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pmid:39624823

Identification of key genes and immune infiltration of diabetic peripheral neuropathy in mice and humans based on bioinformatics analysis.

Yumin Zhang · Hui Zhou · Juan Liu · Nan Zhou

Frontiers in endocrinology · 2024 doi:10.3389/fendo.2024.1437979

Paper

complete

Experiment

complete

Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:39624823
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2026-11-01T20:29:53Z
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2026-11-01T22:55:43Z
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title
Identification of key genes and immune infiltration of diabetic peripheral neuropathy in mice and humans based on bioinformatics analysis.
doi
10.3389/fendo.2024.1437979
pmid
39624823
authors array · 4 items
[0]
Yumin Zhang
[1]
Hui Zhou
[2]
Juan Liu
[3]
Nan Zhou
journal
Frontiers in endocrinology
year
2024
abstract
Background: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, while the underlying molecular mechanisms are still unclear. The aim of this study was to screen the key genes and the roles of immune infiltration in DPN using bioinformatics analysis. Methods: DPN mice datasets including GSE222778, GSE11343, GSE70852, GSE27382, and GSE34889 were retrieved from the GEO database. Data of human DPN were retrieved from the dbGaP. The differentially expressed genes (DEGs) were selected and further analyzed by using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Gene Set Enrichment Analysis (GSEA) to find the shared key pathway. Protein-protein interaction networks were built in shared mouse and human DEGs. The hub genes were selected and verified in vitro using high- glucose-treated PC12 cells and Schwann cells. The single-sample GSEA (ssGSEA) algorithm was used to analyze the proportions of infiltrating immune cells in human DPN and the subsequent correlations with hub genes. Results: A total of 323 mouse DEGs and 501 human DEGs were selected, and they were found significantly enriched in immune-related biological functions and pathways. A total of 13 DEGs were found shared in mice and human DPN datasets, and among them, there were 7 hub genes, namely, PLAUR, S100A8, IL7R, CXCL13, SRPX2, CD300LB, and CFI. The expression of Cfi, S100a8, Cxcl13, and Cd300lb was consistently confirmed in vitro. The scores of neutrophils and NK CD56bright cells varied most significantly by immune cell infiltration analysis (p < 0.01). Furthermore, the selected hub genes were found to be highly correlated with the immune infiltration. Conclusion: Our study indicated the importance of immune dysregulations in DPN and identified several hub genes through combined analysis in mice and human DPN samples, thus providing potential diagnostic and therapeutic targets in the future.
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The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives.
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sections[4] "Identification of differentially expressed genes" paragraph 2
quote
The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives.
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
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A total of 323 mouse DEGs and 501 human DEGs were selected, and they were found significantly enriched in immune-related biological functions and pathways.
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A total of 323 mouse DEGs and 501 human DEGs were selected, and they were found significantly enriched in immune-related biological functions and pathways.
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A total of 323 mouse DEGs and 501 human DEGs were selected, and they were found significantly enriched in immune-related biological functions and pathways.
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filtration of diabetic peripheral neuropathy in mice and humans based on bioinformatics analysis.
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The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39624823", "kind": "science-record", "revision": 4, "content_hash": "sha256:73ff1ff703b5e09e9282ecca6e46ce2c07c4bd72425ca7658f441d550ce88ccd", "created_at": "2026-11-01T20:29:53Z", "updated_at": "2026-11-01T22:55:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes and immune infiltration of diabetic peripheral neuropathy in mice and humans based on bioinformatics analysis.", "doi": "10.3389/fendo.2024.1437979", "pmid": "39624823", "authors": ["Yumin Zhang", "Hui Zhou", "Juan Liu", "Nan Zhou"], "journal": "Frontiers in endocrinology", "year": 2024, "abstract": "Background: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, while the underlying molecular mechanisms are still unclear. The aim of this study was to screen the key genes and the roles of immune infiltration in DPN using bioinformatics analysis.\nMethods: DPN mice datasets including GSE222778, GSE11343, GSE70852, GSE27382, and GSE34889 were retrieved from the GEO database. Data of human DPN were retrieved from the dbGaP. The differentially expressed genes (DEGs) were selected and further analyzed by using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Gene Set Enrichment Analysis (GSEA) to find the shared key pathway. Protein-protein interaction networks were built in shared mouse and human DEGs. The hub genes were selected and verified in vitro using high- glucose-treated PC12 cells and Schwann cells. The single-sample GSEA (ssGSEA) algorithm was used to analyze the proportions of infiltrating immune cells in human DPN and the subsequent correlations with hub genes.\nResults: A total of 323 mouse DEGs and 501 human DEGs were selected, and they were found significantly enriched in immune-related biological functions and pathways. A total of 13 DEGs were found shared in mice and human DPN datasets, and among them, there were 7 hub genes, namely, PLAUR, S100A8, IL7R, CXCL13, SRPX2, CD300LB, and CFI. The expression of Cfi, S100a8, Cxcl13, and Cd300lb was consistently confirmed in vitro. The scores of neutrophils and NK CD56bright cells varied most significantly by immune cell infiltration analysis (p < 0.01). Furthermore, the selected hub genes were found to be highly correlated with the immune infiltration.\nConclusion: Our study indicated the importance of immune dysregulations in DPN and identified several hub genes through combined analysis in mice and human DPN samples, thus providing potential diagnostic and therapeutic targets in the future.", "claims": [{"id": "claim-method", "statement": "The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39624823", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE11343"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"fdr_threshold": "the Benjamini–Hochberg false discovery rate method applied to ident"}, "inputs": ["data:geo:GSE11343"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39624823", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2024.1437979", "sha256": "sha256:093a5cc54989a8ff7291d359af9b75bcadda26739ffccb2e5126ac458fa85837", "bytes": 57150, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2024.1437979", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11608978", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE11343", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11343", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE11343", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives."}}, {"id": "method-2", "family": "group_comparison", "specific": "welch_t_test", "label": "Group comparison", "dsl_operations": ["welch_t"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Welch"}}, {"id": "method-3", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Principal component analysis"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Protein-protein interaction"}}, {"id": "method-6", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE222778", "GSE11343", "GSE70852", "GSE27382", "and GSE34889"], "resolved_accessions": ["GSE222778", "GSE11343", "GSE70852", "GSE27382"], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE222778, GSE11343, GSE70852, GSE27382 in GEO database and the accession resolves in the repository. 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A total of 13 DEGs were found shared in mice and human DPN datasets, and among them, ther", "several hub genes through combined analysis in mice and human DPN samples, thus providing potent", "in interaction networks were built in shared mouse and human DEGs. The hub genes were selected"], "order_basis": "paper_text_order:41", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["erified in vitro using high- glucose-treated PC12 cells and Schwann cells. The single-sample G"], "order_basis": "paper_text_order:945", "id": "assay:2"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The GEOquery package in R (21) was used to download the original data of GSE11343 and GSE222778. The limma package was subsequently used to normalize the data and find the DEGs. DEGs for GSE70852, GSE27382, and GSE34889 datasets were analyzed through the GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool that also used the limma package for identifying DEGs. The Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes that were statistically significant and limited false positives."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}