Transcriptome analysis revealed a novel nine-gene prognostic risk score of clear cell renal cell carcinoma
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View abstract on PubMed
Summary
This summary is machine-generated.Researchers developed a new nine-gene risk score to predict clear cell renal cell carcinoma (ccRCC) survival. This score identifies high-risk patients with poorer outcomes, aiding in personalized treatment strategies for ccRCC.
Area Of Science
- Oncology
- Genomics
- Bioinformatics
Background
- Clear cell renal cell carcinoma (ccRCC) presents a significant global health challenge due to increasing incidence and high mortality rates.
- Understanding molecular alterations in ccRCC is critical for developing prognostic biomarkers and effective therapies.
Purpose Of The Study
- To identify molecular alterations associated with ccRCC onset, persistence, and progression.
- To develop and validate a novel prognostic risk score for ccRCC patients.
Main Methods
- Differential gene expression analysis was performed on bulk RNA sequencing data from The Cancer Genome Atlas Program.
- A nine-gene prognostic risk score was constructed using univariate and multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis.
- The risk score's predictive power was validated using Kaplan-Meier curves, Cox regression, receiver operating characteristic (ROC) analysis, and an external cohort (E-MTAB-1980).
Main Results
- A novel nine-gene prognostic risk score (ZIC2, TNNT1, SAA1, OTX1, C20orf141, CDHR4, HOXB13, IGFL2, IGFN1) was successfully constructed and validated.
- Patients in the high-risk group exhibited significantly shortened overall survival and demonstrated independent predictive power (HR: 1.942, AUC: 0.719).
- The high-risk score correlated with advanced clinicopathological parameters and showed similar prognostic significance in the external validation cohort (HR: 5.121, AUC: 0.787).
Conclusions
- A validated nine-gene prognostic risk score offers a valuable tool for predicting ccRCC patient outcomes.
- The risk score is associated with tumor immune microenvironment, somatic mutation patterns, and key tumorigenesis pathways.
- Further experimental validation is recommended to fully elucidate the clinical utility of this prognostic model.
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