ABSTRACT
BACKGROUND: Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions. METHODS: A total of 266 patients with histopathology-proven IMN were included in this study. Least absolute shrinkage and selection operator regression was utilized to identify the most important variables. Subsequently, multivariate Cox regression analysis was conducted to construct a nomogram, and bootstrap resampling was employed for internal validation. Receiver operating characteristic and calibration curves and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the developed model. RESULTS: A prognostic nomogram was established, which incorporated creatinine, glomerular_basement_membrane_thickening, gender, IgG_deposition, low-density lipoprotein cholesterol, and fibrinogen. The areas under the curves of the 3-, 12-, 24-month were 0.751, 0.725, and 0.830 in the training set, and 0.729, 0.730, and 0.948 in the validation set respectively. These results and calibration curves demonstrated the good discrimination and calibration of the nomogram in the training and validation sets. Additionally, DCA indicated that the nomogram was useful for remission prediction in clinical settings. CONCLUSION: The nomogram was useful for clinicians to evaluate the prognosis of patients with IMN in early stage.
Subject(s)
Glomerulonephritis, Membranous , Humans , Nomograms , Kidney Glomerulus , Machine Learning , ProbabilityABSTRACT
BACKGROUND: Lupus nephritis (LN) is a major organ complication and cause of morbidity and mortality in patients with systemic lupus erythematosus. This study aims to provide the clinician with a quantitative tool for the prediction of the individual remission probability of LN and obtain new insights for improved clinical management in LN treatment. METHODS: A total of 301 patients with renal biopsy-proven LN were recruited and randomly divided into model construction and validation group. The least absolute shrinkage and selection operator regression analysis was conducted to select significant variables, and a multivariate Cox regression predictive model was established. The performance of the model was verified and tested with 1000-bootstrap validation in the validation group. Finally, the nomogram was constructed, and the performance was evaluated. The predictive accuracy and efficiency were verified through receiver operation characteristic and calibration curves. RESULTS: A total of 210 and 91 patients who all received renal biopsy were included in the training and validation group, respectively. A final prognostic model was established, which included the course of LN, gender, 24h-proteinuria, creatinine, triglycerides, FIB, Complement C3, anti-dsDNA antibody, tubular atrophy and classification of kidney biopsy. Moreover, an easy-to-use nomogram was built based on the predictive model. The areas under the curve (AUC) of the 1, 2, 5-year prediction were 77.12, 77.98 and 87.01 in the training group, respectively. In the validation group, the AUC of the 1, 2, 5-year prediction were 81.42, 87.20 and 92.81 respectively, which indicated good performance in predicting the remission probability of LN. CONCLUSION: This novel model was constructed to predict the remission probability of patients with LN for the first time. This model displayed good predictive performance and was easy to use for clinical practice.
Subject(s)
Lupus Erythematosus, Systemic , Lupus Nephritis , Antibodies, Antinuclear , Humans , Lupus Erythematosus, Systemic/complications , Lupus Nephritis/pathology , Proportional Hazards Models , Retrospective StudiesABSTRACT
PURPOSE: Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data. METHODS: Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed. Least absolute shrinkage and selection operator regression analysis were conducted to establish a multivariate logistic regression model. A nomogram based on the multivariate logistic regression model was constructed for easy application in clinical practice. Concordance index, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram. RESULTS: The predictive factors contained in the multivariate model included duration, gender, respiratory infection, arthritis, edema, estimated glomerular filtration rate, 24 h urine protein, uric acid, and renal ultrasound intensity. The area under the curves (AUC) of the nomogram in the training set and testing set were 0.814 and 0.822, respectively, indicating its good predictive ability. Moreover, the DCA curve and CIC revealed its clinical utility. CONCLUSION: The developed multivariate predictive model combines the clinical and laboratory factors of patients with IgAVN and is useful in the individualized prediction of the 1-year remission probability aid for clinical decision-making during treatment and management of IgAVN.
Subject(s)
IgA Vasculitis/diagnosis , Immunoglobulin A/metabolism , Nephritis/diagnosis , Clinical Decision-Making , Female , Humans , Male , Middle Aged , Models, Statistical , Nomograms , Precision Medicine , Prognosis , Risk FactorsABSTRACT
BACKGROUND: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST. METHODS: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. RESULTS: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively. CONCLUSIONS: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.
Subject(s)
Crohn Disease , Ustekinumab , Crohn Disease/drug therapy , Crohn Disease/genetics , Gene Expression , Humans , Machine Learning , Ustekinumab/therapeutic useABSTRACT
Acute kidney injury (AKI) is a common serious syndrome characterized by rapid decrease of glomerular filtration rate and the progressive increase of serum creatinine. Circular RNAs (circRNAs) are regulatory RNAs that recently became popular among various diseases. However, the expression profile and function of circRNAs in AKI remain largely unknown. The main function of circRNAs is acting as competing endogenous RNAs (ceRNAs) by binding with microRNAs (miRNAs), as indicated by recent research. In the present study, we established cisplatin-induced AKI model in mice and isolated renal tubular tissues to extract circRNAs for next-generation sequencing (NGS) and bioinformatics analysis. We analyzed the composition, distribution and Gene Ontology terms of circRNAs in cisplatin-induced AKI and revealed differentially expressed circRNAs related to AKI. By finding homologous genes between mouse and human, we identified circRNA- circ-0114427 in humans. We further investigated its function in AKI cell model. Circ-0114427 expression was significantly up-regulated in different AKI cell models. Knockdown of circ-0114427 indicated that circ-0114427 bound to miR-494 as a miRNA sponge to regulate ATF3 expression and further affected the expression of downstream cytokine IL-6. Circ-0114427 regulates inflammatory progression in AKI's early stage via circ-0114427/miR-494/ATF3 pathway. Our findings reveal the expression profile of circRNAs in cisplatin-induced AKI and provide a novel insight into the regulatory mechanism of circRNAs, which may become a new molecular target resource for early diagnosis and treatment strategies.
Subject(s)
Acute Kidney Injury/genetics , Inflammation/genetics , RNA, Circular/metabolism , Sequence Analysis, RNA , Transcriptome/genetics , Activating Transcription Factor 3/metabolism , Animals , Base Sequence , Cell Line , Cisplatin , Gene Expression Profiling , Gene Expression Regulation , Humans , Interleukin-6/metabolism , Male , Mice, Inbred C57BL , MicroRNAs/genetics , MicroRNAs/metabolism , Models, Biological , RNA, Circular/geneticsABSTRACT
Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome-wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor-operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival-predicting model and promising data resources for future in-depth studies on AS mechanisms in KIRC.
Subject(s)
Alternative Splicing/genetics , Carcinoma, Renal Cell/genetics , Kidney Neoplasms/genetics , Carcinoma, Renal Cell/pathology , Cohort Studies , Gene Expression Regulation, Neoplastic , Gene Ontology , Humans , Kidney Neoplasms/pathology , Models, Biological , Multivariate Analysis , Prognosis , Survival AnalysisABSTRACT
Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated. In the present study, an integrated bioinformatics analysis was applied to further explore novel potential gene targets for IgAN. The mRNA expression profile datasets GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis of DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, and hub genes were identified through the degree of connectivity in the PPI. The hub genes were subjected to Kyoto Encyclopedia of Genes and Genomes pathway analysis, and co-expression analysis was performed. A total of 298 DEGs between IgAN and control groups were identified, and 148 and 150 of these DEGs were upregulated and downregulated, respectively. The DEGs were enriched in distinct GO terms for Biological Process, including cell growth, epithelial cell proliferation, ERK1 and ERK2 cascades, regulation of apoptotic signaling pathway and ECM organization. The top 10 hub genes were then screened from the PPI network by Cytoscape. As novel hub genes, Fos proto-oncogene, AP-1 transcription factor subunit and early growth response 1 were determined to be closely associated with apoptosis and cell proliferation in IgAN. Tumor protein 53, integrin subunit ß2 and fibronectin 1 may also be involved in the occurrence and development of IgAN. Co-expression analysis suggested that these hub genes were closely linked with each other. In conclusion, the present integrated bioinformatics analysis provided novel insight into the molecular events and novel candidate gene targets of IgAN.