Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38317886", "kind": "science-record", "revision": 3, "content_hash": "sha256:8d71771b6744ee0f3cfa08278b39997c1294d94cdc9fa9c898d1c348be088955", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:11:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model.", "doi": "10.1016/j.heliyon.2024.e24861", "pmid": "38317886", "authors": ["Hongru Cao", "Ping Huang", "Jiawei Qiu", "Xiaohui Gong", "Hongfei Cao"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Various studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.\nMethods: We integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.\nResults: To describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.\nConclusions: Taken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.", "claims": [{"id": "claim-method", "statement": "LASSO-Cox regression analysis, including 16 type immune cells, was performed in the internal training cohort and identified 5 type signature immune cells associated with HCC risk (Fig. 2A and B), including 3 high-risk immune cells (T.cells.CD4.memory.resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38317886", "locator": "sections[13] \"Establishment and validation of immune-related prognostic model\" paragraph 2", "quote": "LASSO-Cox regression analysis, including 16 type immune cells, was performed in the internal training cohort and identified 5 type signature immune cells associated with HCC risk (Fig. 2A and B), including 3 high-risk immune cells (T.cells.CD4.memory.resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article. 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:38317886", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this published article. 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": "A five-cell immune prognostic signature was developed with LASSO-Cox regression and showed stable prognostic performance in internal training, internal validation, and external validation cohorts.", "kind": "quantitative", "target": {"value": 0.725, "unit": "auc", "tolerance": null, "source": "The ROC curve at 1 and 3 years was generated and the AUC was 0.725 and 0.696 respectively (Fig. 2F), while they were 0.727, and 0.788 in the internal validation cohort (Figs. 2G), 0.698 and 0.726 in the external validation cohort respectively (Fig. 2H)."}, "evidence": [{"asset": "source:pmid:38317886", "locator": "sections[13] \"Establishment and validation of immune-related prognostic model\" paragraph 2", "quote": "LASSO-Cox regression analysis, including 16 type immune cells, was performed in the internal training cohort and identified 5 type signature immune cells associated with HCC risk (Fig. 2A and B), including 3 high-risk immune cells (T.cells.CD4.memory.resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8)."}, {"asset": "source:pmid:38317886", "locator": "sections[13] \"Establishment and validation of immune-related prognostic model\" paragraph 4", "quote": "The ROC curve at 1 and 3 years was generated and the AUC was 0.725 and 0.696 respectively (Fig. 2F), while they were 0.727, and 0.788 in the internal validation cohort (Figs. 2G), 0.698 and 0.726 in the external validation cohort respectively (Fig. 2H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-cell immune prognostic signature was developed with LASSO-Cox regression and showed stable prognostic performance in internal training, internal validation, and external validation cohorts.", "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:38317886"], "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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