Identification and Verification of Diagnostic Biomarkers for Glomerular Injury in Diabetic Nephropathy Based on Machine Learning Algorithms

Han, Hongdong; Chen, Yanrong; Yang, Hao; Cheng, Wei; Zhang, Sijing; Liu, Yunting; Liu, Qiuhong; Liu, Dongfang; Yang, Gangyi; Li, Ké

doi:10.3389/fendo.2022.876960

Cited by 28 publications

(21 citation statements)

References 32 publications

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“…TGFBI in urine has been shown to be relevant to the prognosis of DN (26). In addition, multiple studies have demonstrated high expression of TGFBI in the glomerular tissue of DN (15,25), and these ndings enhance the accessibility and feasibility of clinical application of TGFBI as a diagnostic marker. Activating Transcription Factor 3 (ATF3) is a member of the mammalian activation transcription factor/cAMP responsive element-binding (CREB) protein family of transcription factors Studies have found that a variety of stress stimuli, including ischemia-reperfusion injury, can induce the expression of ATF3 in the kidney (27).…”

Section: Discussionmentioning

confidence: 82%

“…It has been found that ETS Proto-Oncogene 1, Transcription Factor (ETS1) is low expressed in the glomerular tissue of DN and is closely related to matrix remodeling (14). Wang et al analyzed 5 sets of DN-associated genes and identi ed bronectin 1 (FN1) and complement component 3 (C3) as markers of immune in ltration associated with DN (15). Another study found that Monocyte Chemotactic Protein 1 (MCP1) is up-regulated in glomerular and tubular epithelium and responsible for monocyte adhesion and migration through endothelial cells (16).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Biomarkers Associated with Diagnosis of Diabetic Nephropathy Patients Based on Bioinformatics and Machine Learning

Yin¹,

Zheng²,

Xie³

et al. 2023

Preprint

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Diabetic nephropathy (DN) is a common microvascular complication of diabetes, and early diagnosis is crucial for the prognosis of patients. The purpose of this study was to identify novel diagnostic biomarkers of DN and to analyze possible associations between key genes and infiltrating immune cells. Download the GSE1009, GSE30528, and GSE47183 datasets, including human DN and normal samples, from the GEO dataset. Differentially expressed genes (DEGs) were identified in 33 DN cases and 26 control cases. SVM-RFE analysis and LASSO regression model were used to screen for possible markers. The composition patterns of 22 immune cell components in DN were determined by using CIBERSORT technique. In this study, 57 genes were identified as significantly up-regulated and 80 genes as significantly down-regulated. Analysis of KEGG signaling pathway showed that complete and coalition cascades, PI3K-Akt signaling pathway, focal adhesion, RAP1 signaling pathway and age-rage signaling pathway were significantly enriched. Based on the results of machine learning, we identified C1orf21, TGFBI, ATF3, and BHMT as key diagnostic genes for DN, which were further confirmed by ROC analysis. Immunocyte infiltration analysis revealed four key diagnostic gene expressions associated with multiple immunocytes in DN. Overall, our findings may offer some new insights into the exploration of new markers for the diagnosis of DN. Key genes and their relationship with immune infiltration may provide new insights into the development of DN.

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Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Identification of Biomarkers Associated with Diagnosis of Diabetic Nephropathy Patients Based on Bioinformatics and Machine Learning

Yin¹,

Zheng²,

Xie³

et al. 2023

Preprint

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show abstract

“…A diagnostic model that combines PRKAR2B and TGFBI was established to assess the risk of diabetic glomerular injury with high sensitivity and accuracy. Additionally, the potential association with in ltrating immune cells was demonstrated, providing a novel perspective on their roles in DN [12]. In addition to the identi cation of PRKAR2B and TGFBI as potential biomarkers of DN, this study also identi ed 92 immune score-related DEGs (ISRDEGs) that were enriched in in ammation-and immune-associated pathways.…”

Section: Introductionmentioning

confidence: 84%

Single-cell and transcriptome analysis reveals TAL cells in diabetic nephropathy

Zhang

Wang

2023

Preprint

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Diabetic nephropathy is a global public health concern with multifaceted pathogenesis, primarily involving hypertension. Excessive activation of AT1R has been strongly associated with hypertension onset and progression in diabetic nephropathy. This study aimed to conduct thick ascending limb cell single-cell and transcriptomic analysis in diabetic nephropathy, including screening for biological markers, cellular communication, and immune infiltration, to identify potential biomarkers and effective means for prevention and treatment. By using high-dimensional weighted gene co-expression network analysis, Least Absolute Shrinkage and Selection Operator, machine learning, neural deconvolution, quasi-chronological analysis, Non-negative Matrix Factorization clustering, and Monocyte Chemotactic Protein-induced Counter, we identified 7 potential thick ascending limb cell biomarkers for diabetic nephropathy and elucidated the Bone Morphogenetic Protein pathway's regulation of thick ascending limb cells through podocyte epithelial cells and podocyte cells. The study also highlighted the role of COBL, PPARGC1A, and THSD7A in Non-negative Matrix Factorization clustering and their relationship with thick ascending limb cell immunity in diabetic nephropathy. Our findings provide new insights and avenues for managing diabetic nephropathy, ultimately alleviating the burden on patients and society.

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“…Diao identified eight hub genes of DKD, such as Scd5, Coasy, and Idi1, by constructing a PPI network ( 28 ). Han used machine learning to obtain two diagnostic markers of protein kinase cAMP-dependent type II regulatory subunit beta and transforming growth factor beta 1 (PRKAR2B and TGFBI, respectively) ( 29 ) in glomerular injury in diabetic nephropathy. Wei screened and identified biomarkers in early DKD based on WGCNA, and initially explored the biological functions of candidate markers ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

Ming

Zhu

et al. 2023

Front. Endocrinol.

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IntroductionDiabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD.MethodsWe downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA.ResultsBiological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers.ConclusionIn conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.

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Identification and Verification of Diagnostic Biomarkers for Glomerular Injury in Diabetic Nephropathy Based on Machine Learning Algorithms

Cited by 28 publications

References 32 publications

Identification of Biomarkers Associated with Diagnosis of Diabetic Nephropathy Patients Based on Bioinformatics and Machine Learning

Identification of Biomarkers Associated with Diagnosis of Diabetic Nephropathy Patients Based on Bioinformatics and Machine Learning

Single-cell and transcriptome analysis reveals TAL cells in diabetic nephropathy

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

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