Dataset Information

Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients.

ABSTRACT: In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings.

SUBMITTER: Piliszek R

PROVIDER: S-EPMC9266725 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients.

Piliszek Radosław R Brożyna Anna A AA Rudnicki Witold R WR

International journal of molecular sciences 20220624 13

In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such ...[more]

PMID: 35806060

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Project description:BackgroundHigh-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach.MethodsThe gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database.ResultsA total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes.ConclusionsOur study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

Dataset Information

Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients.

Publications

Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients.

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