Identification of prognostic metabolic genes in adrenocortical carcinoma and establishment of a prognostic nomogram: A bioinformatic study.

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Therefore, it is necessary to establish an effective method to diagnose and evaluate the prognosis of patients, so as to realize individualized treatment and improve their survival rate.This study investigated metabolic genes that may be potential therapeutic targets for Adrenocortical carcinoma (ACC). Level 3 gene expression data from the ACC cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. To verify, other ACC datasets (GSE76021, GSE19750) were downloaded from the Gene Expression Omnibus (GEO) database. The ACC datasets from TCGA and GEO were used to screen metabolic genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping metabolic genes of the 2 datasets were identified.A signature of five metabolic genes (CYP11B1, GSTM2, IRF9, RPL31, and UBE2C) was identified in patients with ACC. The signature could be used to divide the patients with ACC into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of ACC. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model.The results indicated that the signature of 5 metabolic genes had excellent predictive value for ACC. These findings might help improve personalized treatment and medical decisions.", "claims": [{"id": "claim-method", "statement": "Metabolic prognostic genes were further evaluated with univariate Cox proportional hazard regression by using a “survival” package (version 3.2–3) on the R platform (version 3.6.3).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34918636", "locator": "sections[4] \"Metabolic gene screening\" paragraph 2", "quote": "Metabolic prognostic genes were further evaluated with univariate Cox proportional hazard regression by using a “survival” package (version 3.2–3) on the R platform (version 3.6.3)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are publicly available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34918636", "locator": "sections[17] \"Footnotes\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are publicly available."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our results showed that the prognostic signature from the database from TCGA in the current study performed well in predicting 1-, 3-, and 5-year survival rates.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34918636", "locator": "sections[11] \"Prognostic model construction and receiver operating characteristic curve analysis\" paragraph 2", "quote": "Our results showed that the prognostic signature from the database from TCGA in the current study performed well in predicting 1-, 3-, and 5-year survival rates."}, {"asset": "source:pmid:34918636", "locator": "sections[11] \"Prognostic model construction and receiver operating characteristic curve analysis\" paragraph 2", "quote": "The area under the curve values for 1-, 3-, and 5-year survival were 0.865, 0.929, and 0.867, respectively (Fig. 3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our results showed that the prognostic signature from the database from TCGA in the current study performed well in predicting 1-, 3-, and 5-year survival rates.", "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:34918636"], "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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