Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32802843", "kind": "science-record", "revision": 8, "content_hash": "sha256:5b34b82404cd2a519fb997dcae3b288a1cf9e1a5174a55c29b9539b83e723392", "created_at": "2026-11-01T15:41:08Z", "updated_at": "2026-11-01T22:45:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma.", "doi": "10.1155/2020/3708231", "pmid": "32802843", "authors": ["Jingwei Zhao", "Le Wang", "Bo Wei"], "journal": "BioMed research international", "year": 2020, "abstract": "Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR = 3.192, 95%CI = 2.182-4.670; CGGA: HR = 1.922, 95%CI = 1.431-2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.", "claims": [{"id": "claim-method", "statement": "The Linear Models for Microarray Data (LIMMA) method (version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [15] in the Bioconductor R package (version 3.4.1; http://www.R-project.org/) was used to identify differentially expressed genes (DEGs) and lncRNAs (DELs) between LGG and normal controls.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32802843", "locator": "sections[4] \"2.2. Identification of Energy Metabolism-Related Genes\" paragraph 3", "quote": "The Linear Models for Microarray Data (LIMMA) method (version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [15] in the Bioconductor R package (version 3.4.1; http://www.R-project.org/) was used to identify differentially expressed genes (DEGs) and lncRNAs (DELs) between LGG and normal controls."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data were collected from GEO (GSE4290; http://www.ncbi.nlm.nih.gov/geo/), TCGA (https://gdc-portal.nci.nih.gov/), and CGGA (http://www.cgga.org.cn) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32802843", "locator": "sections[14] \"Data Availability\" paragraph 2", "quote": "All data were collected from GEO (GSE4290; http://www.ncbi.nlm.nih.gov/geo/), TCGA (https://gdc-portal.nci.nih.gov/), and CGGA (http://www.cgga.org.cn) databases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32802843"], "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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Development of Energy Metabolism-Related DER-Based Risk Score", "row_count": 11, "column_count": 11}, "table": {"label": "Table 2", "caption": "The optimal signature combination.", "rows": [["Symbol", "Type", "Expression (LGG vs. CK)", "Expression (GBM vs. LGG)", "Multivariate Cox regression analysis", "LASSO coefficient"], ["logFC", "FDR (GSE4290)", "p value (GSE4290)", "p value (CGGA)", "p value (TCGA)", "HR", "95% CI", "p value"], ["GABPB1-AS1", "lncRNA", "1.07", "2.72e − 07", "7.59e − 03", "6.72e − 06", "8.83e − 289", "0.9946", "0.9899-0.9994", "2.846e − 02", "-0.05468"], ["HAR1A", "lncRNA", "-1.51", "7.65e − 04", "4.31e − 03", "1.65e − 15", "1.62e − 102", "0.9938", "0.988-0.9996", "3.580e − 02", "-0.64387"], ["LINC00599", "lncRNA", "-1.3", "4.80e − 05", "3.64e − 02", "2.36e − 06", "2.62e − 105", "1.0035", "1.0001-1.007", "4.628e − 02", "0.00904"], ["SNAI3-AS1", "lncRNA", "-1.76", "9.51e − 08", "7.96e − 03", "3.12e − 10", "1.71e − 258", "0.9817", "0.9701-0.9934", "2.310e − 03", "-1.81399"], ["SNHG1", "lncRNA", "1.3", "6.06e − 09", "1.28e − 01", "1.28e − 04", "8.19e − 222", "1.0054", "1.0004-1.0104", "3.328e − 02", "0.29846"], ["FABP6", "mRNA", "-2.18", "1.13e − 07", "2.94e − 01", "4.34e − 02", "1.42e − 128", "1.0062", "1.004-1.0085", "6.270e − 08", "0.55872"], ["MBOAT7", "mRNA", "-1.27", "3.92e − 09", "8.01e − 01", "1.11e − 03", "0", "1.0084", "1.0017-1.0152", "1.463e − 02", "0.77305"], ["SLC25A1", "mRNA", "1.03", "2.39e − 09", "4.05e − 03", "5.96e − 12", "0", "0.9955", "0.9911-0.9999", "4.934e − 02", "-0.40797"], ["UST", "mRNA", "1.47", "5.50e − 10", "5.80e − 05", "7.57e − 08", "2.41e − 256", "0.9951", "0.9914-0.9989", "1.177e − 02", "-0.55501"]], "row_count": 11, "column_count": 11, "numeric_cells": 86, "header_rows": 2, "locator": "section[10]/table-wrap[1]#tab2"}}, {"id": "asset:table:pmid:32802843:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:b293a1107da2f526985f027cc024de83ca2695075467fa9cd4d1d84da16f1653", "bytes": 2009, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[2]#tab3", "version": null, "note": "Univariate and multivariate Cox regression analyses of clinical pathologic features for overall survival.", "structure": {"pmcid": "PMC7403901", "section_index": 10, "section_title": "3.2. Development of Energy Metabolism-Related DER-Based Risk Score", "row_count": 17, "column_count": 8}, "table": {"label": "Table 3", "caption": "Univariate and multivariate Cox regression analyses of clinical pathologic features for overall survival.", "rows": [["Variables", "TCGA (N = 520)", "Univariate analysis", "Multivariate analysis"], ["HR", "95% CI", "p value", "HR", "95% CI", "p value"], ["Age (years, mean ± SD)", "42.84 ± 13.39", "1.057", "1.043-1.072", "2.78e − 15", "1.049", "1.003-1.096", "3.59e − 02"], ["Gender (male/female)", "286/234", "1.145", "0.811-1.617", "4.40e − 01", "—", "—", "—"], ["Animal insect allergy history (yes/no/-)", "16/289/215", "0.831", "0.202-3.428", "7.92e − 01", "—", "—", "—"], ["Asthma history (yes/no/-)", "22/346/152", "0.894", "0.409-1.956", "7.76e − 01", "—", "—", "—"], ["Food allergy history (yes/no/-)", "20/290/210", "1.008", "0.363-2.801", "9.87e − 01", "—", "—", "—"], ["Hay fever history (yes/no/-)", "38/304/178", "0.488", "0.177-1.347", "1.24e − 01", "—", "—", "—"], ["Headache history (yes/no/-)", "174/297/49", "0.841", "0.576-1.227", "3.64e − 01", "—", "—", "—"], ["Histological type (astrocytoma/oligoastrocytoma/oligodendroglioma)", "194/132/194", "0.756", "0.620-0.921", "5.25e − 03", "0.529", "0.244-1.149", "1.08e − 01"], ["IDH1 mutation (yes/no/-)", "91/34/395", "0.181", "0.0675-0.484", "4.14e − 04", "0.226", "0.0729-0.701", "9.98e − 03"], ["Neoplasm histologic grade (G2/G3/-)", "254/265/1", "3.416", "2.341-4.985", "1.84e − 11", "0.949", "0.294-3.063", "9.30e − 01"], ["Radiation therapy (yes/no/-)", "294/181/45", "1.847", "1.209-2.820", "2.83e − 03", "1.981", "0.584-6.722", "2.73e − 01"], ["Targeted molecular therapy", "268/200/52", "1.389", "0.955-2.019", "8.08e − 02", "—", "—", "—"], ["Risk score (high/low)", "260/260", "3.192", "2.182 -4.670", "1.99e − 10", "5.041", "1.272-19.97", "2.13e − 02"], ["Death (dead/alive)", "133/387", "—", "—", "—", "—", "—", "—"], ["Overall survival days (months, mean ± SD)", "32.97 ± 32.78", "—", "—", "—", "—", "—", "—"]], "row_count": 17, "column_count": 8, "numeric_cells": 75, "header_rows": 2, "locator": "section[10]/table-wrap[2]#tab3"}}, {"id": "asset:table:pmid:32802843:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:10a1b2ff7a123ece51f3268165d8acf3a1a3277dd0b6cb767dac31716774aa2c", "bytes": 688, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[3]#tab4", "version": null, "note": "The performance of the nomogram assessed by discrimination parameters.", "structure": {"pmcid": "PMC7403901", "section_index": 10, "section_title": "3.2. 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