Exploring the role of alternative lengthening of telomere-related genes in diagnostic modeling for non-alcoholic fatty liver disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39638831", "kind": "science-record", "revision": 4, "content_hash": "sha256:078aaaafcb1e5f29d4a8f3ec1936c49e5d239f554d1d28edbfdb389e2a7e66c7", "created_at": "2026-11-01T20:29:55Z", "updated_at": "2026-11-01T22:55:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the role of alternative lengthening of telomere-related genes in diagnostic modeling for non-alcoholic fatty liver disease.", "doi": "10.1038/s41598-024-81129-z", "pmid": "39638831", "authors": ["Nan Zhu", "Xiaoliang Wang", "Huiting Zhu", "Yue Zheng"], "journal": "Scientific reports", "year": 2024, "abstract": "Previous studies have reported an association between telomere length and non-alcoholic fatty liver disease (NAFLD). This study aimed to explore the involvement of alternative lengthening of telomere-related genes (ALTRGs) in the pathology of NAFLD, construct a risk signature, and evaluate both treatment and prognosis. Three NAFLD datasets (GSE48452, GSE89632, and GSE63067) were collected from the GEO database and merged into combined GEO datasets. ALTRGs were collected from GeneCards and PubMed databases. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. This study employed a support vector machine algorithm and least absolute shrinkage and selection operator regression analysis to identify key genes for constructing a diagnostic model. High- and low-risk groups were identified from the combined GEO datasets using the diagnostic model. Gene set enrichment analysis, regulatory network analysis, and intergroup immune infiltration analysis were performed. This study identified the key genes using receiver operating characteristic and Friends analysis. Expression of these genes was validated in a mouse model of NAFLD. Twenty-five genes were differentially expressed, with a positive correlation between FOS and EGR1 and a negative correlation between MYC and CEBPA. A diagnostic model was constructed using 12 genes, and high- and low-risk groups were identified. CAMK2G, ERBB2, FOSB, WT1, and CEBPA showed certain accuracy, and their expression levels were significantly different in the model. Immune infiltration analysis between the risk groups revealed that six immune cells were statistically significant. This includes a strong negative interaction between type 2 T helper cells and SPHK2 in the high-risk group. These findings suggest that ALTRDEGs are potential therapeutic targets and prognostic indicators for NAFLD. However, further investigations are required to elucidate the specific underlying mechanisms.", "claims": [{"id": "claim-method", "statement": "This study employed a support vector machine algorithm and least absolute shrinkage and selection operator regression analysis to identify key genes for constructing a diagnostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39638831", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This study employed a support vector machine algorithm and least absolute shrinkage and selection operator regression analysis to identify key genes for constructing a diagnostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analysed during the current study are available in the GEO repository(https://www.ncbi.nlm.nih.gov/geo/, accessed on 19 November 2023).Public data sources and R-related analyses in our study were conducted from November 2023.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39638831", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The datasets generated and analysed during the current study are available in the GEO repository(https://www.ncbi.nlm.nih.gov/geo/, accessed on 19 November 2023).Public data sources and R-related analyses in our study were conducted from November 2023."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A diagnostic model was constructed using 12 genes, and high- and low-risk groups were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39638831", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A diagnostic model was constructed using 12 genes, and high- and low-risk groups were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A diagnostic model was constructed using 12 genes, and high- and low-risk groups were identified.", "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:39638831"], "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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