Development and validation of a prognostic classifier based on HIF-1 signaling for hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32084009", "kind": "science-record", "revision": 8, "content_hash": "sha256:2dec8714fcc2e765257f2bd2e516d7e1befd21d92d8b36e7244466b2db0ab7e7", "created_at": "2026-11-01T15:40:56Z", "updated_at": "2026-11-01T22:01:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a prognostic classifier based on HIF-1 signaling for hepatocellular carcinoma.", "doi": "10.18632/aging.102820", "pmid": "32084009", "authors": ["Feiwen Deng", "Dong Chen", "Xiaoli Wei", "Shilin Lu", "Xuan Luo", "Jincan He", "Junting Liu", "Tiebao Meng", "Anli Yang", "Huanwei Chen"], "journal": "Aging", "year": 2020, "abstract": "HIF-1 (hypoxia-inducible factor 1) signaling played a vital role in HCC (hepatocellular carcinoma) prognosis. We aimed to establish an accurate risk scoring system for HCC prognosis prediction and treatment guidance. 424 samples from TCGA (The Cancer Genome Atlas) and 445 samples from GSE14520 dataset were included as the derivation and validation cohort, respectively. In the derivation cohort, prognostic relevant signatures were selected from sixteen HIF-1 related genes and LASSO regression was adopted for model construction. Tumor-infiltrating immune cells were calculated using CIBERSORT algorithm. HIF-1 signaling significantly increased in HCC samples compared with normal tissues. Scoring system based on SLC2A1, ENO1, LDHA and GAPDH exhibited a continuous predictive ability for OS (overall survival) in HCC patients. PCA and t-SNE analysis confirmed a reliable clustering ability of risk score in both cohorts. Patients were classified into high-risk and low-risk groups and the survival outcomes between the two groups showed significant differences. In the derivation cohort, Cox regression indicated the scoring system was an independent predictor for OS, which was validated in the validation cohort. Different infiltrating immune cells fraction and immune scores were also observed in different groups. Herein, a novel integrated scoring system was developed based on HIF-1 related genes, which would be conducive to the precise treatment of patients.", "claims": [{"id": "claim-method", "statement": "Eleven signatures were subjected to the LASSO Cox regression model using the glmnet package [29]. Risk scores were calculated using the generated coefficients and corresponding expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32084009", "locator": "sections[13] \"Development of risk score based on HIF-1 related genes\" paragraph 2", "quote": "Eleven signatures were subjected to the LASSO Cox regression model using the glmnet package [29]. Risk scores were calculated using the generated coefficients and corresponding expression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA dataset was obtained from TCGA using gdc-client. The mRNA expression matrices of GSE14520 were downloaded from the GEO dataset [28]. 424 samples from TCGA and 445 samples from GSE14520 were finally enrolled in our study as the derivation and validation cohort, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32084009", "locator": "sections[12] \"Study subjects\" paragraph 3", "quote": "The TCGA dataset was obtained from TCGA using gdc-client. 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