ArticleOncology research2020
Correlating Transcriptional Networks to Papillary Renal Cell Carcinoma Survival: A Large-Scale Coexpression Analysis and Clinical Validation.
Article in Oncology research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study.Frontiers in public health · 2022Trial
- Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database.Cancer reports (Hoboken, N.J.) · 2022Article
- Pivotal models and biomarkers related to the prognosis of breast cancer based on the immune cell interaction network.Scientific reports · 2022Article
- Prognostic Values of E2F1/2 Transcriptional Expressions in Chromophobe Renal Cell Carcinoma Patients: Evidence from Bioinformatics Analysis.International journal of general medicine · 2021Article
- Review
- Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer.Cell transplantationArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to investigate the potential mechanisms of progression and identify novel prognosis-related biomarkers for papillary renal cell carcinoma (PRCC) patients. The related data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by weighted gene coexpression network analysis (WGCNA). The correlation between each module and the clinical traits were analyzed by Pearson's correlation analysis. Pathway analysis was conducted to reveal potential mechanisms. Hub genes within each module were screened by intramodule analysis, and visualized by Cytoscape software. Furthermore, important hub genes were validated in an external dataset and clinical samples. A total of 5,839 differentially expressed genes were identified. By using WGCNA, we identified 21 coregulatory gene clusters based on 289 PRCC samples. We found many modules were significantly associated with clinicopathological characteristics. The gray, pink, light yellow, and salmon modules served as prognosis indicators for PRCC patients. Pathway enrichment analyses found that the hub genes were significantly enriched in the cancer-related pathways. With the external Gene Expression Omnibus (GEO) validation dataset, we found that PCDH12, GPR4, and KIF18A in the pink and yellow modules were continually associated with the survival status of PRCC, and their expressions were positively correlated with pathological grade. Notably, we randomly chose PCDH12 for validation, and the results suggested that the PRCC patients with higher pathological grades (II + III) mostly had higher PCDH12 protein expression levels compared with those patients in grade I. These validated hub genes play critical roles in the prognosis prediction of PRCC and serve as potential biomarkers for future personalized treatment.
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