{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:33:49Z","timestamp":1759484029941,"version":"3.41.2"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ20H160021"],"award-info":[{"award-number":["LQ20H160021"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Laboratory Analysis"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>This study aimed to find ferroptosis\u2010related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis\u2010related genes were screened by univariate regression analysis, machine\u2010learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis\u2010related gene\u2010based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE19750\">GSE19750<\/jats:ext-link> cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival\u2010associated ferroptosis\u2010related genes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Seventy genes were identified as survival\u2010associated ferroptosis\u2010related genes. The prognostic model was constructed with 17 ferroptosis\u2010related genes including <jats:italic>STMN1<\/jats:italic>, <jats:italic>RRM2<\/jats:italic>, <jats:italic>HELLS<\/jats:italic>, <jats:italic>FANCD2<\/jats:italic>, <jats:italic>AURKA<\/jats:italic>, <jats:italic>GABARAPL2<\/jats:italic>, <jats:italic>SLC7A11<\/jats:italic>, <jats:italic>KRAS<\/jats:italic>, <jats:italic>ACSL4<\/jats:italic>, <jats:italic>MAPK3<\/jats:italic>, <jats:italic>HMGB1<\/jats:italic>, <jats:italic>CXCL2<\/jats:italic>, <jats:italic>ATG7<\/jats:italic>, <jats:italic>DDIT4<\/jats:italic>, <jats:italic>NOX1<\/jats:italic>, <jats:italic>PLIN4<\/jats:italic>, and <jats:italic>STEAP3<\/jats:italic>. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1\u2010, 3\u2010, and 5\u2010 year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram\u00a0prognostic evaluation model showed better predictive ability than conventional staging systems.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We constructed a prognosis model of ACC based on ferroptosis\u2010related genes with better predictive value than the conventional staging system. These efforts provided candidate\u00a0targets\u00a0for\u00a0revealing the\u00a0molecular\u00a0basis of ACC, as well as novel targets for drug development.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcla.24465","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T19:55:00Z","timestamp":1651521300000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Ferroptosis\u2010based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6783-215X","authenticated-orcid":false,"given":"Chen","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Breast Surgery The Second Affiliated Hospital Zhejiang University School of Medicine  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruofei","family":"Hu","sequence":"additional","affiliation":[{"name":"Lifestyle Supporting Technologies Group Technical University of Madrid  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"FangFang","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cancer Prevention and Intervention China National Ministry of Education The Second Affiliated Hospital Cancer Institute Zhejiang University School of Medicine  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology The Second Affiliated Hospital Zhejiang University School of Medicine  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/med.21398"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40291-018-0329-9"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2011.10.033"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01926230701320337"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2012.03.042"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2017.00992"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13045-019-0720-y"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1426-6"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.7314\/APJCP.2015.16.6.2129"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-019-0149-1"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2019.04.002"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.201704007"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1210\/er.2013-1029"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11912-019-0773-7"},{"issue":"2","key":"e_1_2_9_16_1","first-page":"109","article-title":"Advanced adrenocortical carcinoma \u2010 What to do when first\u2010line therapy fails?","volume":"127","author":"Megerle F","year":"2019","journal-title":"Exp Clin Endocrinol Diabetes"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.path.2019.08.010"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41574-019-0221-7"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1530\/JME-18-0122"},{"issue":"1","key":"e_1_2_9_20_1","first-page":"58","article-title":"Clinical and molecular prognostic factors in adrenocortical carcinoma","volume":"44","author":"Lib\u00e9 R","year":"2019","journal-title":"Minerva Endocrinol"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.2217\/fon.09.45"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baaa021"},{"volume-title":"Machine Learning with R","year":"2017","author":"Mastering CL","key":"e_1_2_9_23_1"},{"key":"e_1_2_9_24_1","unstructured":"PatrickJ.Heagerty PS\u2010C. 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