Prognostic Hypoxia-Angiogenesis-Related Gene Signature in Hepatocellular Carcinoma, in Which HILPDA Contributes to Tumor Progression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39219818", "kind": "science-record", "revision": 3, "content_hash": "sha256:e428924fcf222f1388328de1a1c97d309e022f29731fc6bfee75cb31dfcac6c2", "created_at": "2026-11-01T20:29:20Z", "updated_at": "2026-11-01T22:12:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic Hypoxia-Angiogenesis-Related Gene Signature in Hepatocellular Carcinoma, in Which HILPDA Contributes to Tumor Progression.", "doi": "10.2147/JIR.S476388", "pmid": "39219818", "authors": ["Sheng Wang", "Wufei Ye", "Kui Yang", "Xiongwen Lv", "Jiajie Luan"], "journal": "Journal of inflammation research", "year": 2024, "abstract": "Objective: Hepatocellular carcinoma (HCC) is the predominant form of liver cancer. Hypoxia can be involved in HCC tumor growth, invasion and metastasis through inducing angiogenesis. Nevertheless, the assessment of the impact of hypoxia and angiogenesis on the prognosis of HCC remains inadequate.\nMethods: According to hypoxia-angiogenesis-related genes (HARGs) expression information and clinical data from patients within the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a prognostic model (HARG-score) using bioinformatic tools. In addition to assessing the predictive ability of this prognostic model in both Liver Cancer-Riken-Japan (LIRI-JP) and GSE14520 cohorts, we analyzed the correlation between HARG-score and clinical characteristics, immune infiltration and immunotherapy efficacy. Moreover, we investigated the exact role and underlying mechanism of key HARGs through molecular experiments.\nResults: We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs. The findings demonstrated that HARG-score was a good predictor of the prognosis of HCC patients from distinct cohorts and was correlated with clinical characteristics and immune infiltration. Furthermore, the HARG-score was identified as an independent prognostic factor. Lower HARG-score implied greater immunotherapy efficacy and better response. The expression and prognostic significance of these 5 genes were additionally validated in clinical data. In addition, experimental data revealed that the key gene HILPDA contributes to the progression of HCC through facilitating angiogenesis and affecting the expression of cytotoxic T-lymphocyte-associated protein 4 (CTLA4).\nConclusion: HARG-score has promising applications in prognosis prediction of HCC patients, in which HILPDA may be a latent prognostic biomarker and therapeutic target, providing a foundation for further research and treatment of HCC.", "claims": [{"id": "claim-method", "statement": "In the training cohort (TCGA-LIHC), we identified the differentially expressed HARGs related to overall survival using univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox analysis utilized the R package “glmnet” to eliminate overfitting among prognostic genes and conduct penalty parameter tuning through 10-fold cross validation, aiming to reduce the number of prognostic genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39219818", "locator": "sections[5] \"Construction of Hypoxia-Angiogenesis-Related Prognostic Model HARG-Score\" paragraph 2", "quote": "In the training cohort (TCGA-LIHC), we identified the differentially expressed HARGs related to overall survival using univariate Cox regression analysis."}, {"asset": "source:pmid:39219818", "locator": "sections[5] \"Construction of Hypoxia-Angiogenesis-Related Prognostic Model HARG-Score\" paragraph 2", "quote": "Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox analysis utilized the R package “glmnet” to eliminate overfitting among prognostic genes and conduct penalty parameter tuning through 10-fold cross validation, aiming to reduce the number of prognostic genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data relevant to our study are summarized in the paper or included in Supplementary figures and tables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39219818", "locator": "sections[36] \"Data Sharing Statement\" paragraph 2", "quote": "Data relevant to our study are summarized in the paper or included in Supplementary figures and tables."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39219818", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs.", "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:39219818"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39219818", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S476388", "sha256": 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