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Yan D, Wang Y, Hu J, Lu R, Ye C, Liu N, Chen D, Liang W, Zheng L, Liu W, Lan T, Lan N, Shao Q, Zhuang S, Ma X, Liu N. External validation of a novel nomogram for diagnosis of Protein Energy Wasting in adult hemodialysis patients. Front Nutr 2024; 11:1351503. [PMID: 39193561 PMCID: PMC11347328 DOI: 10.3389/fnut.2024.1351503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 07/31/2024] [Indexed: 08/29/2024] Open
Abstract
Background Protein Energy Wasting (PEW) has high incidence in adult hemodialysis patients and refers to a state of decreased protein and energy substance. It has been demonstrated that PEW highly affects the quality of survival and increases the risk of death. Nevertheless, its diagnostic criteria are complex in clinic. To simplify the diagnosis method of PEW in adult hemodialysis patients, we previously established a novel clinical prediction model that was well-validated internally using bootstrapping. In this multicenter cross-sectional study, we aimed to externally validate this nomogram in a new cohort of adult hemodialysis patients. Methods The novel prediction model was built by combining four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and body mass index (BMI). We evaluated the performance of the new model using discrimination (Concordance Index), calibration plots, and Clinical Impact Curve to assess its predictive utility. Results From September 1st, 2022 to August 31st, 2023, 1,158 patients were screened in five medical centers in Shanghai. 622 (53.7%) hemodialysis patients were included for analysis. The PEW predictive model was acceptable discrimination with the area under the curve of 0.777 (95% CI 0.741-0.814). Additionally, the model revealed well-fitted calibration curves. The McNemar test showed the novel model had similar diagnostic efficacy with the gold standard diagnostic method (p > 0.05). Conclusion Our results from this cross-sectional external validation study further demonstrate that the novel model is a valid tool to identify PEW in adult hemodialysis patients effectively.
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Affiliation(s)
- Danying Yan
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Wang
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jing Hu
- Department of Nephrology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Renhua Lu
- Department of Nephrology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Chaoyang Ye
- Department of Nephrology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Nanmei Liu
- International Medicine III (Nephrology & Endocrinology), Naval Medical Center of People's Liberation Army of China, Naval Medical University, Shanghai, China
| | - Dongping Chen
- Department of Nephrology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weiwei Liang
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Liang Zheng
- Key Laboratory of Arrhythmias of the Ministry of Education of China, Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wenrui Liu
- Department of Nephrology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianying Lan
- Department of Nephrology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Naiying Lan
- International Medicine III (Nephrology & Endocrinology), Naval Medical Center of People's Liberation Army of China, Naval Medical University, Shanghai, China
| | - Qing Shao
- International Medicine III (Nephrology & Endocrinology), Naval Medical Center of People's Liberation Army of China, Naval Medical University, Shanghai, China
| | - Shougang Zhuang
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Medicine, Rhode Island Hospital and Alpert Medical School, Brown University, Providence, RI, United States
| | - Xiaoyan Ma
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Na Liu
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
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Li Y, Su H, Liu K, Zhao Z, Wang Y, Chen B, Xia J, Yuan H, Huang DS, Gu Y. Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature. World J Surg Oncol 2024; 22:49. [PMID: 38331878 PMCID: PMC10854045 DOI: 10.1186/s12957-024-03314-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 01/13/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level. METHODS Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA). RESULTS A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion. CONCLUSIONS Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.
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Affiliation(s)
- Yawei Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Hang Su
- School of Clinical Medicine, Guizhou Medical University, Guiyang, Guizhou, China
| | - Kaidong Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhangxiang Zhao
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuquan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Bo Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jie Xia
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Huating Yuan
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - De-Shuang Huang
- Bioinformatics and BioMedical Bigdata Mining Laboratory, School of Big Health, Guizhou Medical University, Guiyang, Guizhou, China.
| | - Yunyan Gu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
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