Dataset Information

The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.

ABSTRACT:

Background

Inflammation within areas of interstitial fibrosis and tubular atrophy (IF/TA) is associated with kidney allograft failure. The aim of this study was to reveal new diagnostic markers of IF/TA based on bioinformatics analysis.

Methods

Raw data of IF/TA samples after kidney transplantation and control samples after kidney transplantation were extracted from the Gene Expression Omnibus (GEO) database (GSE76882 and GSE120495 datasets), and genes that were differentially expressed between the two groups (DEGs) were screened. Gene Set Enrichment Analysis (GSEA), ESTIMATE and single sample GSEA (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression analysis, and competing endogenous RNA (ceRNA) network were used to analyze the data.

Results

The results of GSEA revealed that multiple immune-related pathways were enriched in the IF/TA group, and subsequent immune landscape analysis also showed that the IF/TA group had higher immune and stromal scores and up to 15 types of immune cells occupied them, such as B cells, cytotoxic cells, and T cells. LASSO regression analysis selected 6 (including ANGPTL3, APOH, LTF, FCGR2B, HLA-DQA2, and EGF) out of 14 DE-IRGs as diagnostic genes to construct a diagnostic model. Then, receiver operating characteristic (ROC) curve analysis showed the powerful diagnostic value of the model, and the area under the curve (AUC) of a single diagnostic gene was greater than 0.75. The results of ingenuity pathway analysis (IPA) also indicated that DEGs were involved in the immune system and kidney disease-related pathways. Finally, we found multiple miRNAs that could regulate diagnostic genes from the ceRNA network.

Conclusion

This study identified 6 IF/TA-related genes, which might be used as a new diagnosis model in the clinical practice.

SUBMITTER: Yang B

PROVIDER: S-EPMC9232312 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.

Yang Bin B Shi Dike D Chen Yahong Y Zhu Yi Y

Journal of immunology research 20220617

<h4>Background</h4>Inflammation within areas of interstitial fibrosis and tubular atrophy (IF/TA) is associated with kidney allograft failure. The aim of this study was to reveal new diagnostic markers of IF/TA based on bioinformatics analysis.<h4>Methods</h4>Raw data of IF/TA samples after kidney transplantation and control samples after kidney transplantation were extracted from the Gene Expression Omnibus (GEO) database (GSE76882 and GSE120495 datasets), and genes that were differentially exp ...[more]

PMID: 35755170

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Project description:Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

Project description:BackgroundThis study examines the effect of interstitial inflammation and interstitial fibrosis and tubular atrophy on renal survival in lupus nephritis.MethodsBaseline characteristics, initial (n = 301) and repeat biopsies (n = 94) and clinical outcomes for patients with biopsy-proven lupus nephritis from 1998 to 2014 were retrospectively collected from the medical record. Clinical and morphologic variables were evaluated using a Cox proportional hazards model and multiple imputation to address missing data. Renal survival was defined as the time from initial biopsy to end-stage renal disease [estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m2], dialysis or transplant.ResultsA total of 218 patients had follow-up and Class IV had worse renal survival, especially in patients with active and chronic glomerular lesions {relative to non-IV; Class IV-A: hazard ratio [HR] 0.92 [95% confidence interval (CI) 0.41-2.04], Class IV-AC: HR 5.02 [95% CI 2.70-9.36]}. Interstitial inflammation grade [relative to interstitial inflammation <5%; interstitial inflammation 5-25%: HR 2.36 (95% CI 1.13-4.91), interstitial inflammation 25-50%: HR 3.84 (95% CI 1.53-9.62), interstitial inflammation >50%: HR 7.67 (95% CI 3.75-15.67)] and increased interstitial fibrosis and tubular atrophy (IFTA) category [relative to IFTA <5%; IFTA 5-25%: HR 3.93 (95% CI 1.58-9.75), IFTA 25-50%: HR 4.01 (95% CI 1.37-11.70), IFTA >50%: HR 13.99 (95% CI 4.91-39.83)] predicted worse renal survival among all patients and those with Class IV on initial and repeat biopsy (n = 94) in a dose-dependent manner. Interstitial inflammation grade and IFTA category were significant predictors of renal survival in a multivariable model adjusted for age, gender, race, ethnicity and serum creatinine.ConclusionsInterstitial inflammation and IFTA independently affect renal survival and grading these lesions stratifies risk within the International Society of Nephrology and Renal Pathology Society classification of lupus nephritis.

Project description:ImportanceInterstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available.ObjectiveTo develop and validate a deep learning (DL) algorithm to quantify IFTA from kidney ultrasonography images.Design, setting, and participantsThis was a single-center diagnostic study of consecutive patients who underwent native kidney biopsy at John H. Stroger Jr. Hospital of Cook County, Chicago, Illinois, between January 1, 2014, and December 31, 2018. A DL algorithm was trained, validated, and tested to classify IFTA from kidney ultrasonography images. Of 6135 Crimmins-filtered ultrasonography images, 5523 were used for training (5122 images) and validation (401 images), and 612 were used to test the accuracy of the DL system. Kidney segmentation was performed using the UNet architecture, and classification was performed using a convolution neural network-based feature extractor and extreme gradient boosting. IFTA scored by a nephropathologist on trichrome stained kidney biopsy slide was used as the reference standard. IFTA was divided into 4 grades (grade 1, 0%-24%; grade 2, 25%-49%; grade 3, 50%-74%; and grade 4, 75%-100%). Data analysis was performed from December 2019 to May 2020.Main outcomes and measuresPrediction of IFTA grade was measured using the metrics precision, recall, accuracy, and F1 score.ResultsThis study included 352 patients (mean [SD] age 47.43 [14.37] years), of whom 193 (54.82%) were women. There were 159 patients with IFTA grade 1 (2701 ultrasonography images), 74 patients with IFTA grade 2 (1239 ultrasonography images), 41 patients with IFTA grade 3 (701 ultrasonography images), and 78 patients with IFTA grade 4 (1494 ultrasonography images). Kidney ultrasonography images were segmented with 91% accuracy. In the independent test set, the point estimates for performance matrices showed precision of 0.8927 (95% CI, 0.8682-0.9172), recall of 0.8037 (95% CI, 0.7722-0.8352), accuracy of 0.8675 (95% CI, 0.8406-0.8944), and an F1 score of 0.8389 (95% CI, 0.8098-0.8680) at the image level. Corresponding estimates at the patient level were precision of 0.9003 (95% CI, 0.8644-0.9362), recall of 0.8421 (95% CI, 0.7984-0.8858), accuracy of 0.8955 (95% CI, 0.8589-0.9321), and an F1 score of 0.8639 (95% CI, 0.8228-0.9049). Accuracy at the patient level was highest for IFTA grade 1 and IFTA grade 4. The accuracy (approximately 90%) remained high irrespective of the timing of ultrasonography studies and the biopsy diagnosis. The predictive performance of the DL system did not show significant improvement when combined with baseline clinical characteristics.Conclusions and relevanceThese findings suggest that a DL algorithm can accurately and independently predict IFTA from kidney ultrasonography images.

Dataset Information

The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.

Background

Methods

Results

Conclusion

Publications

The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.

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