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Updated: Dec 3, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through
Banlai Ruan1,2,3, Xianzhen Feng4, Xueyi Chen1,2
1Medical Research Center, Xi'an No. 3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, 710016 Shaanxi Province, China.
Background:
With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).
Methods:
Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.
Results:
A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC.
Conclusion:
The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.
Insights
This study identified four key genes (CDKL2, LRFN1, STAT2, SOWAHB) that can predict patient survival in kidney renal clear cell carcinoma (KIRC). A gene expression model using these genes offers a promising tool for KIRC prognosis.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Kidney renal clear cell carcinoma (KIRC) presents significant challenges due to high recurrence and metastasis rates.
- Despite extensive research, effective prognostic markers for KIRC remain critical for improving patient outcomes.
Purpose of the Study:
- To identify novel gene expression signatures for predicting prognosis in kidney renal clear cell carcinoma (KIRC).
- To develop a reliable gene-based risk model for assessing patient survival in KIRC.
Main Methods:
- Utilized The Cancer Genome Atlas (TCGA) data for comprehensive analysis.
- Employed differential expression analysis, Cox regression, WGCNA, LASSO, and survival analysis.
- Validated prognostic accuracy using ROC curve analysis and examined clinicopathological correlations.
Main Results:
- Identified 5,777 differentially expressed genes, with 1,853 showing statistical significance.
- A four-gene signature (CDKL2, LRFN1, STAT2, SOWAHB) emerged as a strong predictor of KIRC patient survival.
- High-risk scores based on these four genes correlated with unfavorable prognoses and KIRC progression.
Conclusions:
- A gene expression model comprising CDKL2, LRFN1, STAT2, and SOWAHB shows promise for KIRC prognosis.
- These findings may offer valuable insights for KIRC diagnosis and therapeutic strategies.
