Identification and validation of a prognostic model based on four genes related to satellite nodules in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38972883", "kind": "science-record", "revision": 3, "content_hash": "sha256:b4bc1ae60abcb1a255cc73a09f98932e36b626847ac62d48830abca280a47c24", "created_at": "2026-11-01T20:29:03Z", "updated_at": "2026-11-01T22:12:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a prognostic model based on four genes related to satellite nodules in hepatocellular carcinoma.", "doi": "10.1038/s41598-024-66610-z", "pmid": "38972883", "authors": ["Feng Liu", "Tinghua Yan", "Dan Cui", "Jinhua Jiang"], "journal": "Scientific reports", "year": 2024, "abstract": "Satellite nodules is a key clinical characteristic which has prognostic value of hepatocellular carcinoma (HCC). Currently, there is no gene-level predictive model for Satellite nodules in liver cancer. For the 377 HCC cases collected from the dataset of Cancer Genome Atlas (TCGA), their original pathological data were analyzed to extract information regarding satellite nodules status as well as other relevant pathological data. Then, this study employed statistical modeling for prognostic model establishment in TCGA, and validation in International Cancer Genome Consortium (ICGC) cohorts and GSE76427. Through rigorous statistical analyses, 253 differential satellite nodules-related genes (SNRGs) were identified, and four key genes related to satellite nodules and prognosis were selected to construct a prognostic model. The high-risk group predicted by our model exhibited an unfavorable overall survival (OS) outlook and demonstrated an association with adverse worse clinical characteristics such as larger tumor size, higher alpha-fetoprotein, microvascular invasion and advanced stage. Moreover, the validation of the model's prognostic value in the ICGC and GSE76427 cohorts mirrored that of the TCGA cohort. Besides, the high-risk group also showed higher levels of resting Dendritic cells, M0 macrophages infiltration, alongside decreased levels of CD8+ T cells and γδT cells infiltration. The prognostic model based on SNRGs can reliability predict the OS of HCC and is likely to have predictive value of immunotherapy for HCC.", "claims": [{"id": "claim-method", "statement": "LASSO regression was performed to mitigate the risk of overfitting among the eight genes. Subsequently, Cox regression analysis was conducted to formulate a prognostic model centered on the presence of satellite nodules.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38972883", "locator": "sections[17] \"Construction and identification of prognostic models\" paragraph 2", "quote": "LASSO regression was performed to mitigate the risk of overfitting among the eight genes. Subsequently, Cox regression analysis was conducted to formulate a prognostic model centered on the presence of satellite nodules."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in the study. The RNA-seq data of TCGA_LIHC, ICGC_ LIRI_JP and GSE76427 were separately from https://portal.gdc.cancer.gov/, https://daco.icgc.org/ and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76427 .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38972883", "locator": "sections[28] \"notes\" paragraph 1", "quote": "Publicly available datasets were analyzed in the study. The RNA-seq data of TCGA_LIHC, ICGC_ LIRI_JP and GSE76427 were separately from https://portal.gdc.cancer.gov/, https://daco.icgc.org/ and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76427 ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used TCGA LIHC RNA expression and pathological data, applied differential-expression analysis, LASSO regression, and Cox regression to construct a four-gene HCC prognostic model, and validated the model in the ICGC LIRI-JP and GSE76427 cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38972883", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Through rigorous statistical analyses, 253 differential satellite nodules-related genes (SNRGs) were identified, and four key genes related to satellite nodules and prognosis were selected to construct a prognostic model."}, {"asset": "source:pmid:38972883", "locator": "sections[3] \"Data acquisition and preprocessing\" paragraph 2", "quote": "The RNA expression data, pathological data and clinical data of The Cancer Genome Atlas liver hepatocellular carcinoma cohort (TCGA LIHC) were downloaded from the official website of Genomic Data Commons using R package “TCGAbiolinks”."}, {"asset": "source:pmid:38972883", "locator": "sections[17] \"Construction and identification of prognostic models\" paragraph 2", "quote": "LASSO regression was performed to mitigate the risk of overfitting among the eight genes. 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