ArticleBioMed research international2020
Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 16 citations in OpenAlex.
- Integrative Meta-Analysis and WGCNA Reveal Candidate Diagnostic Hub Genes in Clear Cell Carcinoma.International journal of cell biology · 2026Article
- Integrating machine learning, deep learning, and docking to predict aristolochic acid A carcinogenesis.Frontiers in pharmacology · 2026Article
- The Role of MCM7 and Its Hosted miR-106b-25 Cluster in Renal Cancer Progression.International journal of molecular sciences · 2025Article
- Prognostic Significance of Overexpression of BCL9 and TPX2 in High-Grade Clear Cell Renal Cell Carcinoma: Prognostic Markers for Metastasis and Survival.International journal of molecular sciences · 2025Article
- Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma.Journal of clinical medicine · 2025Review
- A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma.Briefings in bioinformatics · 2024Article
- Important role and underlying mechanism of non‑SMC condensin I complex subunit G in tumours (Review).Oncology reports · 2024Review
- Nonstructural maintenance of chromatin condensin I complex subunit G promotes the progression of glioblastoma by facilitating Poly (ADP-ribose) polymerase 1-mediated E2F1 transactivation.Neuro-oncology · 2023Article
- Increased expression of NCAPG (Non-SMC condensing I complex subunit G) is associated with progression and poor prognosis of lung adenocarcinoma.Bioengineered · 2022Article
- Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry.Frontiers in molecular biosciences · 2022Article
- A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma.BioMed research international · 2021Article
- Translational changes induced by acute sleep deprivation uncovered by TRAP-Seq.Molecular brain · 2020Article
- Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.Therapeutic advances in urologyReview
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments.
methodsThe public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes.
resultsA total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (
conclusionAfter the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.
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