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A Method to Identify Potential Prognostic Markers Across Distinct Tumor Types.

Boxi Zhang1, Elena Kochetkova1,2, Erik Norberg3

  • 1Department of Physiology and Pharmacology, Biomedicum, Karolinska Institutet, Stockholm, Sweden.

Methods in Molecular Biology (Clifton, N.J.)
|January 1, 2022
PubMed
Summary

Researchers can discover new cancer biomarkers using gene expression and survival data. This method, applied to The Cancer Genome Atlas (TCGA), aids in identifying prognostic markers across various tumor types.

Keywords:
AutophagyCancerKaplan–MeierLung adenocarcinomaLung cancerNext-generation sequencingRNA-seqSurvival analysisTCGAThe cancer genome atlas

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Area of Science:

  • Oncology
  • Genomics
  • Biostatistics

Background:

  • Identifying novel cancer biomarkers is crucial for patient prognosis.
  • Gene expression and survival analyses are key components in biomarker discovery.
  • The Kaplan-Meier survival analysis is a standard method for assessing survival fractions.

Purpose of the Study:

  • To present a method for identifying potential prognostic markers in cancer patients.
  • To guide researchers in utilizing large-scale cancer genomics datasets for biomarker discovery.
  • To provide practical instructions for prognostic marker identification.

Main Methods:

  • Utilizing The Cancer Genome Atlas (TCGA) dataset, encompassing gene expression and clinical follow-up data for 33 tumor types.
  • Applying survival analysis techniques, including Kaplan-Meier analysis.
  • Adapting the method for any open-source dataset with RNA expression and clinical outcome data.

Main Results:

  • The study outlines a reproducible method for prognostic marker identification.
  • The Cancer Genome Atlas (TCGA) serves as a valuable resource for this type of analysis.
  • The described methodology is applicable across diverse cancer types.

Conclusions:

  • The proposed method enables the identification of novel prognostic markers in cancer.
  • Researchers can leverage public datasets like TCGA for robust biomarker discovery.
  • This approach facilitates advancements in personalized cancer medicine through improved prognostic tools.