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

Systemic Inflammatory Response Index (SIRI) is associated with all-cause mortality and cardiovascular mortality in population with chronic kidney disease: evidence from NHANES (2001-2018)

Objective

To examine the correlation between SIRI and the probability of cardiovascular mortality as well as all-cause mortality in individuals with chronic kidney disease.

Methods

A cohort of 3,262 participants from the US National Health and Nutrition Examination Survey (NHANES) database were included in the study. We categorized participants into five groups based on the stage of chronic kidney disease. A weighted Cox regression model was applied to assess the relationship between SIRI and mortality. Subgroup analyses, Kaplan-Meier survival curves, and ROC curves were conducted. Additionally, restricted cubic spline analysis was employed to elucidate the detailed association between SIRI and hazard ratio (HR).

Results

This study included a cohort of 3,262 individuals, of whom 1,535 were male (weighted proportion: 42%), and 2,216 were aged 60 or above (weighted proportion: 59%). Following adjustments for covariates like age, sex, race, and education, elevated SIRI remained a significant independent risk factor for cardiovascular mortality (HR=2.50, 95%CI: 1.62-3.84, p<0.001) and all-cause mortality (HR=3.02, 95%CI: 2.03-4.51, p<0.001) in CKD patients. The restricted cubic spline analysis indicated a nonlinear relationship between SIRI and cardiovascular mortality, with SIRI>1.2 identified as an independent risk factor for cardiovascular mortality in CKD patients.

Conclusion

Heightened SIRI independently poses a risk for both all-cause and cardiovascular mortality in chronic kidney disease patients, with potentially heightened significance in the early stages (Stage I to Stage III) of chronic kidney disease.

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

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.

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

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, CD56^dim 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.