Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38694506", "kind": "science-record", "revision": 5, "content_hash": "sha256:089a6bf388c80d57c633da7e2b7408e82343e44ead09b45c31819866336d51ba", "created_at": "2026-11-01T19:20:39Z", "updated_at": "2026-11-01T22:54:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.", "doi": "10.3389/fimmu.2024.1244392", "pmid": "38694506", "authors": ["Erbao Chen", "Zhilin Zou", "Rongyue Wang", "Jie Liu", "Zhen Peng", "Zhe Gan", "Zewei Lin", "Jikui Liu"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Objective: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.\nMethods: We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the \"one-class logistic regression (OCLR)\" algorithm to obtain the mRNAsi of patients with HCC. We performed \"unsupervised consensus clustering\" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.\nResults: We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.\nConclusion: The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.", "claims": [{"id": "claim-method", "statement": "We constructed the mRNAsi model using the “one-class logistic regression (OCLR)” algorithm (5). Next, we used “Spearman correlation analysis” to determine the similarity between cancer cells of patients with HCC and stem cells.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38694506", "locator": "sections[4] \"Calculating mRNAsi\" paragraph 2", "quote": "We constructed the mRNAsi model using the “one-class logistic regression (OCLR)” algorithm (5). Next, we used “Spearman correlation analysis” to determine the similarity between cancer cells of patients with HCC and stem cells."}], "state": "complete"}, {"id": "claim-data-access", "statement": "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. All date and materials are available from the corresponding authors upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38694506", "locator": "sections[30] \"Data availability statement\" paragraph 2", "quote": "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. All date and materials are available from the corresponding authors upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A nine-gene stemness classifier predicts HCC prognosis and response to immune checkpoint blockade and was validated in the independent GSE14520 cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38694506", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient’s prognosis and response to ICBs."}, {"asset": "source:pmid:38694506", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Further, the model was validated in an independent GSE14520 dataset and performed well."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A nine-gene stemness classifier predicts HCC prognosis and response to immune checkpoint blockade and was validated in the independent GSE14520 cohort.", "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:38694506"], "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:38694506", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1244392", "sha256": "sha256:57f1e85dfb2e3689725e0f42d53cde53548460000f7fc0b2e930f7334199c55c", "bytes": 56503, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1244392", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11061862", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "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=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "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. All date and materials are available from the corresponding authors upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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. 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We also found knockdown UCK2 changed expressions of immune genes in"], "order_basis": "paper_text_order:2645", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["C, we imported and analyzed the unnormalized RNA-Seq data using the “Cell-type Identification by", "C First, we used the “OCLR” algorithm on the transcriptomic data of patients in TCGA-LIHC cohort to obta"], "order_basis": "paper_text_order:3927", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["a the right flank to develop immunocompetent murine models of HCC. 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