Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Hasan, Md. Al Mehedi; Maniruzzaman, Md.; Shin, Jungpil

doi:10.1038/s41598-022-18273-x

Cited by 12 publications

(7 citation statements)

References 63 publications

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“…6 In addition, a bioinformatic analysis including another previous transcriptomic profiling study for IgAN glomerulus by microarray addressed a significant reduction of FOS, JUN, FOSB, EGR1, and DUSP1 in IgAN, similar to the current results. 23 Therefore, the early downregulation of JUN/FOS pathway may have particular importance in IgAN pathophysiology although the direct mechanism should be revealed in future experimental studies. Another notable gene that was downregulated in IgAN glomerulus was DUSP1.…”

Section: Discussionmentioning

confidence: 99%

Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation

Park

Kang

Kim

et al. 2022

Preprint

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Background Mesangial proliferation is a key pathologic feature of diagnosis and also a risk factor for progression in immunoglobulin A nephropathy (IgAN). We aimed to investigate the gene expression profiles of IgAN glomerulus according to the presence of mesangial proliferation. Methods We performed spatial-specific transcriptomic profiling for formalin-fixed-paraffin-embedded kidney biopsy tissues using the GeoMX Digital Spatial Profiler. A total of 3 glomeruli with direct pathologic classification for each case were profiled, including 12 cases (4 M1-IgAN, 4 M0-IgAN, and 4 donor controls). The differentially expressed genes (DEGs) were analyzed between the groups with gene ontology (GO) term annotation. Results In the result which successfully enriched glom-specific genes, M1-IgAN were distinctively separated from controls (77 upregulated and 55 downregulated DEGs), while certain DEGs were identified between M1-IgAN and M0-IgAN cases (24 upregulated and 8 downregulated DEGs) or between M0 and controls (1 upregulated and 16 downregulated DEGs). The upregulated DEG that was consistent in M1-IgAN and M0-IgAN cases was TCF21 which is known as early podocyte injury marker while the DEGs uniformly downregulated in IgAN cases included ATF3, EGR1, DUSP1, FOS, JUNB, KLF2, KLF4, NR4A1, RHOB, and ZFP36. Glomeruli from M1-IgAN cases were significantly enriched for cell surface/adhesion molecules and the gene expressions that consist vascular development or extracellular matrix. Conclusion We found certain transcriptomic differences according to the histologic changes in IgAN which was present even in the early stage of the disease. Spatial transcriptomic analysis may contribute to dissecting structure-specific pathogenesis and molecular changes in IgAN.

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Section: Discussionmentioning

confidence: 99%

Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation

Park

Kang

Kim

et al. 2022

Preprint

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“…Support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) machine algorithms were used to identify candidate hub secretory diagnostic genes. “e1071” and “glmnet” packages were applied to SVM-RFE and LASSO algorithms for gene screening research in R software ( Al Mehedi Hasan et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…The diagnosis of IgA nephropathy depends mainly on kidney tissue puncture biopsy, and there is no effective non-invasive diagnostic biomarker. In particular, bioinformatics has been extensively used for screening biomarkers and key target molecules in kidney diseases, such as diabetic nephropathy ( Zhou et al, 2019 ; Gao et al, 2021 ), membranous nephropathy ( Cai et al, 2022 ), and IgAN ( Al Mehedi Hasan et al, 2022 ; Zhang et al, 2022a ), which provided the potential for the identification of novel target molecules.…”

Section: Introductionmentioning

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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“…Identification of novel genetic biomarkers of IgAN can also be achieved with the use of machine learning and statistic-based bioinformatic models. Such approach showed that five candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) can differentiate between IgA and healthy individuals, however, their biological role is yet to be described (Al Mehedi Hasan et al 2022 ). The association between risk allele burden and the age of onset is suggestive of the cumulative effects of genetic variants on clinical disease characteristics (Kiryluk et al 2014 ).…”

Section: Omicsmentioning

confidence: 99%

Omics are Getting Us Closer to Understanding IgA Nephropathy

Mucha

Pac

Pączek

2023

Arch. Immunol. Ther. Exp.

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During the last decade, thanks to omics technologies, new light has been shed on the pathogenesis of many diseases. Genomics, epigenomics, transcriptomics, and proteomics have helped to provide a better understanding of the origin and heterogeneity of several diseases. However, the risk factors for most autoimmune diseases remain unknown. The successes and pitfalls of omics have also been observed in nephrology, including immunoglobulin A nephropathy (IgAN), the most common form of glomerulonephritis and a principal cause of end-stage renal disease worldwide. Unfortunately, the immense progress in basic research has not yet been followed by the satisfactory development of a targeted treatment. Although, most omics studies describe changes in the immune system, there is still insufficient data to apply their results in the constantly evolving multi-hit pathogenesis model and thus do to provide a complete picture of the disease. Here, we describe recent findings regarding the pathophysiology of IgAN and link omics studies with immune system dysregulation. This review provides insights into specific IgAN markers, which may lead to the identification of potential targets for personalised treatment in the future.

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Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Cited by 12 publications

References 63 publications

Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation

Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation

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

Omics are Getting Us Closer to Understanding IgA Nephropathy

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