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pmid:36568106
Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy.
Kunna Lu · Li Wang · Yan Fu · Guanghong Li · Xinhuan Zhang · Mingfeng Cao
Frontiers in endocrinology · 2022 doi:10.3389/fendo.2022.1048139
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Our study aimed to reveal the roles of immune-related genes during DN progression.\nMethods: The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration.\nResults: 92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells.\nConclusions: We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.", "claims": [{"id": "claim-method", "statement": "WGCNA was used to establish potential modules associated with different subgroups of DN samples (29).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568106", "locator": "sections[6] \"Weighted gene coexpression network analysis\" paragraph 2", "quote": "WGCNA was used to establish potential modules associated with different subgroups of DN samples (29)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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