Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Journal of the Egyptian National Cancer Institute |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43046-022-00153-0 |
| _version_ | 1797977364622213120 |
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| author | Güldal Inal Gültekin Özlem Timirci Kahraman Murat Işbilen Saliha Durmuş Tunahan Çakir İlhan Yaylim Turgay Isbir |
| author_facet | Güldal Inal Gültekin Özlem Timirci Kahraman Murat Işbilen Saliha Durmuş Tunahan Çakir İlhan Yaylim Turgay Isbir |
| author_sort | Güldal Inal Gültekin |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T05:05:40Z |
| format | Article |
| id | doaj.art-cd6b7d896e9b46cabac13d750011bcf9 |
| institution | Directory Open Access Journal |
| issn | 2589-0409 |
| language | English |
| last_indexed | 2024-04-11T05:05:40Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of the Egyptian National Cancer Institute |
| spelling | doaj.art-cd6b7d896e9b46cabac13d750011bcf92022-12-25T12:25:41ZengSpringerOpenJournal of the Egyptian National Cancer Institute2589-04092022-12-013411810.1186/s43046-022-00153-0Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathwaysGüldal Inal Gültekin0Özlem Timirci Kahraman1Murat Işbilen2Saliha Durmuş3Tunahan Çakir4İlhan Yaylim5Turgay Isbir6Department of Physiology, Faculty of Medicine, Istanbul Okan UniversityDepartment of Molecular Medicine, Istanbul University, Aziz Sancar Experimental Research InstituteDepartment of Biostatistics and Bioinformatics, Acibadem Mehmet Ali Aydinlar UniversityDepartment of Bioengineering, Faculty of Engineering, Gebze Technical UniversityDepartment of Bioengineering, Faculty of Engineering, Gebze Technical UniversityDepartment of Molecular Medicine, Istanbul University, Aziz Sancar Experimental Research InstituteDepartment of Molecular Medicine, Faculty of Medicine, Yeditepe UniversityAbstract 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.https://doi.org/10.1186/s43046-022-00153-0Cell divisionDifferentially expressed genesIntegrated bioinformaticsLGALS3AURKBIntrinsic apoptosis |
| spellingShingle | Güldal Inal Gültekin Özlem Timirci Kahraman Murat Işbilen Saliha Durmuş Tunahan Çakir İlhan Yaylim Turgay Isbir Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways Journal of the Egyptian National Cancer Institute Cell division Differentially expressed genes Integrated bioinformatics LGALS3 AURKB Intrinsic apoptosis |
| 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 | Cell division Differentially expressed genes Integrated bioinformatics LGALS3 AURKB Intrinsic apoptosis |
| url | https://doi.org/10.1186/s43046-022-00153-0 |
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