RESUMO
Background: Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive. Methods: Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers. Results: Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability. Conclusion: Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.
RESUMO
BACKGROUND: Peritoneal dialysis (PD) patients are at high risk of developing glucose metabolism disturbance (GMD). The incidence and prevalence of new-onset GMD, including diabetes mellitus (DM), impaired glucose tolerance (IGT) and impaired fast glucose (IFG), after initiation of PD, as well as their correlated influence factors, varies among studies in different areas and of different sample sizes. Also, the difference compared with hemodialysis (HD) remained unclear. Thus we designed this meta-analysis and systematic review to provide a full landscape of the occurrence of glucose disorders in PD patients. METHODS: We searched the MEDLINE, Embase, Web of Science and Cochrane Library databases for relevant studies through September 2018. Meta-analysis was performed on outcomes using random effects models with subgroup analysis and sensitivity analysis. RESULTS: We identified 1124 records and included 9 studies involving 13 879 PD patients. The pooled incidence of new-onset DM (NODM) was 8% [95% confidence interval (CI) 4-12; I2 = 98%] adjusted by sample sizes in PD patients. Pooled incidence rates of new-onset IGT and IFG were 15% (95% CI 3-31; I2 = 97%) and 32% (95% CI 27-37), respectively. There was no significant difference in NODM risk between PD and HD [risk ratio 0.99 (95% CI 0.69-1.40); P = 0.94; I2 = 92%]. PD patients with NODM were associated with an increased risk of mortality [hazard ratio 1.06 (95% CI 1.01-1.44); P < 0.001; I2 = 92.5%] compared with non-DM PD patients. CONCLUSIONS: Around half of PD patients may develop a glucose disorder, which can affect the prognosis by significantly increasing mortality. The incidence did not differ among different ethnicities or between PD and HD. The risk factor analysis did not draw a definitive conclusion. The glucose tolerance test should be routinely performed in PD patients.
