Cancers (Mar 2023)

Critical Evaluation of a microRNA-Based Risk Classifier Predicting Cancer-Specific Survival in Renal Cell Carcinoma with Tumor Thrombus of the Inferior Vena Cava

  • Mischa J. Kotlyar,
  • Markus Krebs,
  • Antonio Giovanni Solimando,
  • André Marquardt,
  • Maximilian Burger,
  • Hubert Kübler,
  • Ralf Bargou,
  • Susanne Kneitz,
  • Wolfgang Otto,
  • Johannes Breyer,
  • Daniel C. Vergho,
  • Burkhard Kneitz,
  • Charis Kalogirou

DOI
https://doi.org/10.3390/cancers15071981
Journal volume & issue
Vol. 15, no. 7
p. 1981

Abstract

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(1) Background: Clear cell renal cell carcinoma extending into the inferior vena cava (ccRCCIVC) represents a clinical high-risk setting. However, there is substantial heterogeneity within this patient subgroup regarding survival outcomes. Previously, members of our group developed a microRNA(miR)-based risk classifier—containing miR-21-5p, miR-126-3p and miR-221-3p expression—which significantly predicted the cancer-specific survival (CSS) of ccRCCIVC patients. (2) Methods: Examining a single-center cohort of tumor tissue from n = 56 patients with ccRCCIVC, we measured the expression levels of miR-21, miR-126, and miR-221 using qRT-PCR. The prognostic impact of clinicopathological parameters and miR expression were investigated via single-variable and multivariable Cox regression. Referring to the previously established risk classifier, we performed Kaplan–Meier analyses for single miR expression levels and the combined risk classifier. Cut-off values and weights within the risk classifier were taken from the previous study. (3) Results: miR-21 and miR-126 expression were significantly associated with lymphonodal status at the time of surgery, the development of metastasis during follow-up, and cancer-related death. In Kaplan–Meier analyses, miR-21 and miR-126 significantly impacted CSS in our cohort. Moreover, applying the miR-based risk classifier significantly stratified ccRCCIVC according to CSS. (4) Conclusions: In our retrospective analysis, we successfully validated the miR-based risk classifier within an independent ccRCCIVC cohort.

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