An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

“…In the clinical evaluation phase, randomized clinical trials are conducted to test whether these biomarkers have diagnostic, prognostic, or predictive utility. Once the biomarker’s utility is demonstrated, it proceeds to the clinical use phase where it is commercialized with its respective protocol and complies with the corresponding regulatory processes [ 49 , 94 , 95 , 96 ].…”

“…Within the information provided by the GEO database, it includes the type of data, number of samples, biological type, cell line type, and whether or not they received treatment. Prior to data analysis, the data must be preprocessed by removing duplicate data and normalizing using packages such as LIMMA in the R software [ 66 , 67 , 95 , 97 ]. In the discovery of biomarkers, not only the analysis of differential gene expression has been important but also the use of clustering algorithms such as K-means, co-expression analysis, and investigation of central genes using tools such as Cytoscape ( accessed on 20 March 2023) or STRING ( accessed on 20 March 2023), gene ontology analysis (DAVID: accessed on 20 March 2023), KEGG ( accessed on 20 March 2023), Gene Ontology (GO: accessed on 20 March 2023), and detection of associated metabolic pathways and co-expression/abundance of genes.…”

CD44 in Bladder Cancer

“…Such a finding points to the possibility of frequent, non-invasive screening of patients with manageable, non-muscle-invasive bladder cancer to monitor for progression of the disease to a muscle-invasive state, which would require a more aggressive treatment program. Progression-free survival of bladder cancer patients was also shown to correlate with expression levels of CD44 in patient urine [ 79 ]. In fact, CD44 was found to be one of the main differentially expressed genes between bladder cancer patients and control urine samples.…”

Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy

“…The Boruta algorithm is a supervised classification method utilized for feature selection, aiming to ascertain features associated with a given classification task [35]. These methods are widely used to identify biomarkers with superior accuracy and good interpretability [27][28][29]. They were employed to obtain feature genes of stroke.…”

“…These techniques provide potent instruments for analyzing complex biological data and extracting valuable insights. Among the various machine learning algorithms, LASSO (Least Absolute Shrinkage and Selection Operator), SVM-RFE (Support Vector Machine Recursive Feature Elimination), and Boruta have garnered considerable acclaim for their effectiveness in feature selection and classification tasks in molecular diagnostics [27][28][29]. However, the majority of extant studies predominantly concentrate on the singular exploration of machine learning or molecular docking, with a notable dearth of attention devoted to the concurrent application of these methodologies in the context of ischemic stroke diagnosis and therapy.…”