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Zhai X, Lou H, Hu J. Five-gene signature predicts acute kidney injury in early kidney transplant patients. Aging (Albany NY) 2022; 14:2628-2644. [PMID: 35320116 PMCID: PMC9004575 DOI: 10.18632/aging.203962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 09/18/2021] [Indexed: 12/04/2022]
Abstract
Patients with acute kidney injury (AKI) show high morbidity and mortality, and a lack of effective biomarkers increases difficulty in its early detection. Weighted gene co-expression network analysis (WGCNA) detected a total of 22 gene modules and 6 miRNA modules, of which 4 gene modules and 3 miRNA modules were phenotypically co-related. Functional analysis revealed that these modules were related to different molecular pathways, which mainly involved PI3K-Akt signaling pathway and ECM-receptor interaction. The brown modules related to transplantation mainly involved immune-related pathways. Finally, five genes with the highest AUC were used to establish a diagnosis and prediction model of AKI. The model showed a high area under curve (AUC) in the training set and validation set, and their prediction accuracy for AKI was as high as 100%. Similarly, the prediction accuracy of AKI after 24 h in the 0 h transplant sample was 100%. This study may provide new features for the diagnosis and prediction of AKI after kidney transplantation, and facilitate the diagnosis and drug development of AKI in kidney transplant patients.
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Affiliation(s)
- Xia Zhai
- Medical Molecular Biology Laboratory, School of Medicine, Jinhua Polytechnic, Jinhua 321000, China
| | - Hongqiang Lou
- Medical Molecular Biology Laboratory, School of Medicine, Jinhua Polytechnic, Jinhua 321000, China
| | - Jing Hu
- Medical Molecular Biology Laboratory, School of Medicine, Jinhua Polytechnic, Jinhua 321000, China
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Sha K, Lu Y, Zhang P, Pei R, Shi X, Fan Z, Chen L. Identifying a novel 5-gene signature predicting clinical outcomes in acute myeloid leukemia. Clin Transl Oncol 2020; 23:648-656. [PMID: 32776271 DOI: 10.1007/s12094-020-02460-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 07/11/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Acute myeloid leukemia (AML) is the most common type of acute leukemia and biologically heterogeneous diseases with poor prognosis. Thus, we aimed to identify prognostic markers to effectively predict the prognosis of AML patients and eventually guide treatment. METHODS Prognosis-associated genes were determined by Kaplan-Meier and multivariate analyses using the expression and clinical data of 173 AML patients from The Cancer Genome Atlas database and validated in an independent Oregon Health and Science University dataset. A prognostic risk score was computed based on a linear combination of 5-gene expression levels using the regression coefficients derived from the multivariate logistic regression model. The classification of AML was established by unsupervised hierarchical clustering of CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 expression levels. RESULTS High FCHO2 and LRCH4 expression was related to decreased mortality. While high CALCRL, DOCK1, PLA2G4A expression was associated with increased mortality. The risk score was predictive of increased mortality rate in AML patients. Hierarchical clustering analysis of the five genes discovered three clusters of AML patients. The cluster1 AML patients were associated with lower cytogenetics risk than cluster2 or 3 patients, and better prognosis than cluster3 patients (P values < 0.05 for all cases, fisher exact test or log-rank test). CONCLUSION The gene panel comprising CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 as well as the risk score may offer novel prognostic biomarkers and classification of AML patients to significantly improve outcome prediction.
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Affiliation(s)
- K Sha
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China.
| | - Y Lu
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
| | - P Zhang
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
| | - R Pei
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
| | - X Shi
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
| | - Z Fan
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
| | - L Chen
- Department of Hematology, The Affiliated People's Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China
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