Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through in vitro experiments.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39872516", "kind": "science-record", "revision": 4, "content_hash": "sha256:d3432b6c4b7af9d45f4f51d53419e5a3375e897148e662cd1f2d3d13de403621", "created_at": "2026-11-01T20:30:09Z", "updated_at": "2026-11-01T22:56:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through in vitro experiments.", "doi": "10.3389/fimmu.2024.1478904", "pmid": "39872516", "authors": ["Li Pengping", "Yin Kexin", "Xie Yuwei", "Sun Ke", "Li Rongguo", "Wang Zhenyu", "Jin Haigang", "Wang Shaowen", "Huang Yuqing"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Introduction: While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form with a median survival time of just five months. Conventional therapies offer limited benefits for this type of thyroid cancer. Our study aims to identify ATC patients who might bene t from immunotherapy.\nMethods: Our study uses multiple algorithms by R4.2.0, and gene expression and clinical data are collected from TCGA, GEO and local cohort. In vitro experiments, such as western blot and immunofluorescence staining, are performed.\nResults: Using a set of five genes uniquely expressed across various types of thyroid cancer, we developed a machine-learning model to distinguish each type within the GEO dataset of thyroid cancer patients (GSE60542, GSE76039, GSE33630, GSE53157, GSE65144, GSE29265, GSE82208, GSE27155, GSE58545, GSE54958, and GSE32662). These genes allowed us to stratify ATC into three distinct groups, each exhibiting significantly different responses to anti-PD1 therapy as determined by consensus clustering. Through weighted gene co-expression network analysis (WGCNA), we identified 12 differentially expressed genes closely associated with immunotherapy outcomes. This led to the creation of a refined signature for predicting ATC's immune responsiveness to anti-PD1 therapy, which was further validated using thyroid cancer cohorts from TCGA and nine melanoma cohorts from clinical trials. Among the 12 genes, HLF stood out due to its strong association with various cancer hallmarks.\nDiscussion: Our study revealed that HLF impedes ATC progression by down-regulating the epithelial-to-mesenchymal transition (EMT) pathway, reducing T cell exhaustion, and increasing sensitivity to sorafenib, as demonstrated through our in-vitro experiments.", "claims": [{"id": "claim-method", "statement": "Firstly, WGCNA (Weighted Gene Co-Expression Network Analysis) was applied to screen out hub genes using the WGCNA package in R4.2.0. The most correlated gene sets (both negative and positive) were collected for subsequent machine learning. AI modeling for ATC subgroup stratification was developed using six AI functions: extreme gradient boosting (XGboost, xgboost package in R4.2.0), support vector machine (SVM, e1071 packages in R4.2.0), multi-logistic (nnet packages in R4.2.0), random forest (RF, randomForest package in R4.2.0), deep learning (DL, h2o package in R4.2.0), and K-Nearest Neighbor (KNN, kknn package in R4.2.0).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39872516", "locator": "sections[10] \"The 12-gene signature construction\" paragraph 2", "quote": "Firstly, WGCNA (Weighted Gene Co-Expression Network Analysis) was applied to screen out hub genes using the WGCNA package in R4.2.0. The most correlated gene sets (both negative and positive) were collected for subsequent machine learning. AI modeling for ATC subgroup stratification was developed using six AI functions: extreme gradient boosting (XGboost, xgboost package in R4.2.0), support vector machine (SVM, e1071 packages in R4.2.0), multi-logistic (nnet packages in R4.2.0), random forest (RF, randomForest package in R4.2.0), deep learning (DL, h2o package in R4.2.0), and K-Nearest Neighbor (KNN, kknn package in R4.2.0)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39872516", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using a set of five genes uniquely expressed across various types of thyroid cancer, we developed a machine-learning model to distinguish each type within the GEO dataset of thyroid cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39872516", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using a set of five genes uniquely expressed across various types of thyroid cancer, we developed a machine-learning model to distinguish each type within the GEO dataset of thyroid cancer patients (GSE60542, GSE76039, GSE33630, GSE53157, GSE65144, GSE29265, GSE82208, GSE27155, GSE58545, GSE54958, and GSE32662)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using a set of five genes uniquely expressed across various types of thyroid cancer, we developed a machine-learning model to distinguish each type within the GEO dataset of thyroid cancer patients.", "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:39872516"], "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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