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Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways

BACKGROUND: The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a si...

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Autores principales: Inal Gültekin, Güldal, Timirci Kahraman, Özlem, Işbilen, Murat, Durmuş, Saliha, Çakir, Tunahan, Yaylim, İlhan, Isbir, Turgay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760318/
https://www.ncbi.nlm.nih.gov/pubmed/36529823
http://dx.doi.org/10.1186/s43046-022-00153-0
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author Inal Gültekin, Güldal
Timirci Kahraman, Özlem
Işbilen, Murat
Durmuş, Saliha
Çakir, Tunahan
Yaylim, İlhan
Isbir, Turgay
author_facet Inal Gültekin, Güldal
Timirci Kahraman, Özlem
Işbilen, Murat
Durmuş, Saliha
Çakir, Tunahan
Yaylim, İlhan
Isbir, Turgay
author_sort Inal Gültekin, Güldal
collection PubMed
description BACKGROUND: The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders. METHODS: The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples. RESULTS: Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples. CONCLUSION: This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43046-022-00153-0.
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spelling pubmed-97603182022-12-19 Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways Inal Gültekin, Güldal Timirci Kahraman, Özlem Işbilen, Murat Durmuş, Saliha Çakir, Tunahan Yaylim, İlhan Isbir, Turgay J Egypt Natl Canc Inst Research BACKGROUND: The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders. METHODS: The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples. RESULTS: Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples. CONCLUSION: This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43046-022-00153-0. Springer Berlin Heidelberg 2022-12-19 2022 /pmc/articles/PMC9760318/ /pubmed/36529823 http://dx.doi.org/10.1186/s43046-022-00153-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Inal Gültekin, Güldal
Timirci Kahraman, Özlem
Işbilen, Murat
Durmuş, Saliha
Çakir, Tunahan
Yaylim, İlhan
Isbir, Turgay
Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title_full Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title_fullStr Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title_full_unstemmed Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title_short Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
title_sort six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760318/
https://www.ncbi.nlm.nih.gov/pubmed/36529823
http://dx.doi.org/10.1186/s43046-022-00153-0
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