Dataset Information

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy.

ABSTRACT: Diabetic nephropathy (DN), a frequent microvascular complication of diabetes, has been recognized as a primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies found that autophagy of renal tubular epithelial cells plays an important role in DN pathogenesis. Our research aimed to investigate the differentially expressed autophagy-related genes (DEARGs) between DN and healthy renal tubule samples and identify a novel autophagy-related biomarker associated with tubulointerstitial injury in DN. In this study, gene expression profiles of renal tubules from 10 DN patients and 24 healthy controls in the GSE30122 dataset were analyzed, and 43 DEARGs were identified by bioinformatics analysis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and correlation analysis were performed on DEARGs, and the hub gene prolyl 4-hydroxylase subunit beta (P4HB) was screened by protein-protein interaction and verified by utilizing other datasets and stimulating HK-2 cells under high glucose concentration. We found that the expression of P4HB in renal tubules was correlated with renal function. In summary, our research provided novel insights for comprehension of DN molecular mechanisms and identified P4HB as a novel autophagy-related biomarker of DN.

SUBMITTER: Bai F

PROVIDER: S-EPMC9548632 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy.

Bai Fang F Yu Kuipeng K Yang Yanjiang Y Zhang Yimeng Y Ding Lin L An Xin X Feng Feng F Sun Nan N Fan Jiahui J Liu Lei L Yang Huimin H Yang Xiangdong X

Frontiers in genetics 20220926

Diabetic nephropathy (DN), a frequent microvascular complication of diabetes, has been recognized as a primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies found that autophagy of renal tubular epithelial cells plays an important role in DN pathogenesis. Our research aimed to investigate the differentially expressed autophagy-related genes (DEARGs) between DN and healthy renal tubule samples and identify a novel autophagy-related biomarker associated ...[more]

PMID: 36226178

Similar Datasets

Project description:BackgroundDiabetic retinopathy (DR) is one of the most common microvascular complications of diabetes, which is associated with damage of blood-retinal barrier and ischemia of retinal vasculature. It devastates visual acuity due to leakage of retinal vessels and aberrant pathological angiogenesis in diabetic patients. The etiology of DR is complex, accumulated studies have shown that autophagy plays an important role in the pathogenesis of DR, but its specific mechanism needs to be further studied.MethodsThis study chose the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE146615 to carry on the research. Autophagy-related genes that were potentially differentially expressed in DR were screened by R software. Then, the differentially expressed autophagy-related genes were analyzed by correlation analysis, tissue-specific gene expression, gene-ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis. Finally, retinal pigment epithelial cell line (ARPE-19) incubated with high glucose (HG) was used to mimic the DR model, and the mRNA level of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR) in vitro.ResultsA total of 23 differentially expressed autophagy-related genes (9 up-regulated genes and 14 down-regulated genes) were identified by differential expression analysis. The analysis of tissue-specific gene expression showed that these differentially expressed autophagy-related genes were enriched in the retina. GO and KEGG enrichment analysis showed that differentially expressed autophagy-related genes were significantly enriched in autophagy-related pathways such as regulation of autophagy and macroautophagy. Then 10 hub genes were identified by PPI network analysis and construction of key modules. Finally, qRT-PCR confirmed that the expression of MAPK3 in the DR model was consistent with the results of bioinformatics analysis of mRNA chip.ConclusionThrough bioinformatics analysis, we identified 23 potential DR autophagy-related genes, among which the down-regulated expression of MAPK3 may affect the occurrence and development of DR by regulating autophagy. It provides a novel insight into the pathogenesis of DR.

Project description:BackgroundAging and immune infiltration have essential role in the physiopathological mechanisms of diabetic nephropathy (DN), but their relationship has not been systematically elucidated. We identified aging-related characteristic genes in DN and explored their immune landscape.MethodsFour datasets from the Gene Expression Omnibus (GEO) database were screened for exploration and validation. Functional and pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Characteristic genes were obtained using a combination of Random Forest (RF) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm. We evaluated and validated the diagnostic performance of the characteristic genes using receiver operating characteristic (ROC) curve, and the expression pattern of the characteristic genes was evaluated and validated. Single-Sample Gene Set Enrichment Analysis (ssGSEA) was adopted to assess immune cell infiltration in samples. Based on the TarBase database and the JASPAR repository, potential microRNAs and transcription factors were predicted to further elucidate the molecular regulatory mechanisms of the characteristic genes.ResultsA total of 14 differentially expressed genes related to aging were obtained, of which 10 were up-regulated and 4 were down-regulated. Models were constructed by the RF and SVM-RFE algorithms, contracted to three signature genes: EGF-containing fibulin-like extracellular matrix (EFEMP1), Growth hormone receptor (GHR), and Vascular endothelial growth factor A (VEGFA). The three genes showed good efficacy in three tested cohorts and consistent expression patterns in the glomerular test cohorts. Most immune cells were more infiltrated in the DN samples compared to the controls, and there was a negative correlation between the characteristic genes and most immune cell infiltration. 24 microRNAs were involved in the transcriptional regulation of multiple genes simultaneously, and Endothelial transcription factor GATA-2 (GATA2) had a potential regulatory effect on both GHR and VEGFA.ConclusionWe identified a novel aging-related signature allowing assessment of diagnosis for DN patients, and further can be used to predict immune infiltration sensitivity.

Project description:BackgroundAs the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods.MethodsFirst, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related genesets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified.ResultsFinally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models.ConclusionMachine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

Project description:Prolyl 4-hydroxylase, beta polypeptide (P4HB) protein is an endoplasmic reticulum (ER) molecular chaperone protein and has been reported to be overexpressed in multiple tumor types. However, the role of P4HB in bladder cancer (BLCA) has not yet been elucidated. The aim of the present study was to investigate the prognostic value of P4HB and the association between clinicopathological characteristics and P4HB in BLCA. P4HB expression levels were assessed through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot analysis in BLCA tissues and cells. A total of 69 pairs of tumor and normal samples were used to analyze the expression of P4HB via immunohistochemical staining. A co-expression network and functional enrichment analyses were conducted to investigate the biological function of P4HB in BLCA. The protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes. The results showed that P4HB was highly expressed in BLCA cells and tissues. The area under the curve value for P4HB expression to discriminate between tumor and normal tissues was up to 0.888 (95% CI: 0.801-0.975; P<0.001) and 0.881 (95% CI: 0.825-0.937; P<0.001) in TCGA database and our database, respectively. Furthermore, the expression level of P4HB was an independent risk factor for overall survival (OS) and recurrence-free survival (RFS) by univariate and multivariate analyses. Kaplan-Meier survival analysis demonstrated that high P4HB expression was associated with low OS and RFS. Pathway enrichment analysis suggested that P4HB was involved in protein processing in the endoplasmic reticulum (ER), including N-glycan modification and protein metabolic processes responding to ER stress. PPI analysis revealed that the potential targets of P4HB were mainly involved in posttranslational protein modification and response to ER stress. In conclusion, the expression level of P4HB aid in identifying patients with early-stage BLCA and predicting the prognosis of BLCA. Therefore, P4HB may be a novel diagnostic and prognostic biomarker for BLCA.

Dataset Information

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy.

Publications

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets