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Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Epitácio Farias1, Patrick Terrematte2, Beatriz Stransky1,3

  • 1Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte (UFRN), Natal 59078-400, Brazil.

International Journal of Molecular Sciences
|April 27, 2024
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Summary
This summary is machine-generated.

This study identifies an 11-gene signature for clear-cell renal-cell carcinoma (ccRCC) metastasis. This signature, including coding and non-coding genes, shows potential as biomarkers for ccRCC understanding and patient survival.

Keywords:
ceRNA networkmachine learningmetastasisrenal carcinomatranscriptional signature

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Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Clear-cell renal-cell carcinoma (ccRCC) is a significant pathology characterized by high metastatic rates.
  • While coding genes' roles in metastasis are known, non-coding genes, like competitive endogenous RNA (ceRNA), are increasingly studied.
  • Understanding the ceRNA network is crucial for identifying new biomarkers and therapeutic targets in ccRCC.

Purpose of the Study:

  • To construct a ceRNA network for ccRCC associated with metastatic development.
  • To develop and validate a gene signature for predicting ccRCC metastasis.
  • To analyze the biological functions and prognostic significance of the identified gene signature.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC-RECA) datasets.
  • Constructed a ceRNA network using differentially expressed genes.
  • Employed eight feature selection techniques to assemble and select a final 11-gene signature.
  • Performed genomic, risk, and functional annotation analyses.

Main Results:

  • An 11-gene signature (SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, RASD1) was identified.
  • The signature demonstrated good generalization with an Area Under the Curve (AUC) of 81.5% on an external dataset.
  • Key genes (hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, PTTG1) correlated significantly with patient survival and metastasis.
  • Functional annotation revealed involvement in RNA polymerase II transcription regulation and cell cycle control.

Conclusions:

  • The identified 11-gene signature, comprising coding and non-coding genes, shows promise as a biomarker for ccRCC.
  • The ceRNA network analysis suggests lncRNAs acting as sponges for miRNAs, contributing to ccRCC progression.
  • This signature aids in understanding ccRCC biology and could improve patient prognosis and management.