Identification of shared gene signatures and molecular mechanisms between chronic kidney disease and ulcerative colitis

Zhang, Liang; Hu, Xinrong; Lin, Ruoni; Tang, Zhe; Ye, Ziyin; Mao, Ren; Chen, Wei; Zhou, Yi

doi:10.3389/fimmu.2023.1078310

Cited by 5 publications

(1 citation statement)

References 59 publications

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“…For other applications, several studies have utilized machine learning to predict the development of acute kidney injury following surgery and volume responsiveness in patients with oliguric acute kidney injury ( 49 – 51 ). Recently, shared gene signatures and molecular mechanisms have been demonstrated between these two diseases ( 52 , 53 ). This is relevant because shared genes are important for disease prevention and early treatment.…”

Section: Discussionmentioning

confidence: 99%

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Wang

Peng

et al. 2023

Front. Med.

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BackgroundAcute 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.MethodsFour 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.ResultsSeven 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.ConclusionOur 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.

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Section: Discussionmentioning

confidence: 99%

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Wang

Peng

et al. 2023

Front. Med.

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Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis

Xiao,

Cheng,

Chen

et al. 2023

Mol Biotechnol

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The shared mechanisms between pediatric acute lymphoblastic leukaemia (ALL) and pediatric sepsis are currently unclear. This study was aimed to explore the shared key genes of pediatric ALL and pediatric sepsis. The datasets involved were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control samples in GSE13904 and GSE79533 were intersected. The least absolute shrinkage and selection operator (LASSO) and the boruta analyses were performed in GSE13904 and GSE79533 separately based on shared DEGs, and shared key genes were obtained by taking the intersection of sepsis-related key genes and ALL-related key genes. Three shared key genes (HCK, NOG, RNF125) were obtained, that have a good diagnostic value for both sepsis and ALL. The correlation between shared key genes and differentially expressed immune cells was higher in GSE13904 and conversely, the correlation of which was lower in GSE79533. Suggesting that the sharing key genes had a different impact on the immune environment in pediatric ALL and pediatric sepsis. We make the case that this study provides a new perspective to study the relationship between pediatric ALL and pediatric sepsis.

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Neutrophil-percentage-to-albumin ratio is associated with chronic kidney disease: Evidence from NHANES 2009–2018

Li,

Xiang,

Chen

et al. 2024

PLoS ONE

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Introduction The neutrophil-percentage-to-albumin ratio (NPAR), a novel inflammatory biomarker, has been used to predict the prognosis of patients with cancer and cardiovascular disease. However, the relationship between NPAR and chronic kidney disease (CKD) remains unknown. The purpose of this study was to investigate the possible association between NPAR and CKD. Methods The cross-sectional study included participants with complete information on NPAR, serum creatinine (Scr), or urinary albumin-to-creatinine ratio (UACR) from the 2009–2018 National Health and Nutrition Examination Survey (NHANES). CKD was defined as the presence of either low estimated glomerular filtration rate (eGFR) or albuminuria. Univariate and multivariate logistic regression and restricted cubic spline regression were used to assess the linear and nonlinear associations between NPAR and renal function. Subgroup and interactive analyses were performed to explore potential interactive effects of covariates. Missing values were imputed using random forest. Results A total of 25,236 participants were enrolled in the study, of whom 4518 (17.9%) were diagnosed with CKD. After adjustment for covariates, the odds ratios (ORs) for prevalent CKD were 1.19 (95% CI = 1.07–1.31, p <0.05) for the Q2 group, 1.53 (95% CI = 1.39–1.69, p < 0.001) for the Q3 group, and 2.78 (95% CI = 2.53–3.05, p < 0.001) for the Q4 group. There was a significant interaction between age and diabetes mellitus on the association between NPAR and CKD (both p for interaction < 0.05). And there was a non-linear association between NPAR levels and CKD in the whole population (p for non-linear < 0.001). All sensitivity analyses supported the positive association between NPAR and CKD. Conclusions NPAR was positively correlated with increased risk of CKD. The NPAR may serve as an available and cost-effective tool for identifying and intervening the individuals at risk of CKD.

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Identification of shared gene signatures and molecular mechanisms between chronic kidney disease and ulcerative colitis

Cited by 5 publications

References 59 publications

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis

Neutrophil-percentage-to-albumin ratio is associated with chronic kidney disease: Evidence from NHANES 2009–2018

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