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pmid:29805586

Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma.

Zengzeng Wang · Zhihong Zhang · Changwen Zhang · Yong Xu

Oncology letters · 2018 doi:10.3892/ol.2018.8398

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:29805586
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science-record
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sha256:a71ac2633e911363fd946609046f75cd57ed27c33fe46526a5a95abf3ee71f06
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T15:40:34Z
updated_at
2026-11-01T21:59:51Z
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paper object · 10 fields
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complete
title
Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma.
doi
10.3892/ol.2018.8398
pmid
29805586
authors array · 4 items
[0]
Zengzeng Wang
[1]
Zhihong Zhang
[2]
Changwen Zhang
[3]
Yong Xu
journal
Oncology letters
year
2018
abstract
The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.
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T test was used to compare the two groups and P<0.05 was considered to indicate a statistically significant difference.
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T test was used to compare the two groups and P<0.05 was considered to indicate a statistically significant difference.
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All data generated or analyzed during the present study are included in this published article.
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sections[20] "Availability of data and materials" paragraph 2
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All data generated or analyzed during the present study are included in this published article.
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Using GEO-sourced mRNA, miRNA, and methylation profiles, limma and methylation-difference tests identified 2,172 DEGs, 202 DEMs, and 2,172 DMSs, supporting HAPLN1, hsa-miR-204, and hsa-miR-218 as potential pathogenic biomarkers of ccRCC.
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The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441.
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The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues.
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sections[0] "Abstract" paragraph 1
quote
A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples.
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sections[0] "Abstract" paragraph 1
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The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.
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Using GEO-sourced mRNA, miRNA, and methylation profiles, limma and methylation-difference tests identified 2,172 DEGs, 202 DEMs, and 2,172 DMSs, supporting HAPLN1, hsa-miR-204, and hsa-miR-218 as potential pathogenic biomarkers of ccRCC.
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title
Epigenome analysis of clear cell renal cell carcinoma (ccRCC) tissue versus matched normal kidney tissue
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2026-11-01T15:39:55Z
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miR-200c Targets CDK2 and Suppresses Tumorigenesis in Renal Cell Carcinoma
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19615
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Long Noncoding RNA and mRNA Expression Profile in clear cell renal cell carcinoma
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2026-11-01T15:39:55Z
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Complete file
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The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.", "claims": [{"id": "claim-method", "statement": "T test was used to compare the two groups and P<0.05 was considered to indicate a statistically significant difference.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29805586", "locator": "sections[10] \"Statistical analysis\" paragraph 2", "quote": "T test was used to compare the two groups and P<0.05 was considered to indicate a statistically significant difference."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during the present study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29805586", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during the present study are included in this published article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GEO-sourced mRNA, miRNA, and methylation profiles, limma and methylation-difference tests identified 2,172 DEGs, 202 DEMs, and 2,172 DMSs, supporting HAPLN1, hsa-miR-204, and hsa-miR-218 as potential pathogenic biomarkers of ccRCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29805586", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441."}, {"asset": "source:pmid:29805586", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues."}, {"asset": "source:pmid:29805586", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples."}, {"asset": "source:pmid:29805586", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using GEO-sourced mRNA, miRNA, and methylation profiles, limma and methylation-difference tests identified 2,172 DEGs, 202 DEMs, and 2,172 DMSs, supporting HAPLN1, hsa-miR-204, and hsa-miR-218 as potential pathogenic biomarkers of ccRCC.", "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:29805586"], "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:GSE61441"], "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:GSE61441"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29805586", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3892/ol.2018.8398", "sha256": 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