Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39290792", "kind": "science-record", "revision": 3, "content_hash": "sha256:a3399e5c6b8f8de0d9089edb08eeea7c8360b55db91843d3ec85031c34e1a5d4", "created_at": "2026-11-01T20:29:27Z", "updated_at": "2026-11-01T22:12:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.", "doi": "10.2147/DMSO.S472412", "pmid": "39290792", "authors": ["Yiming Guan", "Xiayan Yin", "Liyan Wang", "Zongli Diao", "Hongdong Huang", "Xueqi Wang"], "journal": "Diabetes, metabolic syndrome and obesity : targets and therapy", "year": 2024, "abstract": "Background: Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers.\nMethods: Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients.\nResults: Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A.\nConclusion: Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.", "claims": [{"id": "claim-method", "statement": "The diagnostic efficacy of the candidate genes in the training and validation datasets was evaluated using the “pROC” package (version 1.18.0).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39290792", "locator": "sections[9] \"Diagnostic Performance Evaluation and Validation of Candidate Genes\" paragraph 2", "quote": "The diagnostic efficacy of the candidate genes in the training and validation datasets was evaluated using the “pROC” package (version 1.18.0)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (GSE30122 and GSE104954) used and analyzed in the current study are available from GEO database (https://www.ncbi.nlm.nih.gov/gds).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39290792", "locator": "sections[38] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets (GSE30122 and GSE104954) used and analyzed in the current study are available from GEO database (https://www.ncbi.nlm.nih.gov/gds)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "FAM13B and FAM98A were identified as potential diagnostic biomarkers for diabetic nephropathy, with AUC values of 0.958 and 0.967 in the training dataset and 0.921 each in the validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39290792", "locator": "sections[26] \"Enhanced Diagnostic Efficacy of FAM13B and FAM98A for DN\" paragraph 2", "quote": "The area under the curve (AUC) values for FAM13B, FAM98A, HPS5, PLBD1, and TGFBR3 in the training dataset were 0.958, 0.967, 0.933, 0.983, and 0.958, respectively, while those in the validation set were 0.921, 0.921, 0.675, 0.675, and 0.976, respectively. Therefore, FAM13B, FAM98A, and TGFBR3 exhibited a good diagnostic efficacy for DN (AUC > 0.7) (Figure 5A and B). Differential expression analysis revealed that FAM13B, FAM98A, and TGFBR3 were differentially expressed. In particular, FAM13B and FAM98A were significantly upregulated in the validation and training datasets. Consequently, FAM13B and FAM98A were identified as potential biomarkers for DN (Figure 5C and D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "FAM13B and FAM98A were identified as potential diagnostic biomarkers for diabetic nephropathy, with AUC values of 0.958 and 0.967 in the training dataset and 0.921 each in the validation dataset.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39290792"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE104954"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE104954"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39290792", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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