developed an AVEN-derived prognostic model in Lung adenocarcinoma (LUAD). Method: Pan-cancer analysis was performed to examine AVEN expression in 33 cancer types obtained from the TCGA database. GEPIA analysis was used to determine the predictive value of AVEN in each cancer type with cancer-specific AVEN expression. Lung Adenocarcinomas (LUAD) patients were grouped into AVENhigh and AVENlow based on AVEN expression level. Differentially expressed genes (DEGs) and pathway enrichment analysis were performed to gain insight into the biological function of AVEN in LUAD. In addition, several deconvolution tools, including Timer, CIBERSORT, EPIC, xCell, Quanti-seq and MCP-counter were used to explore immune infiltration. AVEN-relevant prognostic genes were identified by Random Survival Forest analysis via univariate Cox regression. The AVEN-derived genomic model was established using a multivariate-Cox regression model and GEO datasets (GSE31210, GSE50081) were used to validate its prognostic effect. Results: AVEN expression was increased in several cancer types compared to normal tissue, but its impact on survival was only significant in LUAD in the TCGA cohort. High AVEN expression was significantly correlated with tumor progression and shorter life span in LUAD patients. Pathway analysis was performed with 838 genes associated with AVEN expression and several oncogenic pathways were altered such as the Cell cycle, VEGFA-VEGFR2 pathway, and epithelial-mesenchymal-transition pathway. Immune infiltration was also analyzed, and less infiltrated B cells was observed in AVENhigh patients. Furthermore, an AVEN-derived genomic model was established, demonstrating a reliable and improved prognostic value in TCGA and GEO databases. Conclusion: This study provided evidence that AVEN is accumulated in LUAD compared to adjacent tissue and is associated with poor survival, high tumor progression, and immune infiltration alteration. Moreover, the study introduced the AVEN-derived prognostic model as a promising prognosis tool for LUAD.", "claims": [{"id": "claim-method", "statement": "To determine the correlation between each gene from the DEGs and the overall survival of LUAD patients, Univariate Cox Regression model was employed. AVEN-derived genes with p-value < 0.01 were regarded as AVEN-derived prognostic factors. Followed by Random Survival Forest analysis, the relative importance of each gene was calculated. Genes with relative importance >0.5 were used in the Multivariate Cox Regression model. Step forward Cox regression was utilized to optimize the model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37908231", "locator": "sections[9] \"Establishment of an AVEN-derived prognostic genes model\" paragraph 2", "quote": "To determine the correlation between each gene from the DEGs and the overall survival of LUAD patients, Univariate Cox Regression model was employed. AVEN-derived genes with p-value < 0.01 were regarded as AVEN-derived prognostic factors. Followed by Random Survival Forest analysis, the relative importance of each gene was calculated. Genes with relative importance >0.5 were used in the Multivariate Cox Regression model. Step forward Cox regression was utilized to optimize the model."}], "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:37908231", "locator": "sections[21] \"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": "The study established an AVEN-derived genomic model with reliable and improved prognostic value in TCGA and GEO databases.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37908231", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Furthermore, an AVEN-derived genomic model was established, demonstrating a reliable and improved prognostic value in TCGA and GEO databases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established an AVEN-derived genomic model with reliable and improved prognostic value in TCGA and GEO databases.", "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:37908231"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37908231", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmolb.2023.1265359", "sha256": "sha256:e0fc92d44f61b5862effdf3541d41f90fa056c130c94c2f4d7042982190c108e", "bytes": 39174, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmolb.2023.1265359", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10613694", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "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=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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AVEN-derived genes with p-value < 0.01 were regarded as AVEN-derived prognostic factors. Followed by Random Survival Forest analysis, the relative importance of each gene was calculated. Genes with relative importance >0.5 were used in the Multivariate Cox Regression model. Step forward Cox regression was utilized to optimize the model."}}, {"id": "method-2", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "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": "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE50081", "GSE31210"], "resolved_accessions": ["GSE50081", "GSE31210"], "statements": ["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."], "basis": "The paper names GSE50081, GSE31210 in GEO and the accession resolves in the repository. 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Targeting AVEN in AML improves apoptosis sensitivity and chemotherapy efficacy, makin", "Introduction: AVEN, an apoptosis and caspase activation inhibitor, has been associated wi"], "order_basis": "paper_text_order:140", "id": "assay:1"}, {"role": "data_generation", "definition_id": "methylation_array", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["nvolution tools, including Timer, CIBERSORT, EPIC, xCell, Quanti-seq and MCP-counter were used"], "order_basis": "paper_text_order:1190", "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": ["To determine the correlation between each gene from the DEGs and the overall survival of LUAD patients, Univariate Cox Regression model was employed. AVEN-derived genes with p-value < 0.01 were regarded as AVEN-derived prognostic factors. Followed by Random Survival Forest analysis, the relative importance of each gene was calculated. Genes with relative importance >0.5 were used in the Multivariate Cox Regression model. Step forward Cox regression was utilized to optimize the model."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}