APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment

Yu, Kuipeng; Li, Shan; Wang, Chunjie; Zhang, Yimeng; Li, Luyao; Fan, Xiaohui; Lin, Fang; Li, Haiyun; Yang, Huimin; Sun, Jia; Yang, Xiaoling

doi:10.3389/fendo.2023.1102634

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Therefore, we performed immunohistochemistry and immunofluorescence of APOC1 in patients with IgAN. APOC1 expression is elevated in the tubules of patients with IgAN, whereas glomerular expression of APOC1 is elevated in patients with DN ( Yu et al, 2023 ). Expression of APOC1 in different kidney cells implicates that there might be different functions.…”

Section: Discussionmentioning

confidence: 99%

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Ding

et al. 2023

Front. Pharmacol.

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Introduction: IgA nephropathy (IgAN) is the most common disease leading to end-stage renal disease, and tubular fibrosis represents an important risk factor for disease progression. However, research on early molecular diagnostic indicators of tubular fibrosis and the mechanisms underlying disease progression is still lacking.Methods: The GSE93798 dataset was downloaded from the GEO database. DEGs were screened and analyzed for GO and KEGG enrichment in IgAN. The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms were applied to screen for hub secretory genes. The expression and diagnostic efficacy of hub genes were confirmed by the GSE35487 dataset. ELISA was applied to detect the expression of APOC1 in serum. The expression and localization of hub genes in IgAN were verified by the expression of IHC and IF in human kidney tissues, and the correlation of expression with clinical data was verified in the Nephroseq database. Finally, cellular experiments clarified the role of hub genes in the signaling pathway.Results: A total of 339 DEGs were identified in IgAN, of which 237 were upregulated and 102 downregulated. The KEGG signaling pathway is enriched in the ECM–receptor interaction and AGE-RAGE signaling pathway. APOC1, ALB, CCL8, CXCL2, SRPX2, and TGFBI identified six hub secretory genes using the LASSO and SVM-RFE algorithms. In vivo and in vitro experiments demonstrated that APOC1 expression was elevated in IgAN. The serum concentration of APOC1 was 1.232 ± 0.1812 μg/ml in IgAN patients, whereas it was 0.3956 ± 0.1233 μg/ml in healthy individuals. APOC1 exhibited high diagnostic efficacy for IgAN (AUC of 99.091%, specificity of 95.455%, and sensitivity of 99.141%) in the GSE93798 dataset. APOC1 expression negatively correlated with eGFR (R2 = 0.2285, p = 0.0385) and positively correlated with serum creatinine (R2 = 0.41, p = 0.000567) in IgAN. APOC1 exacerbated renal fibrosis, possibly in part by activating the NF-κB pathway in IgAN.Conclusion: APOC1 was identified as the core secretory gene of IgAN, which was closely associated with blood creatinine and eGFR and had significant efficacy in the diagnosis of IgAN. Mechanistic studies revealed that the knockdown of APOC1 could improve IgAN renal fibrosis by inhibiting the NF pathway, which may be a potential therapeutic target for improving renal fibrosis in IgAN.

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Section: Discussionmentioning

confidence: 99%

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Ding

et al. 2023

Front. Pharmacol.

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“…APOC1 concentrations were higher in patients with type 2 diabetes and significantly positively correlated with plasma TG levels, and not visceral adipose tissues 54 . APOC1 serum concentrations were also higher in patients with diabetic nephropathy (DN) compared with the healthy population 55 . The plasma APOC3 levels were also associated with higher TG levels and higher coronary artery calcification in patients with type 2 diabetes 56 .…”

Section: Circulating Levels Of Lpl and Related Apo...mentioning

confidence: 99%

Role of glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 in hypertriglyceridemia and diabetes

Kurooka

Eguchi

Wada

2023

J of Diabetes Invest

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In diabetes, the impairment of insulin secretion and insulin resistance contribute to hypertriglyceridemia, as the enzymatic activity of lipoprotein lipase (LPL) depends on insulin action. The transport of LPL to endothelial cells and its enzymatic activity are maintained by the formation of lipolytic complex depending on the multiple positive (glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 [GPIHBP1], apolipoprotein C‐II [APOC2], APOA5, heparan sulfate proteoglycan [HSPG], lipase maturation factor 1 [LFM1] and sel‐1 suppressor of lin‐12‐like [SEL1L]) and negative regulators (APOC1, APOC3, angiopoietin‐like proteins [ANGPTL]3, ANGPTL4 and ANGPTL8). Among the regulators, GPIHBP1 is a crucial molecule for the translocation of LPL from parenchymal cells to the luminal surface of capillary endothelial cells, and maintenance of lipolytic activity; that is, hydrolyzation of triglyceride into free fatty acids and monoglyceride, and conversion from chylomicron to chylomicron remnant in the exogenous pathway and from very low‐density lipoprotein to low‐density lipoprotein in the endogenous pathway. The null mutation of GPIHBP1 causes severe hypertriglyceridemia and pancreatitis, and GPIGBP1 autoantibody syndrome also causes severe hypertriglyceridemia and recurrent episodes of acute pancreatitis. In patients with type 2 diabetes, the elevated serum triglyceride levels negatively correlate with circulating LPL levels, and positively with circulating APOC1, APOC3, ANGPTL3, ANGPTL4 and ANGPTL8 levels. In contrast, circulating GPIHBP1 levels are not altered in type 2 diabetes patients with higher serum triglyceride levels, whereas they are elevated in type 2 diabetes patients with diabetic retinopathy and nephropathy. The circulating regulators of lipolytic complex might be new biomarkers for lipid and glucose metabolism, and diabetic vascular complications.

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“… 11 Yu et al analyzed differentially expressed genes in DN patients and found that Apolipoprotein C1 (APOC1) may be a candidate intervention target for DN. 12 These biomarkers have shown promise in various studies, but their integration into a comprehensive predictive model for DN remains an area of active investigation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study

Zhu,

Zhang,

Yang

et al. 2024

DMSO

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Purpose Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk of developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop and validate a machine learning-based predictive model using integrated biomarkers. Methods A cross-sectional analysis was conducted on a baseline dataset involving 2184 participants without DN, categorized based on their development of DN over a follow-up period of 36 months: DN (n=1270) and Non-DN (n=914). Various demographic and clinical parameters were analyzed. The findings were validated using an independent dataset comprising 468 participants, with 273 developing DN and 195 remaining as Non-DN over the follow-up period. Machine learning algorithms, alongside traditional descriptive statistics and logistic regression were used for statistical analyses. Results Elevated levels of serum creatinine, urea, and reduced eGFR, alongside an increased prevalence of retinopathy and peripheral neuropathy, were prominently observed in those who developed DN. Validation on the independent dataset further confirmed the model’s robustness and consistency. The SVM model demonstrated superior performance in the training set (AUC=0.79, F1-score=0.74) and testing set (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors of DN included serum creatinine, eGFR, presence of diabetic retinopathy, and peripheral neuropathy. Conclusion Integrating machine learning algorithms with clinical and biomarker data at baseline offers a promising avenue for identifying individuals at risk of developing diabetic nephropathy in type 2 diabetes patients over a 36-month period.

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APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment

Cited by 4 publications

References 38 publications

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Role of glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 in hypertriglyceridemia and diabetes

Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study

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