Development and Validation of TACE Refractoriness-Related Diagnostic and Prognostic Scores and Characterization of Tumor Microenvironment Infiltration in Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35493518", "kind": "science-record", "revision": 4, "content_hash": "sha256:56307c9d4a7e232bddc330940b9fca1e7730bc75db24c739e38ca6a962d8ff12", "created_at": "2026-11-01T18:14:22Z", "updated_at": "2026-11-01T22:06:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of TACE Refractoriness-Related Diagnostic and Prognostic Scores and Characterization of Tumor Microenvironment Infiltration in Hepatocellular Carcinoma.", "doi": "10.3389/fimmu.2022.869993", "pmid": "35493518", "authors": ["Qifan He", "Jian Yang", "Yonghai Jin"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Transcatheter arterial chemoembolization LIHC, Liver hepatocellular carcinoma; (TACE) is a valid therapeutic method for hepatocellular carcinoma (HCC). However, many patients respond poorly to TACE, thus leading to an adverse outcome. Therefore, finding new biomarkers for forecasting TACE refractoriness occurrence and prognosis becomes one of the current research priorities in the field of HCC treatment.\nMaterials and Methods: Based on microarray datasets and a high-throughput sequencing dataset, the TACE refractoriness-related genes (TRGs) were identified by differential expression analysis. LASSO and Cox regression were applied to construct TACE refractoriness diagnostic score (TRD score) and prognostic score (TRP score) and validated their accuracy in external datasets. Functional correlation of TRP score was analyzed by gene set variation analysis and Gene Ontology. CIBERSORT and IMMUNCELL AI algorithms were performed to understand the correlation between the two scores and immune activity. We further carried out the efficacy analysis of immunotherapy and targeted drugs in the different TRP score groups. Furthermore, a nomogram was built by integrating various independent prognostic factors and validated its effectiveness in different datasets.\nResults: We identified 487 TRGs combined with GSE104580 and TCGA datasets. Then four novel TRGs (TTK, EPO, SLC7A11, and PON1) were screened out to construct TRD score and TRP score models, and both two scores had good predictive ability in external datasets. Tumors with high TRP score show an immunosuppressive phenotype with more infiltrations of regulatory T cells and macrophages. Immunotherapy and chemotherapy response evaluation revealed patients with a high TRP score demonstrated well reactions to immune checkpoint inhibitors (ICIs) and sorafenib. TRP score, TNM stage, and cancer type were brought into the combined nomogram with optimum prediction.\nConclusions: Our research provided dependable and simplified methods for patients with HCC to assess tumors' susceptibility to TACE refractoriness and prognosis and guide patients' clinical therapy choices.", "claims": [{"id": "claim-method", "statement": "Subsequently, four TRGs (TTK, EPO, SLC7A11, and PON1) were further confirmed as the optimal combination parameters of prognosis prediction according to LASSO–Cox regression", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35493518", "locator": "sections[16] \"Construction of the TRP Score Prognostic Model\" paragraph 2", "quote": "Subsequently, four TRGs (TTK, EPO, SLC7A11, and PON1) were further confirmed as the optimal combination parameters of prognosis prediction according to LASSO–Cox regression"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35493518", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets analyzed was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO and Cox regression identified TTK, EPO, SLC7A11, and PON1 as the four prognostic genes used to construct the TRP and TRD scores, which showed good predictive ability in external datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35493518", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Then four novel TRGs (TTK, EPO, SLC7A11, and PON1) were screened out to construct TRD score and TRP score models, and both two scores had good predictive ability in external datasets."}, {"asset": "source:pmid:35493518", "locator": "sections[16] \"Construction of the TRP Score Prognostic Model\" paragraph 2", "quote": "Univariate Cox regression analysis found 25 TRGs significantly associated with OS in the training set. Subsequently, four TRGs (TTK, EPO, SLC7A11, and PON1) were further confirmed as the optimal combination parameters of prognosis prediction according to LASSO–Cox regression"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO and Cox regression identified TTK, EPO, SLC7A11, and PON1 as the four prognostic genes used to construct the TRP and TRD scores, which showed good predictive ability in external datasets.", "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:35493518"], "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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