Amir Roointan

The treatment and management of kidney diseases present a significant global challenge, affecting over 800 million individuals and necessitating innovative therapeutic strategies that transcend symptomatic relief. The application of nanotechnology to therapies for kidney diseases, while still in its early stages, holds transformative potential for improving treatment outcomes. Recent advancements in nanoparticle-based drug delivery leverage the unique physicochemical properties of nanoparticles for targeted and controlled therapeutic delivery to the kidneys. Current research is focused on understanding the functional and phenotypic changes in kidney cells during both acute and chronic conditions, allowing for the identification of optimal target cells. In addition, the development of tailored nanomedicines enhances their retention and binding to key renal membranes and cell populations, ultimately improving localization, tolerability, and efficacy. However, significant barriers remain, including inconsistent nanoparticle synthesis and the complexity of kidney-specific targeting. To overcome these challenges, the field requires advanced synthesis techniques, refined targeting strategies, and the establishment of animal models that accurately reflect human kidney diseases. These efforts are critical for the clinical application of nanotherapeutics, which promise novel solutions for kidney disease management. This review evaluates a substantial body of in vivo research, highlighting the prospects, challenges, and opportunities presented by nanotechnology-mediated therapies and their potential to transform kidney disease treatment.

Pancreatic ductal adenocarcinoma (PDAC) is recognized as a highly aggressive fatal disease accounting for more than 90% of all pancreatic malignancies. Considering the limited effective treatment options and its low survival rate, studying PDAC's underlying mechanisms is of utmost importance. The present study focused on investigating PDAC expression data using WGCNA and Trader algorithms to shed light on the underlying mechanisms and identify the most reliable therapeutic candidates in PDAC. After analyzing a recently generated PDAC dataset (GSE132956), the obtained differentially expressed genes (DEGs) were subjected to different exploration steps. WGCNA was applied to cluster the co-expressed DEGs and found the disease's most correlated module and genes. The trader algorithm was utilized to analyze the constructed network of DEGs in STRING and identified the top 30 DEGs whose removal causes a maximum number of separate sub-networks. Hub genes were selected considering the lists of top identified nodes by the two algorithms. "Signaling by Rho GTPases," "Signaling by receptor tyrosine kinases," and "immune system" were top enriched gene ontology terms for the DEGs in the PDAC most correlated module and nine hub genes, including FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1 were identified by considering the top spotted DEGs by two algorithms. The findings provided evidence about the involvement of some pathways in the pathogenesis of PDAC and suggested several hub genes as therapeutic candidates via a comprehensive approach analyzing both the co-expression and PPI networks of DEGs in this cancer.

Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer. Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Background: Renal cell carcinoma (RCC) is recognized as one of the main causes of illness and death worldwide. Understanding the molecular mechanisms in RCC pathogenesis is of utmost importance for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the RCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.Methods: RCC microarray datasets were downloaded from the Gene Expression Omnibus database and after quality checking, normalization, and analysis using the limma algorithm, differentially expressed genes (DEGs) were identified considering the adjusted p-value < 0.049. The intensity values of the identified DEGs were introduced to the WGCNA algorithm for the construction of co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease correlated module and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA and GSCA server was used to make a comparison between the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub genes related drugs.Results: Three datasets including GSE11151, GSE12606, and GSE36897 were retrieved, merged, normalized, and analyzed. Using WGCNA the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell-surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were up-regulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules were identified to interact with the hub genes.Conclusions: By utilizing an integrative bioinformatics approach, this experiment shed a light on the underlying signaling pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer.

Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease’s most correlated module were mainly enriched in the immune system, cell–cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

Background: Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression. Methods: The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed. Results: After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes. Conclusions: The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Background: IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. Methods: Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. Result: “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. Conclusion: Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

Background: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though, still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using weighted gene co-expression network analysis (WGCNA) algorithm. Results: GSE104948 dataset was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potential key players in the IgAN progression. In the constructed regulatory network, miR-129-2, miR-34a and miR-27a, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.Conclusions: The excavated hub genes from the hearts of co-expressed modules and PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

Background: IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease is remained to be more explored and still there is an urgent need for discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied on mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN.Methods: Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database) and long-non coding RNAs (from LncTarD database) were used for construction of a multilayer regulatory network via Cytoscape.Result: “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN.Conclusion: Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.