ArticleOncology letters2018
Functional analysis of gene expression profiling-based prediction in bladder cancer.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Sex-related differences in gene expression in early-stage bladder cancer revealed by whole-transcriptome sequencing.BMC cancer · 2026Article
- Novel tumor gene expression signatures improve the overall survival prediction efficiency over tumor mutation burden and PD-L1 expression in bladder carcinoma with checkpoint blockade immunotherapy.American journal of cancer research · 2024Article
- The use of RNA-based treatments in the field of cancer immunotherapy.Molecular cancer · 2023Review
- Review
- When Oxidative Stress Meets Epigenetics: Implications in Cancer Development.Antioxidants (Basel, Switzerland) · 2020Review
- Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor (
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