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      Identification of the pivotal role of SPP1 in kidney stone disease based on multiple bioinformatics analysis

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          Abstract

          Background

          Kidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets.

          Methods

          Based on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall’s plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein–protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis.

          Results

          A total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup.

          Conclusions

          There are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12920-022-01157-4.

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          Most cited references58

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                westshane@126.com
                qidongxia_md@163.com
                jason980620@163.com
                didudude@outlook.com
                sunjianxuan123@126.com
                tjxyang1993@163.com
                sgwangtjm@163.com
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                11 January 2022
                11 January 2022
                2022
                : 15
                : 7
                Affiliations
                GRID grid.33199.31, ISNI 0000 0004 0368 7223, Department of Urology, Tongji Hospital, Tongji Medical College, , Huazhong University of Science and Technology, ; Wuhan, China
                Article
                1157
                10.1186/s12920-022-01157-4
                8751247
                35016690
                f8358ac1-9ce3-46fd-aa13-b3a1a80694ea
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 12 November 2021
                : 6 January 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Genetics
                kidney stone disease,weighted gene co-expression network analysis,hub genes,spp1,macrophages

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