Predictive Models for HCC Prognosis, Recurrence Risk, and Immune Infiltration Based on Two Exosomal Genes: MYL6B and THOC2.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34466015", "kind": "science-record", "revision": 7, "content_hash": "sha256:98260565d4fc5e7ac7a6dac5a5879c0dbe2e85f8f6d11c92bb5e7adcb282cfb4", "created_at": "2026-11-01T15:41:50Z", "updated_at": "2026-11-01T22:04:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Predictive Models for HCC Prognosis, Recurrence Risk, and Immune Infiltration Based on Two Exosomal Genes: MYL6B and THOC2.", "doi": "10.2147/JIR.S315957", "pmid": "34466015", "authors": ["Jinyu Zhu", "Bufu Tang", "Yang Gao", "Suqin Xu", "Jianfei Tu", "Yajie Wang", "Weibin Yang", "Shiji Fang", "Qiaoyou Weng", "Zhongwei Zhao", "Min Xu", "Yang Yang", "Minjiang Chen", "Chenying Lu", "Jiansong Ji"], "journal": "Journal of inflammation research", "year": 2021, "abstract": "INTRODUCTION: Hepatocellular carcinoma (HCC) is a heterogeneous molecular disease with complex molecular pathogenesis that influences the efficacy of therapies. Exosomes play a crucial role in tumorigenesis and poor disease outcomes in HCC.\nOBJECTIVE: The aim of this study was to identify the optimal gene set derived from exosomes in HCC with substantial predictive value to construct models for determining prognosis, recurrence risk and diagnosis and to identify candidates suitable for immunotherapy and chemotherapy, thereby providing new ideas for the individualized treatment of patients and for improving prognosis.\nMETHODS: Weighted correlation network analysis (WGCNA) and univariate and multivariate Cox PH regression analyses were applied to identify exosome-related signatures in the TCGA and exoRbase databases associated with clinical relevance, immunogenic features and tumor progression in HCC. Cell experiments were performed to further confirm the oncogenic effect of MYL6B and THOC2.\nRESULTS: The models for prognosis and recurrence risk prediction were built based on two exosomal genes (MYL6B and THOC2) and were confirmed to be independent predictive factors with superior predictive performance. Patients with high prognostic risk had poorer prognosis than patients with low prognostic risk in all HCC datasets, namely, the TCGA cohort (HR=2.5, P<0.001), the ICGC cohort (HR=3.15, P<0.001) and the GSE14520 cohort (HR=1.85, P=0.004). A higher recurrence probability was found in HCC patients with high recurrence risk than in HCC patients with low recurrence risk in the TCGA cohort (HR=2.44, P<0.001) and the GSE14520 cohort (HR=1.54, P=0.025). High prognostic risk patients had higher expression of immune checkpoint genes, such as PD1, B7H3, B7H5, CTLA4 and TIM3 (P<0.05). Diagnostic models based on the same two genes were able to accurately distinguish HCC patients from normal individuals and HCC from dysplastic nodules.\nCONCLUSION: Our findings lay the foundation for identifying molecular markers to increase the early detection rate of HCC, improve disease outcomes, and determine more effective individualized treatment options for patients.", "claims": [{"id": "claim-method", "statement": "The comparison of the mean of two independent samples was performed by Student’s t-test or Wilcoxon test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34466015", "locator": "sections[13] \"Statistical Analysis\" paragraph 2", "quote": "The comparison of the mean of two independent samples was performed by Student’s t-test or Wilcoxon test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data and materials used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34466015", "locator": "sections[30] \"Data Sharing Statement\" paragraph 2", "quote": "The data and materials used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:34466015"], "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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