ArticleCancer reports (Hoboken, N.J.)2022
Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database.
Article in Cancer reports (Hoboken, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Sex-related differences in gene expression in early-stage bladder cancer revealed by whole-transcriptome sequencing.BMC cancer · 2026Article
- Identification of CSRP1 as novel biomarker for hormone-sensitive prostate cancer by the combination of clinical and functional research.Cancer cell international · 2025Article
- CSRP1 gene: a potential novel prognostic marker in acute myeloid leukemia with implications for immune response.Discover oncology · 2024Article
- Analyzing the mutational landscape of prostate cancer susceptibility genes through next-generation sequencing (NGS).American journal of translational research · 2024Article
- A novel feature selection algorithm for identifying hub genes in lung cancer.Scientific reports · 2023Article
- Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co‑expression and support vector machine algorithms.Medical microbiology and immunology · 2023Article
- Dysregulated circular RNAs are closely linked to multiple myeloma prognosis, with circ_0026652 predicting bortezomib‑based treatment response and survival via the microRNA‑608‑mediated Wnt/β‑catenin pathway.Oncology reports · 2022Article
- Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database.Cancer reports (Hoboken, N.J.) · 2022Article
- Evaluating the cytotoxicity and pathogenicity of multi-walled carbon nanotube through weighted gene co-expression network analysis: a nanotoxicogenomics study.BMC genomic data · 2022Article
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMuscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.
aimWe aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer. METHODS AND
resultsGene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up-regulated genes and 2961 down-regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up-regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co-expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan-Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC.
conclusionThe research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.