Ferroptosis-related genes, a novel therapeutic target for focal segmental glomerulosclerosis

doi:10.21203/rs.3.rs-3146694/v1

Download PDF

Research Article

Ferroptosis-related genes, a novel therapeutic target for focal segmental glomerulosclerosis

https://doi.org/10.21203/rs.3.rs-3146694/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 17 Feb, 2024

Read the published version in BMC Nephrology →

You are reading this latest preprint version

Recent studies have suggested that ferroptosis participates in various renal diseases. However, its effect on focal segmental glomerulosclerosis remains unclear. This study analyzed the GSE125779 and GSE121211 datasets to identify the differentially expressed genes (DEGs) in renal tubular samples with and without FSGS. The Cytoscape was used to construct the protein-protein interaction network. Moreover, the ferroptosis-related genes (FRGs) were obtained from the ferroptosis database, while ferroptosis-related DEGs were obtained by intersection with DEGs. The target genes were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The GSE108112 dataset was used to verify the expression of target FRGs. Besides, we built the mRNA-miRNA network regarding FRGs using the NetworkAnalyst database, and circRNAs corresponding to key miRNAs were predicted in the ENCORI database. In this study, 16 ferroptosis-related DEGs were identified between FSGS and healthy subjects, while five co-expressed genes were obtained by three topological algorithms in Cytoscape. These included the most concerned Hub genes JUN, HIF1A, ALB, DUSP1, and ATF3. The KEGG enrichment analysis indicated that FRGs were associated with mitophagy, renal cell carcinoma, and metabolic pathways. Simultaneously, the co-expressed hub genes were analyzed to construct the mRNA-miRNA interaction network and important miRNAs such as hsa-mir-155-5p, hsa-mir-1-3p, and hsa-mir-124-3p were obtained. Finally, 75 drugs targeting 57 important circRNAs and FRGs were predicted. This study identified the Hub FRGs and transcriptomic molecules from FSGS in renal tubules, thus providing novel diagnostic and therapeutic targets for FSGS.

Bioinformatics

Ferroptosis

Focal segmental glomerulosclerosis (FSGS)

Renal tubule

Focal segmental glomerulosclerosis (FSGS) is characterized by partial glomerular (focal) or partial glomerular capillary loops (segmental) sclerosis. In most parts of the world, FSGS is one of the leading causes of glomerulopathy in end-stage renal disease (ESRD) (Gipson et al. 2011) (Korbet 2002) (Deegens et al. 2005). The treatment of FSGS mainly includes corticosteroids, immunosuppressive therapy, and kidney transplantation. Unfortunately, FSGS patients have become increasingly resistant to corticosteroids and immunosuppressive therapy, thus showing a limited response to FSGS. The existing treatment modalities and efficacy of FSGS are insufficient in delaying the progression of ESRD, which necessitates more effective treatments for the management of FSGS (D'Agati 1994) (Collins et al. 2010) (Laurin et al. 2016) (Tang et al. 2019). The study reported that the degree of tubular injury and interstitial lesions in FSGS patients were related to the degree and extent of glomerular involvement. With the progression of glomerular disease, tubular atrophy and interstitial fibrosis increase. However, there is a lack of studies on the expression of tubular genes in diseased kidneys.

Ferroptosis was first proposed by Dr. Brent R. Tockwell in 2012. It is defined as a new programmed cell death procedure characterized by the accumulation of reactive oxygen species and the overload of intracellular ions, thus causing lipid peroxidation (Hu et al. 2019). Its mechanism largely involves the upregulation of unsaturated fatty acids (USFAs) on cell membranes based on ester oxygenase and divalent ion effect, while USFAs can be catalyzed to produce lipid peroxidation, eventually causing cell apoptosis. Ferroptosis takes place during various chronic disorders, including renal cancer, pancreatic cancer, Parkinson's syndrome, and cardiomyopathy (Wang et al. 2020). The involvement of ferroptosis in acute kidney injury (AKI) has been reported in nephrology but not in FSGS.

Previous studies have reported that ferroptosis does not have the morphological characteristics of traditional apoptosis, such as cell wrinkling, chromatin agglutination, and apoptotic body formation, although the characteristic changes such as mitochondrial wrinkling and increased bilayer density can be observed by electron microscopy. Studies on the model of FSGS induced by Adriamycin amycin (ADR) in mice have reported changes such as mitochondrial atrophy, reduction in mitochondrial crista, and rupture of the mitochondrial membrane in the podocytes of mice models of FSGS, suggesting that the mitochondria of FSGS podocytes have morphological changes that characterize ferroptosis (Stoyanovsky et al. 2019). Recent studies have confirmed that both GSH depletion and lipid peroxide accumulation are characteristic changes of ferric death (Friedmann Angeli and Conrad 2018). Decreased GSH will impact the glutathione peroxidase activity, decrease the cell antioxidant ability, and promote lipid ROS and lipid peroxidation, resulting in ferric death (van Swelm et al. 2022). The concentration of GSH in the kidney tissue of the above FSGS model mice was significantly decreased, while the concentration of MDA, a product of lipid peroxidation, was significantly increased, indicating the occurrence of ferric death in FSGS. In 2022, Vanswelm et al. published a study suggesting that the accumulation of renal iron in experimental FSGS was associated with the progression of tubulointerstitial (TI) injury in FSGS models. Besides, the reduced renal iron load avoided TI fibrosis (van Swelm et al. 2022). This offers a specific method for the reduction of renal iron load while maintaining the minimal cell iron level required to maintain an optimal balance of renal iron. Thus, the mechanism of ferric death in FSGS is still unclear, with studies focusing on renal tubular tissue in FSGS being rare. Therefore, the study of differential genes involved in renal tubular lesions in FSGS and the interaction between them will improve understanding of this field.

In this study, the DEGs of the two datasets were retrieved through the GEO database, and the rich biological functions and pathway information of the differential genes were obtained through the Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Then, the ferroptosis-related genes (FRGs) in FSGS were obtained by a Venn diagram, and incorporated into the STRING database to construct a corresponding protein-protein interaction (PPI) network. Thereafter, the five most significantly correlated genes were chosen as the key genes using the CytoHubba plugin. The GSE108112 dataset was then used to assess the expression of these genes. The mRNA-miRNA network was constructed based on the five central genes to explore the possible regulation of miRNAs on FRGs in FSGS and explore the potential circRNAs and target drugs.

Data acquisition

Data were obtained from the mRNA expression profile datasets GSE121211, GSE125779, and GSE108112 from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (Barrett et al. 2013). Based on the FerrDb database (http://www.zhounan.org/ferrdb/), 259 genes were observed to be associated with ferroptosis.

Screening for differential genes associated with ferroptosis

We used the program GEO2R to compare gene expression spectra between the normal group and disease group, using the GEO query package and the Limma package to extract data, remove the batch effect, and identify DEGs, including screening the standard set to P < 0.05 and |logFC|≥1. The common upregulated and downregulated genes of the two datasets were identified by the Venn diagram. DEGs from the two datasets were obtained based on the criteria of P < 0.05 and |logFC| ≥ 0.5, and genes associated with ferroptosis were identified according to their differential expression between the normal and disease groups and through the FerrDb database and termed as ferroptosis-related genes.

GO functional enrichment analysis and KEGG pathway enrichment analysis of differential genes

The GO database (https://geneontology.org) (The Gene Ontology 2019) was established by the Gene Ontology Consortium and included biological processes (BP), molecular functions (MF), and cellular components (CC). The KEGG Database (https://www.kegg.jp/) (Kanehisa et al. 2021) is a database that integrates information on the genomic, chemical, and systematic functions. Metascape (http://metascape.org) (Zhou et al. 2019b) integrates over 40 bioinformatics databases, including rich gene annotation functions, enrichment analysis of biological pathways, and structural analysis of protein-interaction networks, and presents the results as high-quality charts. We used Metascape to analyze the function of differential genes. We input the DEGs of the two datasets on the main interface and select "H.sapiens". The conditions of "Min Enrichment = 1.5, P < 0.05; Min overlap = 3" indicated statistically significant differences.

Construction of PPI network and module analysis

The STRING database (https://string-db.org/) was used to analyze the PPI network for DEGs (Szklarczyk et al. 2019).Then, the software Cytoscape (version 3.9.0) was employed to import the results of the STRING analysis. The top five most significant genes were selected as the hub genes based on MCC, degree, and MNC algorithm, using the CytoHubba plugin of Cytoscape(Otasek et al. 2019).

Construction of an mRNA-miRNA regulatory network

The NetworkAnalyst3.0 database (https://www.networkanalyst.ca/) was used to predict the associations of differentially expressed mRNAs (DEmRNAs) with differentially expressed miRNAs (DEmiRNAs) (Zhou et al. 2019a). Then, the mRNA-miRNA regulatory network was built to describe the miRNA-mRNA interactions as the possible targets for tubular tissues from kidneys with FSGS. We then visualized the network using the Cytoscape software.

Screening of circRNAs corresponding to miRNAs

The online database ENCORI (https://starbase.sysu.edu.cn/) (Li et al. 2014) was used to predict the interactions between differential expression of microRNAs and circRNA. The circRNAs corresponding to the previously predicted key miRNAs are presented as UpSet graphics.

Validation of ferroptosis hub genes

The programs “ggplot2 and pROC” (R3.6.3) were used to perform the receiver operating characteristic (ROC) analysis to determine the specificity and sensitivity of target genes. Then, the area under the ROC curve (AUC) value was determined to quantify the results. Genes showing an AUC value of > 0.6 were identified as the diagnostic genes.

Prediction of potential therapeutic drugs

Genes from the DSigDB database (http://tanlab.ucdenver.edu/DSigDB)-drug interaction data used to predict the potential therapeutic drugs for FSGS.

Screening for differential genes

The GSE121211 dataset contains five FSGS samples and five normal samples, while the GSE15779 dataset contains eight FSGS and eight corresponding normal specimens (Figure 1). After normalization of the expression matrices of the two datasets, the box plot distribution trend revealed nearly straight lines (Figure 2A-2B). To assess the in-group data reproducibility, UMAP analysis was performed on the two datasets, which showed good reproducibility (Figure 2C-2D). Through the “limma” package of the R language, the analysis of variance between the two datasets (screening criteria were P < 0.05 and |logFC| ≥ 1) revealed 340 upregulated and 160 downregulated DEGs from the GSE121211 dataset (Figure 2E) and 39 upregulated, and 63 downregulated DEGs from the GSE125779 dataset (Figure 2F). A total of 82 differentially expressed genes were screened using the Venn diagram, and one gene with an inconsistent expression trend was excluded, resulting in 81 genes. A total of 35 upregulated and 46 downregulated DEGs were obtained (Figure 3A-3B).

KEGG and GO enrichment analysis of differential genes

To analyze the DEG-related biological classification, GO and KEGG analyses of the obtained DEGs were conducted. The KEGG analysis indicated that the DEGs were mostly associated with focal adhesion, malaria, age-range pathway, diabetic complications, complement and coagulation cascades, fluid shear stress, atherosclerosis, microRNAs in hormone synthesis, secretion and action of cancer, parathyroid hormone, proteoglycans in cancer, and regulation of the actin cytoskeleton.

GO enrichment analysis was conducted from the biological process (BP), molecular function (MF), and cell component (CC). Our enrichment results indicated that the DEGs in FSGS were mainly associated with the cellular response to organonitrogen compounds, cellular response to growth factor stimulus, development of vasculature, negative regulation of response to external stimulus, regulation of epithelial cell proliferation, Ritz response, transport of nitric oxide, regulation of peptidase activity, response to extracellular stimulus, multicellular organismal homeostasis, and other BPs. The products of differential genes were mainly distributed in the platelet alpha granule, collagen-containing extracellular matrix, endocytic vesicular lumen, endoplasmic reticular lumen, transcription factor AP-1 complex, tertiary granular lumen, vacuolar lumen, microvillus, and sarcolemma, among others. The molecular functions involved include binding of growth factor, extracellular matrix structural constituent, binding of tissue-derived growth factor, heme binding, integrin binding, peptidase regulator activity, glucocorticoid receptor binding, proteoglycan binding, calcium ion binding, mRNA 3'-UTR binding, enzyme activator activity, and growth factor receptor binding, among others (Figure 4A-4D).

Ferroptosis-related genes in FSGS

A total of 259 FRGs were obtained from the ferroptosis database, including 108 genes with driver function, 69 genes with suppressor function, 111 genes with marker function, and one gene with three different roles (HMOX1) (Figure 5A). When the two-cube variance analysis of the datasets GSE121211 and GSE125779 was set to the standard P < 0.05 and |logFC| ≥ 0.5, 466 different genes and 16 ferroptosis-related genes were obtained with the combination of the related database (Figure 5B). The GO functional enrichment analysis of 16 ferroptosis-related genes and KEGG pathway enrichment analysis results in Figure 5C-5D. The results of the PPI network analysis are presented in Figure 5E. Interaction maps of important proteins were obtained by the software Cytoscape, with a total of 19 edges and 12 nodes, with one edge representing protein interaction and one node representing one protein. The node size was adjusted according to the degree. PPI results were imported into Cytoscape and opened with the CytoHubba plugin. The top five genes of MCC, MNC, and degree topology algorithms were selected to obtain common genes as ferroptosis-related hub genes (Figure 5F-5H). A total of five hub genes were obtained, which were JUN, ALB, ATF3, HIF1A, and DUSP1 (Table 1).

Gene network establishment and GO/KEGG analysis of FRGs

The program NetworkAnalyst 3.0 was used to predict the target miRNAs for key genes. Finally, 240 target miRNAs for five specific FRHGs were obtained, and 333 mRNA-miRNA pairs were determined (Figure 6C). A total of 118 miRNAs regulated JUN, 101 regulated HIF1A, 79 regulated DUSP1, nine modulated ALB, and 26 modulated ATF3. All four miRNAs hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-27a-5p, and hsa-mir-1-3p were correlated with the five specifically expressed FRHGs, which is highly significant. Each of the four miRNAs, hsa-mir-6-5p, hsa-mir-30a-5p, hsa-mir-107, and hsa-mir-10b-5p had four genes associated with them. Seven circRNAs corresponding to miRNAs were screened in the ENCORI database (no corresponding circRNA was predicted in the ENCORI database for hsa-mir-27a-5p), and a total of 54 common associated circRNAs were obtained (Figure 7).

The five ferroptosis-related DEGs were subjected to GO and KEGG analysis. The most significantly enriched GO terms included transcription regulation of RNA polymerase II promoter upon stress, regulation of DNA-templated transcription upon stress, response to starvation, nuclear transcription factor complex, RNA polymerase II transcription factor complex, the activity of DNA-binding transcription activator, transcription factor complex, and RNA polymerase II-specific and nuclear chromatin. The results of KEGG indicated that these DEGs were mostly associated with mitophagy-animal, renal cell carcinoma, choline metabolism, Th17 cell differentiation, PD-L1, expression, and PD-1 checkpoint pathway in cancer (Figure 6A-6B).

GSE108112 confirmed the expression and diagnostic value of FRHGs

The dataset GSE108112 was used to check the levels of selected targets. It was observed that three key FRGs (ALB, ATF3, and DUSP1), which were differentially expressed between renal tubular tissues with and without FSGS in patients receiving tumor nephrectomy, conformed to the predicted results (Figure 8A-8E).

Then, the GSEA approach was used for functional enrichment between FSGS and normal subjects, and the obtained results were compared with the five above-mentioned genes. It was observed that the genes were mainly present in the REACTOME_M_PHASE (NES =-1.599; P.adjust = 0.017; FDR = 0.012) and REACTOME_NEUTROPHIL_DEGRANULATION (NES =-2.312; P.adjust = 0.017; FDR = 0.012) signal pathway enrichment (Figure 9A-9C).

The ROC curves were plotted based on the obtained data in renal tubular tissues with and without FSGS. The above five genes were identified as the diagnostic genes for FSGS. The AUC value of the variable DUSP1 was 0.962 (95% CI: 0.926–0.999), while the AUC values for ATF3, ALB, JUN, and HIF1A were 0.908 (95% CI: 0.0838–0.978), 0.834 (95% CI: 0.730–0.938), 0.730 (95% CI: 0.589–0.0.871), and 0.650 (95% CI: 0.519–0.781), respectively (Figure 8F-8J).

Targeted drug prediction

We used the DSigDB database to predict the potential targeted agents that were associated with FRGs, which might treat FSGS by modulating ferroptosis. A total of 75 drugs were predicted, among which, nitroglycerin was identified as a targeted inhibitor of HIF1A (Table 2).

In this study, bioinformatics was used to screen the differential genes related to ferroptosis in the FSGS renal tubule tissue and healthy control datasets, yielding a total of 16 FRGs. By constructing the PPI interaction network, the CytoHubba plugin was used to obtain the five most important ferroptosis-related hub genes, namely JUN, ALB, ATF3, HIF1A, and DUSP1. The related pathways and biological functions were enriched and analyzed to identify the vital functions of these genes in transcriptional regulation. These genes play a significant role in mitochondria and tumor metabolic pathways. By constructing the mRNA-miRNA network of five genes, four miRNAs with the highest degree of relationship were found, namely hsa-mir-155-3p, hsa-mir-124-3p, hsa-mir-27a-5p, and hsa-mir-1-3p. By analyzing the key miRNAs, the corresponding 57 circRNAs and potential disease-targeting drugs such as nitroglycerin to inhibit HIF1A may represent a breakthrough in the treatment of FSGS.

HIF1A, the key gene for ferroptosis that was identified in our study, has been confirmed in acute or chronic kidney injury. In the renal ischemia-reperfusion model, renal tubular epithelial cells and renal microvasculature are damaged, while iron cells can recruit and promote macrophage stimulation for the recruitment of neutrophils, thereby inducing the AKI inflammatory reaction. Importantly, ACSL4 is an important target for the regulation of ferroptosis, and HIF1A can bind to the ACSL4 promoter. In this study, the expression of ACSL4 was negatively regulated, while HIF1A was associated with response to cellular stress in FSGS (Muller et al. 2017). In another important study, it was also noted that roxaduxat, an inhibitor of HIF proline hydroxylase, protects renal tubular epithelial cells from iohexanol-induced damage both in vivo and in vitro by stabilizing the HIF1A and activating the downstream BNIP3-mediated mitophagy (Lin et al. 2021). Our study also enriched the mitophagy pathway, further demonstrating the important role of HIF1A in renal tubules. Furthermore, Wang et al. confirmed that HIF1A was a tumor suppressor in RCC by mining the ENCORI database and TCGA database, combined with a cohort study by the experimental team. Their study also reported that the Snp-mediated upregulation of lncRNA-ENTPD3-AS1 suppressed renal cell carcinoma (RCC) via the mir-155/HIF1A pathway (Wang et al. 2021). This finding was in line with the RCC-related pathways enriched in this study. Therefore, we hypothesized that the pathological mechanism of RCC and FSGS may be somewhat similar, with hsa-mir-155 and HIF1A playing an important role. There is a lack of studies on FSGS and HIF1A and the mechanism needs further exploration.

ALB has been extensively suggested to have a critical effect on the mechanism of ferric death in FSGS and is the key gene of ferric death that was enriched in this study, with a high diagnostic efficiency based on the ROC curve enrichment results. As the most abundant protein gene encoded in human blood, ALB plays an important role in regulating the osmotic pressure of plasma colloids and can be used as a carrier protein for various endogenous molecules such as metabolites and fatty acids, which are related to ferroptosis-related BPs such as lipid metabolism, iron metabolism, and amino acid metabolism (Cattran et al. 2003). FSGS patients with low serum albumin levels of glomerular pathological damage possibly have proteinuria, decreased serum propagation, increased compensatory synthesis and metabolism of kidney burden of liver cells, which increase hypoalbuminemia, induce infection, cause endocrine disturbance and loss of trace elements, high condensation, and impaired immune function, eventually inducing metabolic disturbance (De Vriese et al. 2021). Presently, bioinformatics has been used to analyze the whole kidney tissue to obtain the related genes involved in tissue fibrosis in the mechanism of FSGS. In this study, renal tubules were directly selected for further analysis, the target genes involved in ferroptosis were obtained, and the expression of differential genes of renal tubular lesions was further elaborated, which provided a new target for more precise treatment.

Although the mechanisms related to ferroptosis in FSGS are still unclear, bioinformatics may open a new world in FSGS research. In this study, through the construction of the mRNA-miRNA network, we observed that hsa-mir-124-3p has important research significance and plays a role in enriched tumor pathways(Li et al. 2019) (Wallace and O'Connell 2017) (Hao et al. 2017) (McGuire et al. 2015) (Chitkara et al. 2015). In a previous experiment involving an animal model of hypertension, EGR1 was found to be the target gene of hsa-mir-124-3p. In that study, silencing EGR1 could overexpress hsa-mir-124-3p, thereby inhibiting the effect of ANG-II on promoting apoptosis and the production of ROS in HUVEC cells. Simultaneously, the accumulation of ROS is among the important signs of ferroptosis, which also indicates that hsa-mir-124-3p may be involved in ferroptosis and play a role in regulating blood pressure (Lv et al. 2021). In a study on renal injury, overexpression of mir-124-3p can partially reverse the inhibition of cell proliferation, induction of apoptosis, and aggravation of cell injury caused by the overexpression of HOXA11-AS, which proves that HOXA11-AS regulates the inflammatory response in calcium oxalate crystal-induced renal injury through the mir-124-3p/McP-1 pathway (Li et al. 2020). This study demonstrates that hsa-mir-124-3p plays an important role in the ferroptosis-related mechanism of FSGS, although further experimental verification is warranted.

Most of the previous studies on FSGS were single-gene experimental confirmatory studies, focusing more on glomerular damage, with the latest studies using bioinformatics methods focusing only on the whole kidney tissue. However, our study was more accurately focused on the renal tubules, with ferric death as the novel mode of cell death, and used bioinformatics to comprehensively evaluate the effect of multiple genes on FSGS. We identified five potential FRGs (JUN, ALB, ATF3, HIF1A, and DUSP1) with good diagnostic performance. This work built the relevant mRNA-miRNA interaction network to obtain critical miRNAs such as hsa-mir-124-3p and hsa-mir-155-5p, to predict the possible related circRNAs and targeted drugs.

This study also has certain limitations. Although different validation datasets were analyzed together, the results of the analysis were predictive. The study lacks cell experiments, animal experiments, and verification. Although several studies have reported that the concentration of HIF1A in the FSGS group is significantly different from that in other groups, our validation set did not show a similar finding, thus necessitating further experimental verification. Ferroptosis and FSGS are presently poorly studied. In the future, more studies will reveal the molecular mechanisms involved in ferroptosis, which will provide more evidence for ferroptosis in the prevention and treatment of FSGS.

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analyzed by the current study are available in the GEO repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi=GSE121211, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi=GSE125779, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi=GSE108112].

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by grant from Natural Science Foundation of Fujian Province(Grant no: 2021J011330) and Natural Science Foundation of Xiamen City (Grant no: 3502Z20194016).

Authors' contributions

Yanbin Lin and Jinxuan He acquired and analysed the data. Lin Yanbin and Wenkang You prepared figures and tables. Mou Zhixiang, and Huiting Chen wrote the main manuscript text. Study supervision by Chen Lan and Guan Tianjun. All authors reviewed the manuscript.

Acknowledgements

We thank all the public databases and websites used in this paper: The GEO database, The FerrDb database, The GO database, The KEGG Database, Metascape, The STRING database, The NetworkAnalyst3.0 database, The online database ENCORI and The DSigDB database.

Barrett, T., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., Marshall, K.A., Phillippy, K.H., Sherman, P.M., Holko, M., Yefanov, A., Lee, H., Zhang, N., Robertson, C.L., Serova, N., et al. (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res, 41, D991-995.
Cattran, D., Neogi, T., Sharma, R., McCarthy, E.T. & Savin, V.J. (2003) Serial estimates of serum permeability activity and clinical correlates in patients with native kidney focal segmental glomerulosclerosis. J Am Soc Nephrol, 14, 448-453.
Chitkara, D., Mittal, A. & Mahato, R.I. (2015) miRNAs in pancreatic cancer: therapeutic potential, delivery challenges and strategies. Adv Drug Deliv Rev, 81, 34-52.
Collins, A.J., Foley, R.N., Herzog, C., Chavers, B.M., Gilbertson, D., Ishani, A., Kasiske, B.L., Liu, J., Mau, L.W., McBean, M., Murray, A., St Peter, W., Guo, H., Li, Q., Li, S., et al. (2010) Excerpts from the US Renal Data System 2009 Annual Data Report. Am J Kidney Dis, 55, S1-420, A426-427.
D'Agati, V. (1994) The many masks of focal segmental glomerulosclerosis. Kidney Int, 46, 1223-1241.
De Vriese, A.S., Wetzels, J.F., Glassock, R.J., Sethi, S. & Fervenza, F.C. (2021) Therapeutic trials in adult FSGS: lessons learned and the road forward. Nat Rev Nephrol, 17, 619-630.
Deegens, J.K., Assmann, K.J., Steenbergen, E.J., Hilbrands, L.B., Gerlag, P.G., Jansen, J.L. & Wetzels, J.F. (2005) Idiopathic focal segmental glomerulosclerosis: a favourable prognosis in untreated patients? Neth J Med, 63, 393-398.
Friedmann Angeli, J.P. & Conrad, M. (2018) Selenium and GPX4, a vital symbiosis. Free Radic Biol Med, 127, 153-159.
Gipson, D.S., Trachtman, H., Kaskel, F.J., Greene, T.H., Radeva, M.K., Gassman, J.J., Moxey-Mims, M.M., Hogg, R.J., Watkins, S.L., Fine, R.N., Hogan, S.L., Middleton, J.P., Vehaskari, V.M., Flynn, P.A., Powell, L.M., et al. (2011) Clinical trial of focal segmental glomerulosclerosis in children and young adults. Kidney Int, 80, 868-878.
Hao, N.B., He, Y.F., Li, X.Q., Wang, K. & Wang, R.L. (2017) The role of miRNA and lncRNA in gastric cancer. Oncotarget, 8, 81572-81582.
Hu, Z., Zhang, H., Yang, S.K., Wu, X., He, D., Cao, K. & Zhang, W. (2019) Emerging Role of Ferroptosis in Acute Kidney Injury. Oxid Med Cell Longev, 2019, 8010614.
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res, 49, D545-D551.
Korbet, S.M. (2002) Treatment of primary focal segmental glomerulosclerosis. Kidney Int, 62, 2301-2310.
Laurin, L.P., Gasim, A.M., Derebail, V.K., McGregor, J.G., Kidd, J.M., Hogan, S.L., Poulton, C.J., Detwiler, R.K., Jennette, J.C., Falk, R.J. & Nachman, P.H. (2016) Renal Survival in Patients with Collapsing Compared with Not Otherwise Specified FSGS. Clin J Am Soc Nephrol, 11, 1752-1759.
Li, J.H., Liu, S., Zhou, H., Qu, L.H. & Yang, J.H. (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res, 42, D92-97.
Li, Y., Yan, G., Zhang, J., Chen, W., Ding, T., Yin, Y., Li, M., Zhu, Y., Sun, S., Yuan, J.H. & Guo, Z. (2020) LncRNA HOXA11-AS regulates calcium oxalate crystal-induced renal inflammation via miR-124-3p/MCP-1. J Cell Mol Med, 24, 238-249.
Li, Y.J., Li, X.F., Yang, E.H. & Shi, M. (2019) [Reaserch Advances on the Role of PI3K/AKT Signaling Pathway and MiRNA in Acute T-Cell Lymphocytic Leukemia--Review]. Zhongguo Shi Yan Xue Ye Xue Za Zhi, 27, 1344-1347.
Lin, Q., Li, S., Jiang, N., Jin, H., Shao, X., Zhu, X., Wu, J., Zhang, M., Zhang, Z., Shen, J., Zhou, W., Gu, L., Lu, R. & Ni, Z. (2021) Inhibiting NLRP3 inflammasome attenuates apoptosis in contrast-induced acute kidney injury through the upregulation of HIF1A and BNIP3-mediated mitophagy. Autophagy, 17, 2975-2990.
Lv, L., Shen, J., Xu, J., Wu, X., Zeng, C., Lin, L., Mao, W. & Wei, T. (2021) MiR-124-3p reduces angiotensin II-dependent hypertension by down-regulating EGR1. J Hum Hypertens, 35, 696-708.
McGuire, A., Brown, J.A. & Kerin, M.J. (2015) Metastatic breast cancer: the potential of miRNA for diagnosis and treatment monitoring. Cancer Metastasis Rev, 34, 145-155.
Muller, T., Dewitz, C., Schmitz, J., Schroder, A.S., Brasen, J.H., Stockwell, B.R., Murphy, J.M., Kunzendorf, U. & Krautwald, S. (2017) Necroptosis and ferroptosis are alternative cell death pathways that operate in acute kidney failure. Cell Mol Life Sci, 74, 3631-3645.
Otasek, D., Morris, J.H., Boucas, J., Pico, A.R. & Demchak, B. (2019) Cytoscape Automation: empowering workflow-based network analysis. Genome Biol, 20, 185.
Stoyanovsky, D.A., Tyurina, Y.Y., Shrivastava, I., Bahar, I., Tyurin, V.A., Protchenko, O., Jadhav, S., Bolevich, S.B., Kozlov, A.V., Vladimirov, Y.A., Shvedova, A.A., Philpott, C.C., Bayir, H. & Kagan, V.E. (2019) Iron catalysis of lipid peroxidation in ferroptosis: Regulated enzymatic or random free radical reaction? Free Radic Biol Med, 133, 153-161.
Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J. & Mering, C.V. (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 47, D607-D613.
Tang, D., Kang, R., Berghe, T.V., Vandenabeele, P. & Kroemer, G. (2019) The molecular machinery of regulated cell death. Cell Res, 29, 347-364.
The Gene Ontology, C. (2019) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res, 47, D330-D338.
van Swelm, R.P.L., Beurskens, S., Dijkman, H., Wiegerinck, E.T.G., Roelofs, R., Thevenod, F., van der Vlag, J., Wetzels, J.F.M., Swinkels, D.W. & Smeets, B. (2022) Kidney tubule iron loading in experimental focal segmental glomerulosclerosis. Sci Rep, 12, 1199.
Wallace, J.A. & O'Connell, R.M. (2017) MicroRNAs and acute myeloid leukemia: therapeutic implications and emerging concepts. Blood, 130, 1290-1301.
Wang, H., Liu, C., Zhao, Y. & Gao, G. (2020) Mitochondria regulation in ferroptosis. Eur J Cell Biol, 99, 151058.
Wang, J., Zou, Y., Du, B., Li, W., Yu, G., Li, L., Zhou, L., Gu, X., Song, S., Liu, Y., Zhou, W., Xu, B. & Wang, Z. (2021) SNP-mediated lncRNA-ENTPD3-AS1 upregulation suppresses renal cell carcinoma via miR-155/HIF-1alpha signaling. Cell Death Dis, 12, 672.
Zhou, G., Soufan, O., Ewald, J., Hancock, R.E.W., Basu, N. & Xia, J. (2019a) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res, 47, W234-W241.
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A.H., Tanaseichuk, O., Benner, C. & Chanda, S.K. (2019b) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun, 10, 1523.

Table 1 Five hub genes and their functions.

Gene symbol	Description
JUN	Jun proto-oncogene, AP-1 transcription factor subunit
ALB	albumin
ATF3	activating transcription factor 3
HIF1A	hypoxia inducible factor 1 subunit alpha
DUSP1	Dual specificity phosphatase 1

Table 2 Drug prediction of ferroptosisrelated genes.

Gene symbol	drug
JUN	SERTRALINE、MECHLORETHAMINE HYDROCHLORIDE、TROPISETRON、BUPROPION HYDROCHLORIDE、AZELASTINE HYDROCHLORIDE、CUPRIC CHLORIDE、CIPROFIBRATE、FENOFIBRATE、VINBLASTINE SULFATE、ATOMOXETINE、HYDROCHLORIDE、CINNARIZINE、COLCHICINE、DIPHENHYDRAMINE、HYDROCHLORIDE、QUINAPRIL、HYDROCHLORIDE、CLOFIBRATE、TRIFLUPROMAZINE、HYDROCHLORIDE、GEMFIBROZIL、CLOTRIMAZOLE、VINORELBINE TARTRATE、METHIMAZOLE
ALB	PYROGALLOL、IODIPAMIDE、DIAZEPAM、DICLOFENAC、OLMESARTAN MEDOXOMIL、RALTITREXED、NAPROXEN、WARFARIN、FLUCONAZOLE、GADOFOSVESET
ATF3	PROGESTERONE
HIF1A	PHENOXYBENZAMINE HYDROCHLORIDE、NIFEDIPINE、PIRETANIDE、EPINEPHRINE、EPINEPHRINE BITARTRATE、AXITINIB、TRIAMTERENE、DEQUALINIUM、BENZBROMARONE、VINCRISTINE SULFATE、INAMRINONE、NITROGLYCERIN、AMCINONIDE、TRETINOIN、DESOXIMETASONE、CYCLOSERINE、OXYTETRACYCLINE、DICLOFENAC SODIUM、NICLOSAMIDE、ISOETHARINE MESYLATE、LORATADINE、HYDROQUINONE、OXATOMIDE、ISOPROTERENOL、MEFENAMIC ACID、DOPAMINE、TOLFENAMIC ACID、SULFASALAZINE、EPOETIN ALFA、ETHAMSYLATE、FLUFENAMIC ACID、NOREPINEPHRINE BITARTRATE、CLOTRIMAZOLE、SORAFENIB、PROMAZINE、LEVONORDEFRIN、PIMOZIDE、DEFEROXAMINE、NOSCAPINE、HYDROCORTISONE、TOPOTECAN HYDROCHLORIDE
DUSP1	HYDROXYUREA、ALBUTEROL、VASOPRESSIN

No competing interests reported.

Download PDF

Journal Publication

published 17 Feb, 2024

Read the published version in BMC Nephrology →

Editorial decision: Revision requested
07 Nov, 2023
Reviews received at journal
20 Oct, 2023
Reviewers agreed at journal
17 Oct, 2023
Reviewers agreed at journal
17 Oct, 2023
Reviewers invited by journal
17 Oct, 2023
Editor assigned by journal
17 Oct, 2023
Editor invited by journal
10 Aug, 2023
Submission checks completed at journal
10 Aug, 2023
First submitted to journal
06 Jul, 2023

You are reading this latest preprint version

Ferroptosis-related genes, a novel therapeutic target for focal segmental glomerulosclerosis

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Data acquisition

Screening for differential genes associated with ferroptosis

GO functional enrichment analysis and KEGG pathway enrichment analysis of differential genes

Construction of PPI network and module analysis

Construction of an mRNA-miRNA regulatory network

Screening of circRNAs corresponding to miRNAs

Validation of ferroptosis hub genes

Prediction of potential therapeutic drugs

Results

Discussion

Declarations

References

Tables

Additional Declarations

Status:

Journal Publication

Version 1