Deciphering the Prognostic and Therapeutic Value of a Gene Model Associated with Two Aggressive Hepatocellular Carcinoma Phenotypes Using Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39634327", "kind": "science-record", "revision": 4, "content_hash": "sha256:943f64da70df3285bb956e7d0f20a8128104e2ec64c5227ca81b2e1a8ea8cf6a", "created_at": "2026-11-01T20:29:54Z", "updated_at": "2026-11-01T22:55:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deciphering the Prognostic and Therapeutic Value of a Gene Model Associated with Two Aggressive Hepatocellular Carcinoma Phenotypes Using Machine Learning.", "doi": "10.2147/JHC.S480358", "pmid": "39634327", "authors": ["Junhan Pan", "Cong Zhang", "Huizhen Huang", "Yanyan Zhu", "Yuhao Zhang", "Shuzhen Wu", "Yan-Ci Zhao", "Feng Chen"], "journal": "Journal of hepatocellular carcinoma", "year": 2024, "abstract": "Background: Macrotrabecular-massive (MTM) and vessels encapsulating tumor clusters (VETC)-hepatocellular carcinoma (HCC) are aggressive histopathological phenotypes with significant prognostic implications. However, the molecular markers associated with MTM-HCC and VETC-HCC and their implications for clinical outcomes and therapeutic strategies remain unclear.\nMethods: Utilizing the TCGA-LIHC cohort, we employed machine learning techniques to develop a prognostic risk score based on MTM and VETC-related genes. The performance of the risk score was assessed by investigating various aspects including clinical outcomes, biological pathways, treatment responses, drug sensitivities, tumor microenvironment, and molecular subclasses. To validate the risk score, additional data from the ICGC-JP, GSE14520, GSE104580, GSE109211, and an in-house cohort were collected and analyzed.\nResults: The machine learning algorithm established a 4-gene-based risk score. High-risk patients had significantly worse prognosis compared to low-risk patients, with the risk score being associated with malignant progression of HCC. Functionally, the high-risk group exhibited enrichment in tumor proliferation pathways. Additionally, patients in the low-risk group exhibited improved response to TACE and sorafenib treatments compared to the high-risk group. In contrast, the high-risk group exhibited reduced sensitivity to immunotherapy and increased sensitivity to paclitaxel. In the in-house cohort, high-risk patients displayed higher rates of early recurrence, along with an increased frequency of elevated alpha-fetoprotein, microvascular invasion, and aggressive MRI features associated with HCC.\nConclusion: This study has successfully developed a risk score based on MTM and VETC-related genes, providing a promising tool for prognosis prediction and personalized treatment strategies in HCC patients.", "claims": [{"id": "claim-method", "statement": "Finally, the overlapping features obtained from both the repeated lasso results and the random forest results were integrated into a stepwise multivariate Cox regression model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39634327", "locator": "sections[5] \"Development of MTM and VETC-Related Risk Score\" paragraph 2", "quote": "Finally, the overlapping features obtained from both the repeated lasso results and the random forest results were integrated into a stepwise multivariate Cox regression model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression data described in this study were deposited in TCGA, GEO and ICGC databases. Additional relevant data supporting the findings of this study are available from the corresponding authors upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39634327", "locator": "sections[29] \"Data Sharing Statement\" paragraph 2", "quote": "Gene expression data described in this study were deposited in TCGA, GEO and ICGC databases. Additional relevant data supporting the findings of this study are available from the corresponding authors upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The machine learning algorithm established a 4-gene-based risk score.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39634327", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The machine learning algorithm established a 4-gene-based risk score."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The machine learning algorithm established a 4-gene-based risk score.", "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:39634327"], "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:GSE104580"], "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:GSE104580"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39634327", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JHC.S480358", "sha256": "sha256:4578c068c3df0525cf79fec1951e99eb97f98c1f6a37da79884482d87be10507", "bytes": 53466, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JHC.S480358", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11614714", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104580", "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=GSE104580", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104580", "version": null, "note": "Gene expression data described in this study were deposited in TCGA, GEO and ICGC databases. 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