Related Experiment Video

Updated: Feb 14, 2026

Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane
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Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane

Published on: February 8, 2020

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Reconstruction of kidney renal clear cell carcinoma evolution across pathological stages

Shichao Pang1, Yidi Sun2,3,4, Leilei Wu5

  • 1Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.

Scientific Reports
|February 22, 2018
PubMed

Insights

Understanding kidney renal clear cell carcinoma (KIRC) evolution is key. Our study reveals how somatic mutations drive KIRC progression, identifying evolutionary paths linked to prognosis and potential biomarkers.

Area of Science:

  • Oncology
  • Genetics
  • Bioinformatics

Background:

  • Kidney renal clear cell carcinoma (KIRC) pathogenesis remains incompletely understood.
  • The evolutionary dynamics and clinical implications of somatic mutations in KIRC require further investigation.

Purpose of the Study:

  • To reconstruct the evolutionary process of KIRC using single nucleotide variants (SNVs).
  • To identify early driver genes and stage-specific mutational expansion patterns.
  • To explore the prognostic value of evolutionary paths among mutated genes in KIRC.

Main Methods:

  • Reconstruction of KIRC evolutionary process utilizing cross-sectional SNVs data from various pathological stages.
  • Analysis of mutation frequency and evolutionary relationships among driver genes.

Main Results:

  • KIRC driver genes are identified early in the evolutionary trajectory.
  • Genes with moderate mutation frequencies exhibit stage-by-stage expansion.
  • Specific evolutionary paths (e.g., VHL-PBRM1, FMN2-PCLO) correlate with stage-specific prognosis, independent of individual gene mutation impact on survival.

Conclusions:

  • The evolutionary relationship among mutated genes, beyond mutation frequency, is crucial for identifying novel KIRC drivers and biomarkers.
  • Understanding KIRC evolutionary paths offers potential for improved clinical utility and targeted therapies.

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