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pmid:37496082

Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis.

Hanchao Zhang · Yue Yang · Zhengdao Liu · Hong Xu · Han Zhu · Peirui Wang · Guobiao Liang

Hereditas · 2023 doi:10.1186/s41065-023-00295-8

Paper

complete

Experiment

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Data

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:37496082
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title
Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis.
doi
10.1186/s41065-023-00295-8
pmid
37496082
authors array · 7 items
[0]
Hanchao Zhang
[1]
Yue Yang
[2]
Zhengdao Liu
[3]
Hong Xu
[4]
Han Zhu
[5]
Peirui Wang
[6]
Guobiao Liang
journal
Hereditas
year
2023
abstract
BACKGROUND: RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m1A/m6A/m5C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m1A/m6A/m5C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood. METHODS: Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs. RESULTS: We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A. CONCLUSIONS: Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.
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Utilizing the R package “Consensus Cluster Plus”, we conducted unsupervised clustering analysis to identify the distinct patterns of MRGs based on their expression profiles.
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Utilizing the R package “Consensus Cluster Plus”, we conducted unsupervised clustering analysis to identify the distinct patterns of MRGs based on their expression profiles.
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The raw data used and analyzed during the current study are available from the corresponding author on reasonable request.
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Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.
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Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.
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Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.
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Fibrosis with Inflammation at One Year Predicts Transplant Functional Decline
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title
Gene Expression in Biopsies of Acute Rejection and Interstitial Fibrosis/Tubular Atrophy Reveals Highly Shared Mechanisms that Correlate with Worse Long-term Outcomes
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37496082", "kind": "science-record", "revision": 3, "content_hash": "sha256:278e910ba4246d3d9995d2e8ecd661bc4cd6d116efbb0d3b291bac341fd25f24", "created_at": "2026-11-01T19:02:16Z", "updated_at": "2026-11-01T22:09:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis.", "doi": "10.1186/s41065-023-00295-8", "pmid": "37496082", "authors": ["Hanchao Zhang", "Yue Yang", "Zhengdao Liu", "Hong Xu", "Han Zhu", "Peirui Wang", "Guobiao Liang"], "journal": "Hereditas", "year": 2023, "abstract": "BACKGROUND: RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m1A/m6A/m5C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m1A/m6A/m5C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood.\nMETHODS: Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs.\nRESULTS: We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A.\nCONCLUSIONS: Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.", "claims": [{"id": "claim-method", "statement": "Utilizing the R package “Consensus Cluster Plus”, we conducted unsupervised clustering analysis to identify the distinct patterns of MRGs based on their expression profiles.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37496082", "locator": "sections[6] \"Identification of molecular subtype\" paragraph 2", "quote": "Utilizing the R package “Consensus Cluster Plus”, we conducted unsupervised clustering analysis to identify the distinct patterns of MRGs based on their expression profiles."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data used and analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37496082", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The raw data used and analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37496082", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.", "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:37496082"], "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:GSE22459"], "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": {}, "inputs": ["data:geo:GSE22459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37496082", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s41065-023-00295-8", "sha256": 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