A novel peroxisome-related gene signature predicts clinical prognosis and is associated with immune microenvironment in low-grade glioma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38406287", "kind": "science-record", "revision": 4, "content_hash": "sha256:134f0d7ec3b9ba826deee96511261124bdfe0e1cbddade37addca2621f416dac", "created_at": "2026-11-01T19:20:21Z", "updated_at": "2026-11-01T22:11:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel peroxisome-related gene signature predicts clinical prognosis and is associated with immune microenvironment in low-grade glioma.", "doi": "10.7717/peerj.16874", "pmid": "38406287", "authors": ["Dandan Gao", "Qiangyi Zhou", "Dianqi Hou", "Xiaoqing Zhang", "Yiqin Ge", "Qingwei Zhu", "Jian Yin", "Xiangqian Qi", "Yaohua Liu", "Meiqing Lou", "Li Zhou", "Yunke Bi"], "journal": "PeerJ", "year": 2024, "abstract": "Low-grade glioma (LGG), a common primary tumor, mainly originates from astrocytes and oligodendrocytes. Increasing evidence has shown that peroxisomes function in the regulation of tumorigenesis and development of cancer. However, the prognostic value of peroxisome-related genes (PRGs) in LGG has not been reported. Therefore, it is necessary to construct a prognostic risk model for LGG patients based on the expression profiles of peroxisome-related genes. Our study mainly concentrated on developing a peroxisome-related gene signature for overall survival (OS) prediction in LGG patients. First, according to these peroxisome-related genes, all LGG patients from The Cancer Genome Atlas (TCGA) database could be divided into two subtypes. Univariate Cox regression analysis was used to find prognostic peroxisome-related genes in TCGA_LGG dataset, and least absolute shrinkage and selection operator Cox regression analysis was employed to establish a 14-gene signature. The risk score based on the signature was positively associated with unfavorable prognosis. Then, multivariate Cox regression incorporating additional clinical characteristics showed that the 14-gene signature was an independent predictor of LGG. Time-dependent ROC curves revealed good performance of this prognostic signature in LGG patients. The performance about predicting OS of LGG was validated using the GSE107850 dataset derived from the Gene Expression Omnibus (GEO) database. Furethermore, we constructed a nomogram model based on the gene signature and age, which showed a better prognostic power. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that neuroactive ligand-receptor interaction and phagosome were enriched and that the immune status was decreased in the high-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U251 and A172 cells. Inhibition of ATAD1 (ATPase family AAA domain-containing 1) and ACBD5 (Acyl-CoA binding-domain-containing-5) expression led to significant inhibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could induce apoptosis of U251 and A172 cells. Therefore, through bioinformatics methods and cell experiments, our study developed a new peroxisome-related gene signature that migh t help improve personalized OS prediction in LGG patients.", "claims": [{"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nThe data is available at TCGA-LGG and NCBI GEO: GSE107850. The raw measurements are available in the Supplementary File.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38406287", "locator": "sections[42] \"Data Availability Statement\" paragraphs 1-3", "quote": "The following information was supplied regarding data availability:\nThe data is available at TCGA-LGG and NCBI GEO: GSE107850. The raw measurements are available in the Supplementary File."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a 14-gene peroxisome-related signature for overall-survival prediction in LGG using univariate Cox and LASSO Cox regression, with TCGA_LGG as the training cohort and GSE107850 as the independent validation cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38406287", "locator": "sections[6] \"Construction and evaluation of peroxisome-related gene signature\" paragraph 3", "quote": "LASSO (least absolute shrinkage and selection operator) penalized Cox regression was used to establish an optimal risk signature from survival-related genes using the “glmnet” R package."}, {"asset": "source:pmid:38406287", "locator": "sections[20] \"Construction of a prognostic gene signature based on the LGG_TCGA cohort\" paragraph 5", "quote": "AUC (Area under the curve) was found to be 0.825 for 1-year, 0.831 for 3- year, and 0.752 for 5-year survival (Fig. 3F)."}, {"asset": "source:pmid:38406287", "locator": "sections[21] \"Validation of the risk gene signature on an independent dataset\" paragraph 2", "quote": "A dataset of 195 LGG patients derived from a GEO cohort (GSE107850) was used as the validation dataset (Table 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a 14-gene peroxisome-related signature for overall-survival prediction in LGG using univariate Cox and LASSO Cox regression, with TCGA_LGG as the training cohort and GSE107850 as the independent validation cohort.", "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:38406287"], "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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Therefore, through b", "gh-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U"], "order_basis": "paper_text_order:1932", "id": "assay:1"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could"], "order_basis": "paper_text_order:2188", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["on in the TCGA_LGG dataset. 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