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A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA
BACKGROUND: Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software f...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165618/ https://www.ncbi.nlm.nih.gov/pubmed/35669725 http://dx.doi.org/10.1155/2022/2449449 |
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author | Li, Hanwen Chen, Shaohua Mi, Hua |
author_facet | Li, Hanwen Chen, Shaohua Mi, Hua |
author_sort | Li, Hanwen |
collection | PubMed |
description | BACKGROUND: Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. METHOD: BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. RESULTS: In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. CONCLUSIONS: In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified. |
format | Online Article Text |
id | pubmed-9165618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91656182022-06-05 A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA Li, Hanwen Chen, Shaohua Mi, Hua Biomed Res Int Research Article BACKGROUND: Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. METHOD: BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. RESULTS: In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. CONCLUSIONS: In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified. Hindawi 2022-05-25 /pmc/articles/PMC9165618/ /pubmed/35669725 http://dx.doi.org/10.1155/2022/2449449 Text en Copyright © 2022 Hanwen Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Li, Hanwen Chen, Shaohua Mi, Hua A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title | A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title_full | A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title_fullStr | A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title_full_unstemmed | A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title_short | A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA |
title_sort | multiomics profiling based on online database revealed prognostic biomarkers of blca |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165618/ https://www.ncbi.nlm.nih.gov/pubmed/35669725 http://dx.doi.org/10.1155/2022/2449449 |
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