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Di H, Zhao J, Zhu X, Zhou X, Hu Y, Wang M, Qiu Z, Zhang W, Chen X. A novel prognostic signature for lung adenocarcinoma based on cuproptosis-related lncRNAs: A Review. Medicine (Baltimore) 2022; 101:e31924. [PMID: 36626411 PMCID: PMC9750635 DOI: 10.1097/md.0000000000031924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is a highly heterogeneous disease with complex pathogenesis, high mortality, and poor prognosis. Cuproptosis is a new type of programmed cell death triggered by copper accumulation that may play an important role in cancer. LncRNAs are becoming valuable prognostic factors in cancer patients. The effect of cuproptosis-related lncRNAs (CRlncRNAs) on LUAD has not been clarified. Based on the Cancer Genome Atlas database, CRlncRNAs were screened by co-expression analysis of cuproptosis- related genes and lncRNAs. Using CRlncRNAs, Cox and LASSO regression analyses constructed a risk prognostic model. The predictive efficacy of the model was assessed and validated using survival analysis, receiver operating characteristic curve, univariate and multifactor Cox regression analysis, and principal component analysis. A nomogram was constructed and calibration curves were applied to enhance the predictive efficacy of the model. Tumor Mutational Burden analysis and chemotherapeutic drug sensitivity prediction were performed to assess the clinical feasibility of the risk model. The novel prognostic signature consisted of 5 potentially high-risk CRlncRNAs, MAP3K20-AS1, CRIM1-DT, AC006213.3, AC008035.1, and NR2F2-AS1, and 5 potentially protective CRlncRNAs, AC090948.1, AL356481.1, AC011477.2, AL031600.2, and AC026355.2, which had accurate and robust predictive power for LUAD patients. Collectively, the novel prognostic signature constructed based on CRlncRNAs can effectively assess and predict the prognosis of patients and provide a new perspective for the diagnosis and treatment of LUAD.
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Affiliation(s)
- Huang Di
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jiting Zhao
- Department of Gastroenterology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xue Zhu
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xinpeng Zhou
- Department of Rheumatology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuanlong Hu
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mengjie Wang
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhanjun Qiu
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wei Zhang
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianhai Chen
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- * Correspondence: Xianhai Chen, Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan, China (e-mail: )
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Ruli E, Ventura L. Accurate likelihood inference for the volume under the ROC surface. Cancer Rep (Hoboken) 2020; 3:e1206. [PMID: 32794638 PMCID: PMC7941487 DOI: 10.1002/cnr2.1206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/09/2019] [Accepted: 06/10/2019] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND With three ordered diagnostic categories, the volume under the receiver operating characteristic (ROC) surface, which is the extension of the area under the ROC curve for binary diagnostic outcomes, is the most commonly used measure for the overall diagnostic accuracy. For a continuous-scale diagnostic test, classical likelihood-based inference about the area under the ROC curve can be inaccurate, in particular when the sample size is small, and higher order inferential procedures have been proposed. AIM The goal of this paper is to illustrate higher order likelihood procedures for parametric inference in small samples, which provide accurate point estimates and confidence intervals for the volume under the ROC surface. METHODS Simulation studies are performed in order to illustrate the accuracy of the proposed methodology, and two applications to real data are discussed. RESULTS We show that likelihood modern inference provide refinements to classical inferential results. Furthermore, the freely available R package likelihoodAsy makes now their use almost automatic. CONCLUSION Modern likelihood inference based on higher-order asymptotic methods for the area under the ROC surface provide refinements to classical inferential results. A possible limitation of higher-order asymptotic methods for practical use is that their software implementation can be awkward. Nevertheless, use of the freely available R package likelihoodAsy makes such implementation straightforward.
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Affiliation(s)
- Erlis Ruli
- Department of Statistical SciencesUniversity of PaduaPaduaItaly
| | - Laura Ventura
- Department of Statistical SciencesUniversity of PaduaPaduaItaly
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