Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36189226", "kind": "science-record", "revision": 4, "content_hash": "sha256:7152fef6e00927922b96f4f018b0f9904ae447d35dc00358a6b529183aa192ae", "created_at": "2026-11-01T18:15:29Z", "updated_at": "2026-11-01T22:07:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment.", "doi": "10.3389/fimmu.2022.975762", "pmid": "36189226", "authors": ["Genhao Zhang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Regulatory T-Cells (Tregs) are important in the progression of hepatocellular cancer (HCC). The goal of this work was to look into Tregs-related genes and develop a Tregs-related prognostic model. We used the weighted gene co-expression network analysis (WGCNA) to look for Tregs-related genes in the TCGA, ICGC, and GSE14520 cohorts and then used the non-negative matrix factorization (NMF) algorithm to find Tregs-related subpopulations. The LASSO-Cox regression approach was used to determine Tregs-related genes, which were then condensed into a risk score. A total of 153 overlapping genes among the three cohorts were considered Tregs-related genes. Based on these genes, two Tregs-associated clusters that varied in both prognostic and biological characteristics were identified. When compared with Cluster 1, Cluster 2 was a TME-exhausted HCC subpopulation with substantial immune cell infiltration but a poor prognosis. Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets. Patients in the high-risk scores group with bad survival outcomes were replete with immune/inflammatory responses, but exhausted T cells and elevated PD-1 and PD-L1 expression. The results of qRT-PCR and immunohistochemical staining (IHC) analysis in clinical tissue samples confirmed the above findings. Moreover, the signature also accurately predicted anti-PD-L1 antibody responses in the IMvigor210 dataset. Finally, HMOX1, MMP9, and TNFRSF11B were expressed differently in Hep3B and Huh7 cells after being treated with a PD1/PD-L1 inhibitor. In conclusion, our study uncovered a Tregs-related prognostic model that could identify TME- exhausted subpopulations and revealed that PD1/PD-L1 inhibitors could alter the expression levels of HMOX1, MMP9, and TNFRSF11B in Hep3B and Huh7 cells, which might help us better understand Tregs infiltration and develop personalized immunotherapy treatments for HCC patients.", "claims": [{"id": "claim-method", "statement": "The Tregs-related genes with prognostic significance were then investigated using the LASSO-Cox regression technique and a classifier linked with prognosis was established.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36189226", "locator": "sections[8] \"Formation and validation of Tregs-related prognostic risk scoring model\" paragraph 2", "quote": "The Tregs-related genes with prognostic significance were then investigated using the LASSO-Cox regression technique and a classifier linked with prognosis was established."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We can find the datasets analyzed in this study at the https://xena.ucsc.edu, https://dcc.icgc.org/projects/LIRI-JP, and https://www.ncbi.nlm.nih.gov/geo. The accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36189226", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "We can find the datasets analyzed in this study at the https://xena.ucsc.edu, https://dcc.icgc.org/projects/LIRI-JP, and https://www.ncbi.nlm.nih.gov/geo. The accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed and validated a five-gene Tregs-related prognostic model for HCC that identifies TME-exhausted subpopulations and predicts anti-PD-L1 response.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36189226", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed and validated a five-gene Tregs-related prognostic model for HCC that identifies TME-exhausted subpopulations and predicts anti-PD-L1 response.", "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:36189226"], "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:36189226", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.975762", "sha256": "sha256:ca3145e645eab8ede0e2b971499f9608e85e586968d6b63af4acf272ed9c695d", "bytes": 49339, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.975762", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9521506", "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": "We can find the datasets analyzed in this study at the https://xena.ucsc.edu, https://dcc.icgc.org/projects/LIRI-JP, and https://www.ncbi.nlm.nih.gov/geo. 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