Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37700319", "kind": "science-record", "revision": 3, "content_hash": "sha256:c04ff9be33e30467f7a267c5b94f5f130fc02c183dd493f19ef5e8b6c6cfafa4", "created_at": "2026-11-01T19:02:30Z", "updated_at": "2026-11-01T22:10:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data.", "doi": "10.1186/s12967-023-04388-w", "pmid": "37700319", "authors": ["Xiaonan Zhang", "Qiannan Ren", "Zhiyong Li", "Xiaolin Xia", "Wan Zhang", "Yue Qin", "Dehua Wu", "Chen Ren"], "journal": "Journal of translational medicine", "year": 2023, "abstract": "BACKGROUND: Gene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.\nMETHODS: Radio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The \"DESeq2\" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.\nRESULTS: Based on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.\nCONCLUSION: Our study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.", "claims": [{"id": "claim-method", "statement": "Then, Lasso COX regression was performed to determine the contributions of radiosensitivity-related genes in survival prediction using the R package “glmnet”, and nine hub genes were used to construct the prognostic risk models [24].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700319", "locator": "sections[8] \"Establishment of radiosensitivity-related prognostic risk signature (RRPRS)\" paragraph 2", "quote": "Then, Lasso COX regression was performed to determine the contributions of radiosensitivity-related genes in survival prediction using the R package “glmnet”, and nine hub genes were used to construct the prognostic risk models [24]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data could be downloaded at (https://portal.gdc.cancer.gov/, https://xenabrowser.net/) and the code used during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700319", "locator": "sections[28] \"Availability of data and materials\" paragraph 2", "quote": "The data could be downloaded at (https://portal.gdc.cancer.gov/, https://xenabrowser.net/) and the code used during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The authors established a nine-gene radiosensitivity-related prognostic risk signature using LASSO Cox regression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37700319", "locator": "sections[15] \"Establishment of a radiosensitivity-related prognostic risk signature (RRPRS)\" paragraph 2", "quote": "Lastly, we performed a LASSO Cox regression analysis of the hub genes, which further narrowed the number of these radiosensitivity-related genes to nine, and established an RRPRS (Fig. 2e, f)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The authors established a nine-gene radiosensitivity-related prognostic risk signature using LASSO Cox regression.", "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:37700319"], "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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